Multi-channel Scheduling Algorithm (PG thesis)

COLLEGE OF ENGINEERING

Multi-Channel Deficit Round Robin Scheduling for Hybrid TDM/WDM Optical Networks

A dissertation submitted to Swansea University in partial fulfillment of the requirements for the degree of

Master of Research (MRes)

by

MITHILEYSH SATHIYANARAYANAN 611702

Course Title: MRes in Communication Systems

Supervisors:

Dr. Kyeong Soo Kim Prof. Thomas Chen

College of Engineering, Swansea University

October 2012

Copyright by

© Mithileysh Sathiyanarayanan 2012. All rights reserved.

AUTHOR'S DECLARATION I hereby declare that I am the sole author of this research dissertation entitled “Multi-Channel Deficit Round Robin Scheduling for Hybrid TCM/WDM Optical Networks” and the entire work embodied in this dissertation has been carried out by me and investigated except the as in cited references with the guidance of my Supervisor Dr. Kim, and no part of it has been submitted for any degree of any institutions previously. The dissertation was carried out in accordance with the regulations of the Swansea University. I authorize Swansea University to provide my thesis to other institutions or individuals for the purpose of scholarly research. Finally, I understand that my thesis will be submitted electronically to TurnitIn for checking the originality of the thesis, and may be made electronically available to the public. Signature: NAME: PLACE: DATE:

ABSTRACT This research project proposes and investigates the performance of a multi-channel

scheduling algorithm based on the well-known deficit round-robin (DRR) calling it as multi-

channel DRR (MCDRR). The original DRR is then extended to the case of multiple channels

with tunable transmitters and fixed receivers to provide efficient fair queueing in hybrid time

division multiplexing (TDM)/ wavelength division multiplexing (WDM) optical networks.

The availability of channels and tunable transmitters are taken into account in extending the

DRR and allow the overlap of `rounds' in scheduling to efficiently utilize channels and

tunable transmitters. When the tunable transmitter is available, it triggers the scheduling

process. At the start of the First Round, the Round-robin pointer starts from the first flow, if

the packet size is lesser than the deficit counter, the packets are served successfully to their

dedicated channels with channel available at that instant. The packets which cannot be served

due to the packet size greater than the deficit counter and the channel available at that

particular instant of time is neglected. Also, if the Flows are empty, then the particular Flows

are neglected. The pointer moves sequentially as the tunable transmitter triggers at a

particular instant of time and scheduling process starts. The pointer continues from the next

flow after the packets are served successfully/unsuccessfully in the previous flow. Once the

pointer moves through all the given flows, we define it as “Completion of one Round’’.

Simulation results show that the proposed MCDRR can provide nearly perfect fairness with

ill-behaved flows for different sets of conditions for interframe times and frame sizes in

hybrid TDM/WDM optical networks with tunable transmitters and fixed receivers.

Index Terms – Multi-channel scheduling, fair queuing, tunable transmitters, hybrid

TDM/WDM, quality of Service (QoS).

DEDICATION

I would like to dedicate this thesis to my lovely parents Bhuvaneshwari Loganathan and Sathiyanarayananan BK.

ACKNOWLEDGEMENTS

I would like to take this opportunity to express a deep sense of gratitude and thank my supervisor Dr. Kim for providing excellent guidance and unbelievable support during my study on this thesis and made me realize of the potential I have within.

It is due to Dr. Kim that I was introduced to the subject of Network Protocols and Architecture and I thank him for guiding me through the research process. My experience of working as a student of Dr. Kim has been an invaluable asset to me, which will benefit my whole life.

Special thanks to my second supervisor Prof. Thomas Chen for his wordly advice and guidance.

It is because of their constant and general interest and assistance that this project has been successful.

My sincere gratitude to Swansea University for making the International Scholarship Bursary possible.

I would also like to thank my family members for their precious advices and sincere support at all times. They have been a source of encouragement and inspiration throughout this project.

Finally, my thanks go to Sharanya for her everlasting love and care. There has been more than one occasion where she motivated me when I used to get mentally exhausted.

Mithileysh Sathiyanarayanan

Swansea University, United Kingdom

October 2012

Table of Contents

Chapter 1 .......................................................................................................................................... 1

Introduction: ..................................................................................................................................... 1

1.1 Basic Introduction to the Scheduling algorithms ................................................................. 1

1.2 Purpose of the Multi-Channel Scheduling .......................................................................... 1

1.3 Background of the Multi-Channel Scheduling and Scheduling Algorithms in brief ............. 2

1.4 Motivation ............................................................................................................................... 4

1.4.1 Deficit Round-Robin .......................................................................................................... 4

1.4.2 Multi-Channel Scheduling ................................................................................................ 5

1.5 Review of the Existing Work ................................................................................................... 5

1.6 Problem Description ................................................................................................................ 8

1.7 Aims and Objectives of the Thesis ........................................................................................... 9

1.7.1 The aim of the thesis [8]: ................................................................................................... 9

1.7.2 The objectives of the thesis are [8]: ................................................................................... 9

1.8 Thesis Layout ........................................................................................................................ 10

Chapter 2 ........................................................................................................................................ 11

Overview ........................................................................................................................................ 11

2.1 Optical Access ....................................................................................................................... 11

2.2 Quality of Service .................................................................................................................. 16

2.3 Packet scheduling .................................................................................................................. 17

2.3.1 Priority Queue(PQ) ......................................................................................................... 20

2.3.2 Fair Queuing (FQ) .......................................................................................................... 22

2.3.3 Weighted Fair Queuing (WFQ) ....................................................................................... 23

2.3.4 Virtual Clock (VC) ......................................................................................................... 24

2.3.5 Round Robin Scheduling (RR Scheduling) ...................................................................... 24

2.3.6 Weighted Round Robin Scheduling (WRR Scheduling) .................................................. 25

2.3.7 Deficit Round Robin Scheduling (DRR Scheduling ......................................................... 26

2.4 Summary ........................................................................................................................... 29

Chapter 3 ........................................................................................................................................ 30

Deficit Round Robin Scheduling ..................................................................................................... 30

3.1 DRR Algorithm ..................................................................................................................... 33

3.2 DRR Example ....................................................................................................................... 35

3.3 Summary ............................................................................................................................... 41

Chapter 4 ........................................................................................................................................ 42

MCDRR ......................................................................................................................................... 42

4.1 MCDRR Algorithm ............................................................................................................... 45

4.2 MCDRR Example ................................................................................................................. 49

4.3 Summary ............................................................................................................................... 57

Chapter 5 ........................................................................................................................................ 58

SIMULATION ................................................................................................................................ 58

5.1 Methods to Evaluate a Scheduling algorithm ......................................................................... 58

5.2 OMNeT++ Modeller.............................................................................................................. 58

5.2.1 The Simulation System Components ............................................................................... 59

5.2.2 General Procedure to Implement a Model........................................................................ 60

5.2.3 GUI Features .................................................................................................................. 61

5.3 Simulated Scenario ................................................................................................................ 61

5.3.1 System Model ................................................................................................................. 61

5.3.2 Simulation Set-up ........................................................................................................... 61

5.3.3 Simulation Parameters .................................................................................................... 64

5.3.4 Simulation Results .......................................................................................................... 64

5.4 Summary ............................................................................................................................... 73

Chapter 6 ........................................................................................................................................ 74

Conclusion and Future Research...................................................................................................... 74

6.1 Conclusion ............................................................................................................................ 74

6.2 Future Works ......................................................................................................................... 75

Bibliography ................................................................................................................................... 76

FOAN 2012 Conference Paper…………………………………………………………………………………………………………78

List of Figures

Figure 1 : Hybrid Passive Optical Network ...................................................................................... 12

Figure 2 : Block diagram of a hybrid TDM/WDM link based on tunable transmitters and fixed receivers ......................................................................................................................................... 15

Figure 3 : General model of FIFO ................................................................................................... 19

Figure 4 : General model of Multi Queuing ..................................................................................... 20

Figure 5 : General model of Round Robin Scheduling ..................................................................... 25

Figure 6 : General Model of DRR Scheduling ................................................................................. 27

Figure 7 : DRR Tx Diagram ............................................................................................................ 27

Figure 8 : General Model of the DRR .............................................................................................. 31

Figure 9 : DRR Tx Diagram ............................................................................................................ 31

Figure 10 : DRR: Start of Round 1 .................................................................................................. 35

Figure 11 : DRR: End of Round 1 ................................................................................................... 36

Figure 12 : DRR Transmission Diagram after Round 1 .................................................................... 36







Figure 19 : DRR Transmission Diagram after each Rounds ............................................................. 40

Figure 20 : Block diagram of a hybrid TDM/WDM link based on tunable transmitters and fixed receivers ......................................................................................................................................... 42

Figure 21 : MCDRR Scheduler........................................................................................................ 44

Figure 22 : MCDRR: Start of Round 1 ............................................................................................ 49

Figure 23 : MCDRR: End of Round 1 ............................................................................................. 50

Figure 28 : MCDRR Transmission Diagram after Round 1 .............................................................. 51



Figure 29 : MCDRR Transmission Diagram after Round 2 .............................................................. 53



Figure 30 : MCDRR Transmission DIagram after Round 3 .............................................................. 55

Figure 31 : MCDRR Transmission Diagram after the Rounds .......................................................... 55

Figure 32 : MCDRR Transmission Phase Diagram .......................................................................... 56

Figure 33 : MCDRR Tunable Transmitter Selection Diagram .......................................................... 56

Figure 34 : Architecture of OMNet++ Simulation Programs ............................................................ 59

Figure 35 : Heirarchy of the executable files .................................................................................... 60

Figure 36 : MCDRR Graphical NED Editor .................................................................................... 62

Figure 37 : MCDRR Top Level Network ......................................................................................... 62

Figure 38 : MCDRR Graphical Tkenv Runtime environment ........................................................... 63

Figure 39 : Throughput for 16 flows - Case 1 .................................................................................. 65

Figure 40 : Throughput for 16 flows - Case 2 .................................................................................. 65

Figure 41 : Mean Delay for different Quantum Size ......................................................................... 68

Figure 42 : Mean Delay for fixed frame size of 500 and 1000 Bytes ................................................ 69

Figure 43 : Throughput for fixed frame size of 1000 Bytes and 500 Bytes ....................................... 71

Figure 44 : Throughput for different Quantum Size ......................................................................... 72

List of Tables Table 1 : Time Complexity and its function ....................................................................................... 2

Table 2 : Characteristics of TDM-PON and WDM-PON ................................................................. 13

Table 3 : Comparison of HPON and 10G PON ................................................................................ 15

Table 4 : Pseudcode of the DRR algorithm ...................................................................................... 34

Table 5 : Pseudcode of the MCDRR algorithm ................................................................................ 48

Table 6 : Simulation Parameters ...................................................................................................... 64

Table 7 : Simulation Parameters for Scenario 1 ............................................................................... 64



Table 10 : Simulation Parameters for Scenario 3.............................................................................. 70

Table 11 : Simulation Parameters for Scenario 3.............................................................................. 71

Glossary of Terms BPS – Bits Per Second DC – Deficit Counter

DRR – Deficit Round Robin

FQ – Fair Queuing

GPS – Generalized Processor Sharing

LR – Latency rate

MDRR – Modified Deficit Round Robin

MCDRR – Multi-channel Deficit Round Robin

OLT – Optical Line Terminal

ONU – Optical Network Unit

PON – Passive Optical Network

PS – Packet Size

PQ – Priority Queuing

QoS – Quality of Service

QS – Quantum Size

RF – Relative Fairness

RFB – Relative Fairness Bound

RR – Round Robin

SFQ – Stochastic Fair Queuing

TDM – Time Division Multiplexing

WDM – Wavelength Division Multiplexing

WFQ – Weighted Fair Queuing

WRR – Weighted Round Robin

1

Chapter 1

Introduction:

In today's network environment, scheduling management is continually seeking new and

better techniques to manage with the complexities and large data packets to provide better

fairness, higher throughput and lower latency that are the characteristics of many network

resources.

1.1 Basic Introduction to the Scheduling algorithms

Scheduling is a method of harmonizing the access to system resources among competing data

flows [1]. It is achieved by specifying the order and the allotted time period for packets from

each flow. Scheduling is an important part of networking systems because it not only enables

the sharing of the bandwidth but also guarantees the quality of service (QoS). Well-designed

scheduling algorithms could provide higher throughput, lower latency, and better fairness

with lower complexity in serving the packets. As such, the scheduling plays an important role

in achieving high performance of the networking systems. The utility of the network mainly

depends on the “performance'' of its scheduling scheme.

1.2 Purpose of the Multi-Channel Scheduling

The scheduling of packets in switches and routers has become an important topic in

networking and communication [1]. Due to its importance in the networking and

communication, the scheduling has been extensively studied but mainly in the context of

single-channel communication. The advent of wavelength division multiplexing (WDM)

technology, however, demands the extension of this packet scheduling problem to the case of

multi-channel communication, especially with tunable transmitters for hybrid time division

multiplexing (TDM)/ wavelength division multiplexing (WDM) systems. The main objective

of the multi-channel scheduling is to schedule the transmissions of the data over multiple

2

channels to the users. The important measures in choosing a scheduling algorithm are

throughput, latency, fairness, and complexity. The major focus of existing work is mostly on

the throughput and delay performance of the scheduling algorithm in SUCCESS-HPON [2],

but there is hardly any support for fairness and Quality of Service (QoS) guarantee. The main

objective of this thesis is to study the multi-channel scheduling in hybrid TDM/WDM optical

networks with tunable transmitters and fixed receivers providing fairness in throughput. In

this thesis we propose and investigate the performance of a multi-channel deficit round-robin

(MCDRR) scheduling algorithm which can provide throughput fairness among flows with

different size packets with O(1) processing per packet which is nothing but the constant time

complexity. The time complexity of an algorithm is the amount of time taken by an

algorithm to run a particular input defined in the algorithm. The major running time

complexity of algorithms is as follows:

Running Time Function

0(1) Whatever the input, this will return in a

fixed, finite time. (Constant time)

0(NlogN) It would be sorting an input array with a

good algorithm. (low complexity)

0(logN) It would be looking up a value in a sorted

input array by bisection.

Table 1 : Time Complexity and its function

1.3 Background of the Multi-Channel Scheduling and Scheduling Algorithms in brief

Initially the First Come First Served (FCFS) scheme [16] was used for packet scheduling. In

general it is called as the First In First Out (FIFO), Multi-queuing was introduced later on

and then the priority queuing.

The packet schedulers are classified into two categories [17]:

• Timestamp-based schedulers

• Frame-based schedulers

3

Some of the timestamp-based schedulers are Fair Queuing (FQ), Weighted Fair Queuing

(WFQ), Virtual Clock (VC) and Self-Clocked Fair Queuing (SCFQ) etc. and some of the

frame-based schedulers are Round Robin (RR), Deficit Round Robin (DRR), Weighted

Round Robin (WRR) etc [17]. All the above said schedulers are explained in detail in the

Second Chapter.

The broad spread of packet data networks in communications has created a driving force

towards an improved Quality of Service (QoS). The primary thing of Quality of Service

(QoS) is packet schedulers. In this thesis, we introduce a new frame-based scheduling

technique called Multi-channel Deficit Round Robin (MCDRR) that is mainly designed for

providing lower latency bounds, better fairness and maximum throughput. The MCDRR is

computationally very efficient with an 0(1) per packet work complexity, and it provides

better fairness.

The work in this project is based on the deficit round-robin (DRR) scheduling algorithm

which extends the simple round-robin with deficit counters [3]. In the basic DRR scheme,

stochastic fair queuing (SFQ) is used to assign flows to queues [4]. DRR uses the principle of

Round-robin scheduling. The round-robin scheduling is used for servicing the queues with a

quantum of service assigned to each flow. The DRR scheduler in constant rotation selects

packets from each flow to send out to the destination. So the packets from all flows that have

queued gets a fair chance to transmit to the particular destination. The DRR maintains an

active list or called as service list which keeps the record of the non-empty queues in a round

and to avoid examining the empty queues. In the DRR mechanism, the scheduler visits each

non-empty queue in the active list and based on the quantum size, the packets are served. It

differs from the traditional round-robin because if the packet in the previous round was too

large compared to the quantum size, then the remainder from the previous quantum is added

to the quantum for the present round, so the packets can be served successfully in the present

round. This can continue for the next rounds as well. The queues which are not serviced

completely in a round are compensated in the next round. To service one particular flow

again, that flow must wait N-1 flows to be serviced again irrespective of its weight.

4

During each round, a flow can transmit at once as many packets as possible if there is enough

quantum for them. For each flow, two variables --- i.e., quantum and deficit counter --- are

maintained. Quantum is the amount of credits in bytes assigned to each of the flow within the

period of one round. It is very important to decide the quantum size as it might cause fairness

issues. In general if we expect that the work complexity O(1) per packet for the DRR, then

the quantum for a flow should be larger than the maximum packet size from the flow so that

at least one packet per backlogged flow can be served in a round [3]. Due to various reasons

the DRR has poor delay and burstiness properties.

1.4 Motivation

1.4.1 Deficit Round-Robin

DRR is also called as the Deficit Weighted Round Robin (DWRR). It is modified weighted

round robin scheduling discipline. It has become one of the popular fair scheduling schemes

that can handle variable packet sizes. DRR is a round-robin scheduling algorithm extended

by deficit counters [3]. In the basic DRR scheme, stochastic fair queuing (SFQ) is used to

assign flows to queues [4]. DRR uses the principle of Round-robin scheduling. The round-

robin scheduling is used for servicing the queues with a quantum of service assigned to each

flow. The DRR scheduler in constant rotation selects packets from each flow to send out to

the destination. So the packets from all flows that have queued gets a fair chance to transmit

to the particular destinations. The DRR maintains an active list or called as service list which

keeps the record of the non-empty queues in a round and to avoid examining the empty

queues. In the DRR mechanism, the scheduler visits each non-empty queue in the active list

and based on the quantum size, the packets are served. It differs from the traditional round-

robin because if the packet in the previous round was too large compared to the quantum

size, then the remainder from the previous quantum is added to the quantum for the present

round, so the packets can be served successfully in the present round. This can continue for

the next rounds as well. The queues which are not serviced completely in a round are

compensated in the next round. To service one particular flow again, that flow must wait N-1

flows to be serviced again irrespective of its weight. During each round, a flow can transmit

at once as many packets as possible if there is enough quantum for them. For each flow, two

variables --- i.e., quantum and deficit counter --- are maintained. Deficit counter is the amount

5

of credits in byte, which constantly compares its credits with the packet sizes in the flow. Initially

deficit counter starts at zero. It increments based on the quantum size set. Quantum is the amount

of credits in bytes assigned to each of the flows within the period of one round. The round

robin pointer moving through the set of flows defined using the deficit counters is known as

the period of one round. It is very important to decide the quantum size as it might cause

fairness issues. In general if we expect that the work complexity O(1) per packet for the

DRR, then the quantum for a flow should be larger than the maximum packet size from the

flow so that at least one packet per backlogged flow can be served in a round [3]. Due to

various reasons the DRR has poor delay and burstiness properties.

1.4.2 Multi-Channel Scheduling

The “Design and Performance analysis of scheduling algorithms for WDM-PON under

SUCCESS-HPON architecture'' [2] gives us ain depthth idea of using scheduling algorithms

for the multi-channel case. We consider only tunable transmitters in the multi-channel case.

The multi-channel scheduling and its applications rely on the ability of the whole system to

provide some sort of quality of service guarantees. There are several measures that are to be

considered when choosing a scheduling algorithm. The most important are: fairness, latency

and the complexity [5]. In multi-channel Scheduling, it is the scheduling algorithm that is key

to achieve high performance. The major focus is on the delay and throughput performance of

the whole system, there is hardly any support for fairness and Quality of Service (QoS)

guarantee [5]. Most of the work in FQ has been done for the single channel case; there are

only a few results for the multichannel case but none for the case using the efficient DRR

technique. The DRR is a simple scheduling algorithm which is for a single channel case. We

have extended the DRR to a multi-channel case, which is namely MCDRR.

1.5 Review of the Existing Work

As for multi-channel scheduling with tunable transceivers in hybrid TDM/WDM optical

networks, the work for the SUCCESS-HPON architecture in [2] provides a detailed

6

investigation of several multi-channel scheduling algorithms like batching earliest departure

first (BEDF) and sequential scheduling with schedule time framing S3F under realistic

environments, which is one of the basis for the work in this thesis. Through extensive

simulations using tunable transmitters and receivers, it has been demonstrated that both the

BEDF and S3F improves the throughput and delay performances. Note that we consider the

case of tunable transmitters and fixed receivers in this thesis, while the SUCCESS-HPON

architecture is based on both tunable transmitters and tunable receivers.

The proposed MCDRR is based on the deficit round-robin (DRR) scheduling algorithm

which extends the simple round-robin with deficit counters [3]. The DRR provides good

fairness, lower complexity, and lower implementation cost, which makes it an ideal

candidate for high-speed gateways or routers. In the basic DRR scheme, stochastic fair

queuing (SFQ) is used to assign flows to queues. DRR uses the principle of Round-robin

scheduling. The round-robin scheduling is used for servicing the queues with a quantum of

service assigned to each flow. The DRR scheduler in constant rotation selects packets from

each flow to send out to the destination. So the packets from all flows that have queued gets a

fair chance to transmit to the particular destinations. The DRR maintains an active list or

called as service list which keeps the record of the non-empty queues in a round and to avoid

examining the empty queues. In the DRR mechanism, the scheduler visits each non-empty

queue in the active list and based on the quantum size, the packets are served. It differs from

the traditional round-robin because if the packet in the previous round was too large

compared to the quantum size, then the remainder from the previous quantum is added to the

quantum for the present round, so the packets can be served successfully in the present



again, that flow must wait N-1 flows to be serviced again irrespective of its weight. During

each round, a flow can transmit at once as many packets as possible if there is enough


maintained. Quantum is the amount of credits in bytes assigned to each of the flow within the


issues. In general if we expect that the work complexity O(1) per packet for the DRR, then

7

the quantum for a flow should be larger than the maximum packet size from the flow so that

at least one packet per backlogged flow can be served in a round [3]. Due to various reasons

the DRR has poor delay and burstiness properties.

Note that the multi-channel scheduling have been studied by others in slightly different

contexts than that of the our thesis scheduler: For instance, optimal wavelength scheduling

for Hybrid WDM/TDM Passive Optical Networks (PONs) [5] inspects the upstream

wavelength scheduling in hybrid wavelength division multiplexing and time division

multiplexing passive optical networks (WDM/TDM PONs), where the minimum resource

allocation unit is a time slot on a wavelength.

They use three optimal wavelength scheduling algorithms for the three kinds of hybrid

WDM/TDM PONs [5].

• Type-I WDM/TDM PONs: Each optical network unit (ONU) has a single optical

transmitter with a tunable wavelength.

• Type-II WDM/TDM PONs: Each ONU still has a single transmitter, but some are

fixed to transmit at a certain wavelength.

• Type-III WDM/TDM PONs: Each ONU has one or more transmitters and all

transmitters can tune their wavelengths.

They proposed algorithms based on the round-robin scheduling (RRS) and shortest channel

first (SCF) concept to calculate the optimal schedule length and achieve the best wavelength

scheduling with the shortest schedule length and the maximum channel utilization.

Also, to provide a fairness guarantee with multiple channels, the extension of fair queueing

(FQ) has been studied in [6], but they are for fixed transceivers as in static WDM systems.

The closest to our work in this paper is the study of multi-server round-robin scheduling in

[7]. Unlike the hybrid TDM/WDM optical network where a specific wavelength is dedicated

to a specific destination, however, they assume that flows can use any of multiple channels.

8

1.6 Problem Description

The thesis is mainly based on the tunable transmitters and fixed receivers in the multi-channel system.

We look to extend fair queuing (FQ) framework to the case of multiple channels with tunable

transmitters and fixed receivers by designing multichannel schedulers for hybrid TDM/WDM PON’s.

It requires investigation in the performance of a multi-channel scheduling algorithm, which can avoid

packet delay and provide fairness (in terms of throughput) for flows with different size packets with

O(1) processing per packet and to reduce end-to-end delay. We need to present an extensive

set of performance and validation test results obtained from simulations conducting using

network models built for each one of the scheduling algorithms under consideration. Later,

we need to discuss the results in detail and explain what needs to be carried out in the future.

If time permits, we will provide mathematical bounds for the results obtained.

9

1.7 Aims and Objectives of the Thesis

1.7.1 The aim of the thesis [8]: In this Research program, we propose new multi-channel scheduling algorithms with tunable

transmitters and fixed receivers which provide fairness and QoS guarantee in hybrid

TDM/WDM optical networks for flows with different size packets (Next-Generation

Networks).

1.7.2 The objectives of the thesis are [8]:

• To extend fair queuing (FQ) framework to the case of multiple channels with tunable

transmitters and fixed receivers.

• To design multichannel schedulers using tunable transmitters for hybrid TDM/WDM

PON’s.

• To design and evaluate the performance of schedulers providing fairness and QoS

guarantee.

• To avoid packet delay and improve throughput of the system (provide fairness).

• To implement detailed simulation models for the designed multi-channel scheduler

for hybrid TDM/WDM PON and evaluate under realistic network and service

environment.

• Finally, to provide mathematical bounds for the simulation models.

10

1.8 Thesis Layout

In Chapter One an Introduction to the concept of multi-channel scheduling and the

importance of quality of service is given. Furthermore, the motivation behind this topic and

the aim of the thesis was identified plus giving a description of the general layout of the

forthcoming chapters.

Chapter Two describes the Overview of the concept multi-channel scheduling and scheduling

algorithms in detail. The review of the existing work is described in detail

Chapter Three describes the Deficit Round Robin Scheduling in detail with an algorithm and

an example, its advantages and disadvantages.

Chapter Four is the core of the thesis and it talks about the Multi-Channel Deficit Round

Robin (MCDRR) Scheduling algorithm in detail. The algorithm and an example is explained

in this chapter.

Chapter Five presents the simulations and discusses the outcomes. We present an extensive

set of performance and validation test results obtained from simulations conducted using

network models built for each one of the scheduling algorithms under consideration.

Finally, Chapter Six gives a conclusion of the work done and the future works are discussed

in detail.

11

Chapter 2

Overview

2.1 Optical Access

A passive optical network (PON) [9] is a point-to-multipoint service network which has an

optical line terminal (OLT) at the service provider's central office and a number of optical

network units (ONUs) at the end users as shown in figure 1. The basic building blocks of a

PON are OLT, ONUs and the fibers and splitters between them. The OLT provides the

interface between the backbone network and the PON. The ONU terminates the PON and

provides service to the users. PONs are deployed for the provision of Fiber-To-The-Home

(FTTH) services by utilizing their ability to share the fiber bandwidth among users in an

economical way. PON is called passive because there are no active element within the access

network other than at the central office, this is not necessary to be true as well. A PON

enables the major services like voice, video and data to the users.

PON enable users with the following services [9]:

• Digital Entertainment:

1) IPTV

2) Video on Demand

3) Video Telephony

4) Audio on Demand etc

• Broadband Data services:

1) High Speed Internet access.

2) Data (Ethernet)

3) Telephone services

4) VoIP ( Voice Over IP)Telephony

12

The different types of PON configurations are [10]:

• Time Division Multipltiplexing (TDM) PONs:

1) Asynchronous Transfer Mode (ATM) PON

2) Broadband PON (BPON)

3) Gigabit PON (GPON)

4) Ethernet PON (EPON)

• Wavelength Division Multiplexing (WDM) PONs

• Hybrid TDM/WDM PONs (HPONs)

In this thesis, we concentrate on the Hybrid TDM/WDM PONs (HPONs) which is called as

Next-Generation Networks. The TDM and WDM combinations give good transmission

properties for favorable economic aspect and the upgradability facilities. The detailed

explanation about the HPONs are as follows:

Figure 1 : Hybrid Passive Optical Network

Hybrid Passive Optical Network (HPON) is a combination of Time division multiplexing

(TDM) and Wavelength division multiplexing (WDM) technologies. To obtain the best

topology properties for data transmission we use both TDM and WDM which have their pros

and cons. The disadvantages of TDM are the advantages of WDM [8].

13

In TDM-PON, the capacity is good, Cost is low and Reliability is best but the upgradability

is difficult whereas in WDM-PON, the capacity is better, the upgradability is easy, reliability

is better but the cost is higher [8].

Characteristics of TDM-PON and WDM-PON [11]

Characteristics TDM-PON WDM-PON

Capacity Good Better

Cost Low Higher

Upgradability Difficult Easy

Reliability Best Better

Table 2 : Characteristics of TDM-PON and WDM-PON

The TDM and WDM combinations give good transmission properties for favorable

economic aspect and the upgradability facilities.

The operation between the OLT and ONT is done using the WDM multiplex and is then

divided by the arrayed waveguide gratings (AWG) demultiplexer into individual

wavelengths, which are then sent to the topology. A part of the network, which is behind the

WDM demultiplexer uses the TDM multiplexer. The TDM demultiplexer is located close to

the target area.

WDM-PON OLT and ONU under the Standford University aCCESS-Hybrid PON

(SUCCESS-HPON) architecture [2] is the target system for this thesis. SUCCESS-HPON is

a research initiative for the next-generation hybrid TDM/WDM optical access architecture.

The SUCCESS-HPON architecture is based on a ring plus distribution tree topology, fast

centralized tunable components and novel scheduling algorithms which exploit the benefits

of flexibility and dynamically-reconfigurable optical networks in access. It has guaranteed a

smooth transition from current TDM-PONs to future WDM-based optical access. The

14

upgrade path from pure TDM-PON to WDM-PONs in SUCCESS-HPON initiative will be

in an economical manner.

The SUCCESS-HPON [2] research initiative has answered a few questions like

• Backward compatibility, which guarantees the coexistence of current-generation

TDM-PONs and next-generation WDM-PONs in the same network.

• Easy upgradeability, which means providing smooth migration paths from TDM-

PON to WDM-PON.

• Protection/restoration capability, which means to support both residential and

business users on the same access infrastructure.

The SUCESS-HPON architecture [2] includes both TDM-PONs and WDM-PONs as its

subsystems for getting the benefits of both TDM-PONs and WDM-PONs. The SUCCESS-

HPON architecture consists of a collector ring with the stars connecting the central office

(CO) and optical networking units (ONU’s). The ONU’s are attached to the remote node’s

(RN’s) . An optical line terminal (OLT) uses tunable transmitters and receivers that are

shared by all optical network units (ONU’s). The tunable transmitters at the OLT are used for

both upstream and downstream transmissions. The sharing of tunable transmitters and

receivers at the OLT and the use of tunable transmitters for both upstream and downstream

transmissions, they use two new scheduling algorithms namely batching earliest departure

first (BEDF) and sequential scheduling with schedule-time framing (S3F) to provide good

transmission efficiency and fairness guarantee between upstream and downstream traffic.

15

Comparison of HPON versus 10G PON [12]

Characteristics HPON 10G PON

Feeder Fiber Capacity 40Gbps ~64 Gbps 10 Gbps

Line Bit Rate Dn:1.25~2.5 Gbps

Up:1.25~2.5 Gbps

Dn:10 Gbps

Up: 1.25 ~ 2.5 Gbps

Reach ~40km ~20 km

Splitting Ratio ~512 ~32

Av. BW/Subs. Symmetric

Dn:78~125 Mbps

Up:78~125 Mbps

Asymmetric

Dn: 312 Mbps

Up: 31.2~312 Mbps

Table 3 : Comparison of HPON and 10G PON

In HPON, The feeder fiber capacity has the average of 50Gbps with a reach of almost 40kms with the splitting ratio of 1:512 which is quite significant in considering for the project. The detailed explanation is as follows:

Figure 2 : Block diagram of a hybrid TDM/WDM link b ased on tunable transmitters and fixed receivers

The multi-channel scheduling with tunable transmitters and receivers in WDM PONs have

been demonstrated by simulations and test bed experiments through projects under the

SUCCESS initiative [5, 6]. The upstream flows are scheduled together with the downstream

16

ones in an integrated way in the SUCCESS-HPON, where both upstream ones and

downstream ones are dependent.

In this thesis, we consider a hybrid TDM/WDM link using tunable transmitters at the

transmitting side and fixed receivers at the receiving ends. This hybrid TDM/WDM link

model as shown in figure 2 is general enough to be applied to downstream flows in a hybrid

TDM/WDM-PON. The performance of this model needs to be investigated.

2.2 Quality of Service

Quality of Service (QoS) [13] in the field of communications can be defined as a set of

specific requirements provided by a network to users in order to achieve required service.

Quality of Service (QoS) for communication networks is a set of methods for establishing

reliable performance of the networks. It is a method to guarantee a bandwidth (throughput) to

the network. The network performance which depends on QoS often includes bandwidth

(throughput), delay (latency), variable delay (jitter) and error rate (packet loss).

� Bandwidth

Bandwidth is measured as the average number of bits per second (bps) that can travel

successfully through the network. Bandwidth refers to the “speed” or throughput of the

network.

� End-to-end delay

End-to-end delay is the average time it takes for a data packet to traverse the network from

one end to an other end. End-to-end delay refers to the latency.

17

� Variable Delay

Variable delay is the variation in the end-to-end delay of sequential packets. The sequential

packets from the source will reach the destination with different delays. Variable delay refers

to Jitter.

� Packet Loss

Packet loss is the number of transmitting packets that never reached the particular

destinations.

The QoS required in day to day life are [13]:

• IP telephony

• Video and sound conferencing

• Video-on-Demand (watching movies at home over IP)

• Video surveillance

2.3 Packet scheduling

Packet scheduling plays an important role in providing QoS guarantees in packet data

networks. The QoS guarantees include guaranteed bit rate, bounded delay and fairness to all

the flows.

Scheduling means bandwidth sharing. After Packet Switching networks came into existence,

there was a need to differentiate between different types or classes of packets. From there on,

the packet scheduling has been a hot research topic and its still being investigated at many

institutions [14].

18

The main aims and objectives of the concept of packet scheduling are: [14] [15]

• The main goal of a scheduling algorithm is to be able to share the total system

bandwidth fairly.

• The scheduling algorithm should be able to guarantee the minimum bandwidth per

user.

• To be able to meet the packet drop guarantees.

• To be able to assure latency guarantees.

• To be able to reduce latency variation.

• Achieving the above enumerated goals results in QoS provisioning.

The metrics on which scheduling algorithms will always be compared are as follows [14]

[15]:

• Simplicity

• Fairness

• Efficiency

• QoS Guarantee

• Flexibility

• Link

• Protection

The parameters for the better performance of the scheduling algorithms in network systems

are [14] [15]:

• Good Throughput

• Good Stability

• Guaranteed QoS

• Low Latency

• Low Complexity

• High Reliability

19

Initially the First Come First Served (FCFS) scheme [16] was used for packet scheduling. In

this scheme all the packets are treated equally which is then placed onto a single queue and

then serving them in the same order in which they were placed in the queue. In detail, the

packets coming from all the sources were enqueued into a First In First Out (FIFO) queuing

system which has a memory, then the packets were dequeued one by one to the output link or

destination. This is shown in figure 3. Since all the packets were treated equally and packets

which required urgent delivery could not be achieved at that instant caused fairness issues

and delay. There is no proper scheduling action taking place in this scheme.

Figure 3 : General model of FIFO

Advantages:

• Easy to implement

• Low Cost

• Low Computational Load

• Highly Predictable

• Preference is always given to packets that arrive first

Disadvantages:

• Classes are not differentiated properly. (All the packets are treated equally)

• High Delay and jitter for real time applications.

• Fairness is not guaranteed.

20

• Bandwidth is not shared equally.

• Inefficient treatment of the flows.

• The ill-behaved flows are not protected.

Later on, different queues were specified and assigned to the packets which are not similar

(non similar) for achieving packet classification. By this way the scheduling was done. The

main task of the scheduling algorithm is to move through all the flows and choose the packet

from each flow to be dequeued from the available queues and serve to the output link. This is

illustrated in figure 4.

Figure 4 : General model of Multi Queuing

2.3.1 Priority Queue(PQ)

Priority queue (PQ) [16] offers a basic and relatively simple method to support the

differentiated service classes. The packets are classified by the system and then they are

placed into different priority queues. Subsequently, packets are scheduled from the head of a

given queue only if higher priority queues are empty. Within the priority queues, packets are

scheduled in FIFO order.

21

Advantages:

• Easy to implement.

• Low Cost.

• Low Computational Load.

• Class are differentiated.

Limitations:

• Bandwidth is not shared equally.

• Delay in real time applications.

• Inefficient treatment of the flows.

The modern packet scheduling theory is based on one of the concept called Generalized

Processor Sharing (GPS). GPS is an ideal scheduler that is served in a round-robin fashion,

an infinitesimal amount of data from each non-empty queue based on the flows's reserved

rate or relative bandwidth weight. In any finite time interval, the scheduler visits every queue

at least once [17].

Advantages:

• Ideal fairness and complete isolation of flows

• It provides every flow with its guaranteed bit rate.

Limitations:

• It is based on the fluid model, so it is not implementable.

The packet schedulers are classified into two categories [17]:

• Timestamp-based schedulers

• Frame-based schedulers

22

In timestamp-based schedulers, packets are given a time related stamp upon their arrival to

the system. The timestamp is used by the scheduler to determine the sequence in which

packets depart the system. These schedulers can achieve a good approximation of the GPS

fluid model by providing low latency bounds and providing good fairness. The major

disadvantage is high work complexity. Some of the timestamp-based schedulers are Fair

Queuing (FQ), Weighted Fair Queuing (WFQ), Virtual Clock (VC) and Self-Clocked Fair

Queuing (SCFQ) etc [17].

In frame-based schedulers, packets are served in rounds or by frames. During each round, a

flow receives at least an opportunity to transmit the data packets or frame. The schedulers are

known for their simplicity and low work complexity. Some of the frame-based schedulers are

Round Robin (RR), Deficit Round Robin (DRR), Weighted Round Robin (WRR) etc [17].

Now we review some of the well-known schedulers.

2.3.2 Fair Queuing (FQ)

Fair queuing is a scheduling algorithm which is used to allow multiple packets from the

flows to fairly share the link capacity. It is a timestamp-based scheduling scheme. It can be

interpreted as a packet approximation of generalized processor sharing (GPS). Fair queuing

was proposed by John Nagle in 1985 [18].

Fair queuing achieves the max-min fairness, i.e. firstly, the minimum data rate that a data

flow achieves is maximized; secondly, the second lowest data rate that a data flow achieves

is maximized, etc. This results in lower throughput.

When compared to the round-robin scheduling, fair queuing takes into account data packet

sizes to ensure that each flow gives equal opportunity to transmit an equal amount of data.

Packet-based flows are however transmitted in sequence. The Fair queuing selects the

transmission order for the packets by checking (modeled) the finish time for each packet and

23

they are transmitted bitwise round robin. The packet with the earliest finish time according to

the transmission order (modeling) is the next selected for transmission [19].

2.3.3 Weighted Fair Queuing (WFQ)

Weighted fair queuing (WFQ) is a scheduling scheme which allows different scheduling

priorities to statistically multiplexed data flows [20]. WFQ is a generalization of fair queuing

(FQ). It emulates the Generalized Processor Sharing (GPS). GPS is a theoretical scheduler

unable to be implemented. It is a timestamp-based scheduling scheme. WFQ supports the

flows with different bandwidth requirements by giving each queue a weight that assigns it a

different percentage of output port bandwidth. It supports variable-length packets so that

flows with larger packets are not being allocated more bandwidth than flows with the smaller

packets. Supporting variable-length packet increases considerably the total computational

complexity of this discipline. Furthermore, its complexity is higher since it needs to calculate

the finish time of each packet. Finish time represents the number assigned to each packet to

express the order by which they should be transmitted on the output port (Demers and

Keshav in 1989) [19].

Advantages:

• Protection to each service class.

• Performs well with the variable size packets.

• Weighted and fair share of output port bandwidth to each service class with a

bounded delay.

Limitations:

• It can be implemented only in the software.

• The ill-behaved flows are not protected.

• Scalability problems.

• High Complexity.

24

• The complexity affects its scalability to work in high-speed interfaces.

• There may be large delays though it provides guaranteed delays.

2.3.4 Virtual Clock (VC)

Virtual Clock [21] is derived from a Time Divison Multiplexing (TDM) system. It allocates a

guaranteed throughput to the flows according to their expected packet arrival rates. It is a

timestamp-based scheduling scheme. The Virtual Clock (VC) scheduling algorithm provides the same

end-to-end delay bound as Weighted Fair Queuing (WFQ) with a simple time-stamp computation

algorithm. The disadvantage of this algorithm is that a backlogged packets can be starved for a long

time [19].

2.3.5 Round Robin Scheduling (RR Scheduling)

The Round-Robin Scheduling is a one of the earliest and simplest frame-based scheduling

techniques used in the networking field. It is the least complex scheduling algorithm. It has a

complexity value of O(1) [22]. The RR algorithm services the backlogged packets in a round

robin fashion. Each time the scheduler pointer moves to a particular queue, one packet is

dequeued from that queue unless the queue is empty and then the scheduler pointer goes to

the next queue. This is shown in Figure (5)

The round robin scheduling algorithm does not provide very high fairness in systems with

variable packet lengths, since the Round Robin tends to serve flows with longer packets for a

long time [19].

25

Figure 5 : General model of Round Robin Scheduling

Advantages: • It is simple.

• 0(1) per packet work complexity.

Limitations:

• Poor fairness

• Poor delay

2.3.6 Weighted Round Robin Scheduling (WRR Scheduling)

A Weighted Round Robin (WRR) scheduler [23] is the simplest approximation of

generalized processor sharing (GPS) which serves multiple packets from a flow according to

the flow's normalized weight. WRR operates on the same basis of Round Robin scheduling,

where the weight of a flow is defined as its relative share of the total link bandwidth.

However, unlike round robin scheduling, WRR assigns a weight to each queue In WRR, the

mean packet size for all the flows are known prior to scheduling packets to overcome

unfairness. WRR was designed to differentiate flows or queues to enable various service

rates. The weight of an individual flow is defined as its relative share of the total link

bandwidth (available system bandwidth). This means that, the number of packets dequeued

from a queue varies according to the weight assigned to that queue [19].

26

Advantages:

• It is simple

• It has a 0(1) computational complexity per packet.

Limitations:

• Poor fairness

• Poor delay

2.3.7 Deficit Round Robin Scheduling (DRR Scheduling)

The Deficit Round Robin (DRR) [17] scheduling algorithm was the first frame-based

scheduling algorithm to overcome the unfairness characteristic caused by the variable packet

sizes by the different flows.The DRR scheduling algorithm overcame the unfairness

characteristic of the both round-robin algorithm and weighted round-robin algorithm. The

DRR retains the per packet complexity value of O(1) from the round-robin scheduling.

In DRR scheduling, every flow is allotted with a deficit counter which is initially set to the

desired quantum value. Deficit counter is the amount of credits in a byte, which constantly

compares its credits with the packet sizes in the flow. Initially deficit counter starts at zero. It

increments based on the quantum size set. A quantum is the amount of credits in bytes

assigned to each of the flow within the period of one round. It is very important to decide the

quantum size as it might cause fairness issues. A quantum represents the amount of bytes a

queue may require in order to serve the packets in the flow.

The deficit counter is incremented by the quantum size in every round of the scheduler. The

deficit counter is reduced by the amount of packets being served in every visit by the

scheduler. The packets are served only if the packet sizes are lesser than the deficit counter.

If the packet sizes are lesser than the deficit counter, then the packets are backlogged and

then they inspect the deficit counter in the next round. In the next round the deficit counter

value gets added to the quantum value, so the packets can be served successfully. Once the

27

packets are served, the deficit counter is reduced by the amount of packets being served. If

there are no packets to be served in the flow, then the deficit counter resets to zero.

If the quantum assigned to a flow is significantly lesser than the maximum size of the packets

that arrive to the system, this causes delay and burstiness. At the same time, if the quantum

assigned to a flow is significantly higher than the maximum size of the packets that arrive to

the system, this causes a short-time unfairness of the system, leading to a higher latency

bound which might increase gradually. The DRR algorithm also requires the knowledge of

the packet size prior to scheduling it, this information could be available in the packet header

of IP packets.

Figure 6 : General Model of DRR Scheduling

Figure 7 : DRR Tx Diagram

28

The DRR Tx diagram which has packets in the y-axis and time in x-axis. The DRR Tx

diagram shows that the Round 2 starts after the completion of Round 1.

Advantages:

• It is simple

• Cost-effective

• It has a complexity of O(1).

Limitations:

• There is latency bound for real time applications.

• It does not provide end to end delay guarantees.

• Not very accurate as other schedulers.

On the other hand, DRR is the most popular and deployed schemes. DRR is Cisco’s favorite

scheduling algorithm due to its simplicity and low cost of implementation. Compared with

Fair Queuing (FQ) scheduler that has a complexity of O (log N) (N is the number of active

flows), the complexity of DRR is O(1).

Taking into account all drawbacks of the aforementioned schedulers, we choose the well-

know basic Deficit Round Robin (DRR) scheduler as the queuing scheme and packet

scheduling for our framework. The scheduler we will be designing is conceptually based on

the DRR approach [3]. We propose an enhancement of the DRR, that is called as Multi-

Channel Scheduling Deficit Round Robin (MCDRR). In this MCDRR Scheduling, we are

using the concept of basic DRR Single channel case. We extend the original DRR to the case

of multiple channels with tunable transmitters and fixed receivers to provide efficient fair

queueing in hybrid time division multiplexing (TDM)/wavelength division multiplexing

(WDM) optical networks. We take into account the availability of channels and tunable

transmitters in extending the DRR and allow the overlap of `rounds' in scheduling to

efficiently utilize channels and tunable transmitters. Simulation results show that the

proposed MCDRR can provide nearly perfect fairness with ill-behaved flows for different

29

sets of conditions for interframe times and frame sizes in hybrid TDM/WDM optical

networks with tunable transmitters and fixed receivers.

Scheduling algorithms are the potential solution which has been taken into major concern,

and the present well known algorithms have been briefly described. Among the scheduling

algorithms that have been briefly described before and for the purpose of achieving

efficiency in multi-channel scheduling, the Multi-Channel Deficit Round Robin (MCDRR)

scheduling algorithm’s attributes, properties and architecture have been studied in detail.

2.4 Summary

This chapter presented a brief review of the two major categories of packet schedulers:

frame-based schedulers and timestamp-based schedulers. Different scheduling algorithms

that have been briefly described, their advantages and their disadvantages. As mentioned

before, this thesis is mainly concerned with the the frame-based schedulers because of their

efficiency and simplicity. In the frame-based schedulers, we choose DRR. DRR is the most

popular and deployed schemes. DRR is Cisco’s favorite scheduling algorithm due to its

simplicity and low cost of implementation. Compared with Fair Queuing (FQ) scheduler that

has a complexity of O (log N) (N is the number of active flows), the complexity of DRR is

O(1). The next chapter explains in detail the importance of using DRR in this thesis.

30

Chapter 3

Deficit Round Robin Scheduling

The Deficit Round Robin (DRR) [17] scheduling algorithm was the first frame-based


sizes by the different flows. The DRR scheduling algorithm overcame the unfairness

characteristic of the both round-robin algorithm and weighted round-robin algorithm. The

DRR retains the per packet complexity value of O(1) from the round-robin scheduling. DRR

is also called as the Deficit Weighted Round Robin (DWRR). It is a modified weighted round

robin scheduling discipline. It has become one of the most popular fair scheduling schemes

that can handle variable packet sizes. DRR is a round-robin scheduling algorithm extended

by deficit counters [3]. In DRR scheduling, every flow is allotted with a deficit counter

which is initially set to the desired quantum value. Deficit counter is the amount of credits in

byte, which constantly compares its credits with the packet sizes in the flow. Initially deficit

counter starts at zero. It increments based on the quantum size set. A quantum is the amount

of credits in bytes assigned to each of the flow within the period of one round. It is very

important to decide the quantum size as it might cause fairness issues. A quantum represents

the amount of bytes a queue may require in order to serve the packets in the flow.

In the basic DRR scheme, stochastic fair queuing (SFQ) is used to assign flows to queues.

DRR uses the principle of Round-robin scheduling. The round-robin scheduling is used for

servicing the queues with a quantum of service assigned to each flow. The DRR scheduler in

constant rotation selects packets from each flow to send out to the destination. So the packets

from all flows that have queued gets a fair chance to transmit to the particular destinations.

The DRR maintains an active list or called as service list which keeps the record of the non-

empty queues in a round and to avoid examining the empty queues. In the DRR mechanism,

the scheduler visits each non-empty queue in the active list and based on the quantum size,

the packets are served. It differs from the traditional round-robin because if the packet in the

previous round was too large compared to the quantum size, then the remainder from the

31

previous quantum is added to the quantum for the present round, so the packets can be served

successfully in the present round. This can continue for the next rounds as well. The queues

which are not serviced completely in a round are compensated in the next round. To service

one particular flow again, that flow must wait N-1 flows to be serviced again irrespective of

its weight. During each round, a flow can transmit at once as many packets as possible if

there is enough quantum for them. For each flow, two variables --- i.e., quantum and deficit

counter --- are maintained. Quantum is the amount of credits in bytes assigned to each of the

flow within the period of one round. It is very important to decide the quantum size as it

might cause fairness issues. In general if we expect that the work complexity O(1) per packet

for the DRR, then the quantum for a flow should be larger than the maximum packet size

from the flow so that at least one packet per backlogged flow can be served in a round [3].

Due to various reasons the DRR has poor delay and burstiness properties.

Figure 8 : General Model of the DRR

Figure 9 : DRR Tx Diagram

32

The DRR Tx diagram which has packets in the y-axis and time in x-axis. The DRR Tx

diagram shows that the Round 2 starts after the completion of Round 1.

The Deficit Round Robin scheduling algorithm was the first frame-based scheduling

algorithm to overcome the unfairness characteristic caused by the variable packet sizes by the

different flows.The DRR scheduling algorithm overcame the unfairness characteristic of the

both round-robin algorithm and weighted round-robin algorithm. The DRR retains the per

packet complexity value of O(1) from the round-robin scheduling. In this DRR scheduling,

every flow is allotted with a deficit counter which is initially set to the desired quantum

value. A quantum is the amount of credits in bytes assigned to each of the flow within the


issues. A quantum represents the amount of bytes a queue may require in order to serve the

packets in the flow.The deficit counter is incremented by the quantum size in every round of

the scheduler. The deficit counter is reduced by the amount of packets being served in every

visit by the scheduler. The packets are served only if the packet sizes are lesser than the

deficit counter. If the packet sizes are lesser than the deficit counter, then the packets are

backlogged and then they inspect the deficit counter in the next round. In the next round the

deficit counter value gets added to the quantum value, so the packets can be served

successfully. Once the packets are served, the deficit counter is reduced by the amount of

packets being served. If there are no packets to be served in the flow, then the deficit counter

resets to zero. If the quantum assigned to a flow is significantly lesser than the maximum size

of the packets that arrive to the system, this causes delay and burstiness. At the same time, if

the quantum assigned to a flow is significantly higher than the maximum size of the packets

that arrive to the system, this causes a short-time unfairness of the system, leading to a higher

latency bound which might increase gradually. The DRR algorithm also requires the

knowledge of the packet size prior to scheduling it, this information could be available in the

packet header of IP packets.

Advantages of the DRR:

• It is simple

• Cost-effective

• It has a complexity of O(1).

33

Limitations of the DRR:

• There is latency bound for real time applications.

• It does not provide end to end delay guarantees.

• Not very accurate as other schedulers.

Due to its major advantages such as simple mechanism, cost-effective and has a complexity

of O(1), we choose DRR single channel scheduling technique to extend it to multi-channel

case.

3.1 DRR Algorithm

In DRR algorithm, every flow is assigned with a deficit counter which is initially set to the

desired quantum value. In the algorithm for ‘n’ flows, deficit counter is set to zero. The

round-robin pointer moves through all the ‘n’ flows sequentially from the first flow to the nth

flow. Once initialized, we need enqueuing module. Once the packets arrive in the flow, it

gets queued and the deficit counter is set to the quantum for each flow. In the dequeuing

module, the deficit counter is incremented by the quantum size in every round of the

scheduler. The deficit counter is reduced by the amount of packets being served in every visit

by the scheduler. The packets are served only if the packet sizes are lesser than the deficit

counter. If the packet sizes are lesser than the deficit counter, then the packets are backlogged

and then they inspect the deficit counter in the next round. In the next round the deficit

counter value gets added to the quantum value, so the packets can be served successfully.

Once the packets are served, the deficit counter is reduced by the amount of packets being

served. If there are no packets to be served in the flow, then the deficit counter resets to zero.

The DRR algorithm also requires the knowledge of the packet size prior to scheduling it, this

information could be available in the packet header of IP packets. The below table shows the

algorithm of the DRR.

34

Table 4 : Pseudcode of the DRR algorithm

Initialization For ( i=0; i<n; i=i+1) DC=0; Enqueuing module i= Packet Flow (); queue.insert (packet); DCi =Qi ; Dequeuing module If (No. of packets in queue i>0) { DCi = Qi + DCi; While((DCi > 0)) Do{ If (PSi ≤ DCi) [SERVE PACKET]; DCi = DCi – PSi; ElseIf (PSi > DCi) DCi = 0; //Reset } } If (No. of packets in queue i=0) DCi = 0;

35

3.2 DRR Example

Figure 10 : DRR: Start of Round 1

Once the packets arrive in the flow, the scheduling process starts. At the start of the First

Round, the round robin pointer starts from the first flow initialized. The deficit counter

becomes equal to the quantum size. If the packet size is lesser than the deficit counter and

channel is available at that instant of time, the packet is served. If the packet size is greater

than the deficit counter, the pointer is moved to the next flow. In the next round, the packets

backlogged will be transmitted in the next round.

In the example quantum size is considered to be 500 credits, the pointer starts from the Flow

1, the packet of size 110 bytes will be served since it is less than the deficit counter 500

credits and the channel is available at that instant of time. After serving, the deficit counter is

updated, that is DC becomes 390 credits. Once the channel is available, it triggers the

scheduling and now the pointer moves to the Flow 2, the packet of size 250 bytes will be

served since they are less than the deficit counter 500 credits and the channel is available at

that instant of time. DC is updated. Now the scheduling process continues and the pointer

36

moves to Flow 3, the packet size is greater than the deficit counter and the flow is skipped.

DC remains the same. The packet in Flow 4 of size 500 bytes is served successfully and the

DC is updated. Since the pointer has moved through all the given flows, we say it as the

“Completion of one Round''. In this case, it is end of Round 1.

Figure 11 : DRR: End of Round 1

Figure 12 : DRR Transmission Diagram after Round 1

37


Once the channel is available, it triggers the scheduling process, which is the start of next

Round, that is Second Round. The deficit counter is updated with the quantum size again i.e

quantum is added to all the deficit counters of the respective flows. DC= DC(prev) +

Quantum Size. The pointer starts from the Flow 1 again. The DC becomes 390 credits + 500

credits. In this second round, two packets in Flow 1 had arrived. According to the

description, multiple packets can be served from each flow irrespective of packet size as far

as they satisfy the dequeuing criteria. So the packet size of 150 bytes and 200 bytes can be

served successfully with channel available at that instant. After the service, the DC is updated

again.

After the packets are transmitted successfully and the channel becomes available, the pointer

moves to the second flow, the packet of 100 bytes in Flow 2 is served successfully as it is

less than the deficit counter. The packet in Flow 3 which was not served in the previous

round is being served in this round using TX2 because the DC is 1000 credits now. After

transmission, the DC is updated. Once the channel is available, it triggers the scheduling

38

process and the packet of 150 bytes in Flow 4 is served successfully, that is the End of

Round, which is the Round 2.



39


Again when the channel is available, it triggers the scheduling process, which is the start of

the next round, that is the Third Round. The DC is updated with the quantum size again. The

pointer starts from the Flow 1. The packet of 100 bytes in the Flow 1 is served and the

pointer is moved to the next Flow 2. In the Flow 2, the packet size of 50 bytes is served

easily. The packet of 200 bytes in Flow 3 can be served at this instant once the channel is

available. Now the pointer moves to the next flow and no packets arrived in this round means

no packets to be served in this Third round i.e Flow is empty, in that case DC is reset to zero

for fairness issues. Once the channel is available, the next round starts from the Flow 1 and

the process continues till all the flows completely become empty which is sequential.

This example covers all the details such as packet size lesser than the deficit counter with

channel available and channel not available at some instant of time, then packet size greater

than the deficit counter with channel available and not available, the flow being empty in one

particular round.

40



Figure 19 : DRR Transmission Diagram after each Rounds

41

The arrows in the above diagrams shows the end of the packet transmission of packet from

each flow and when the channel is available, it triggers the scheduling process. Once the

packets are served from each flow, the scheduling pointer moves to the next flow which is in

sequence. The Figure- Transmission Diagram after each rounds shows the rounds are not

overlapped. The DRR is carried out in such a way, where the next round starts as the

previous round completes successfully. It means that the delay cannot be avoided in DRR

though packets satisfy all the criteria.

3.3 Summary

DRR is one of the frame-based scheduler and is used mainly because of its efficiency and

simplicity. This chapter described the DRR algorithm and an example to illustrate the process

of DRR scheduling. The next chapter explains the importance of using MCDRR in this

thesis. The algorithm and the example are also explained.

42

Chapter 4

MCDRR

MCDRR is called as Multi-channel Deficit Round Robin scheduler which is a frame-based


sizes by the different flows in the mulit-channel case. The scheduling of packets in switches

and routers has been studied mainly in the context of single-channel communication with

fixed transceivers. We extend the packet scheduling problem to the case of multi-channel

communication with tunable transmitters and fixed receivers as shown in the figure. The

MCDRR scheduling algorithm is the frame-based scheduling algorithm to overcome the unfairness

characteristic caused by the variable packet sizes by the different flows in the multi-channel case. We

presented the paper called “ Multi-Channel Deficit Round Robin Scheduling for Hybrid

TDM/WDM Optical Networks” at FOAN-2012 Russia and journal version to be presented

soon to the IEEE. The paper describes the MCDRR algorithm, an example and the simulation

results [24].

Figure 20 : Block diagram of a hybrid TDM/WDM link based on tunable transmitters and fixed receivers

43

The proposed MCDRR, the multi-channel extension of the DRR, takes into account the

availability of channels and tunable transmitters and overlaps ‘rounds' in scheduling to

efficiently utilize channels and tunable transmitters. To service the queues (i.e., virtual output

queues (VOQs)), we use the simple round-robin algorithm with a quantum of service

assigned to each queue as in the case of DRR. The MCDRR can handle variable packet sizes.

The MCDRR retains the per packet complexity value of O(1) from the round-robin

scheduling and DRR. In the MCDRR scheduling, every flow is allotted with a deficit counter

which is initially set to the desired quantum value. Deficit counter is the amount of credits in

byte, which constantly compares its credits with the packet sizes in the flow. Initially deficit

counter starts at zero. It increments based on the quantum size set. A quantum is the amount

of credits in bytes assigned to each of the flow within the period of one round. It is very

important to decide the quantum size as it might cause fairness issues. A quantum represents

the amount of bytes a queue may require in order to serve the packets in the flow.

MCDRR is a round-robin scheduling algorithm extended by deficit counters for multi-

channels. Since the DRR uses the principle of Round-robin scheduling, we use the same

principle in our MCDRR as well. The round-robin scheduling is used for servicing the

queues with a quantum of service assigned to each flow. The MCDRR scheduler in constant

rotation selects packets from each flow as the tunable transmitters triggers to send out to the

destination. So the packets from all flows that have queued gets a fair chance to transmit to

the particular destinations. The MCDRR maintains an active list or called as service list

which keeps the record of the non-empty queues in a round and to avoid examining the

empty queues. In the MCDRR mechanism, once the tunable transmitter triggers, the

scheduling process starts, the scheduler visits each non-empty queue in the active list and

based on the quantum size, the packets are served. It differs from the traditional round-robin

because if the packet in the previous round was too large compared to the quantum size, then

the remainder from the previous quantum is added to the quantum for the present round, so

the packets can be served successfully in the present round. This can continue for the next

rounds as well. The queues which are not serviced completely in a round are compensated in

the next round. To service one particular flow again, that flow must wait N-1 flows to be

44

serviced again irrespective of its weight. During each round, a flow can transmit at once as

many packets as possible if there is enough quantum for them. For each flow, two variables -

-- i.e., quantum and deficit counter --- are maintained. Deficit counter is the amount of

credits in byte, which constantly compares its credits with the packet sizes in the flow.

Initially deficit counter starts at zero. It increments based on the quantum size set. Quantum

is the amount of credits in bytes assigned to each of the flow within the period of one round.

It is very important to decide the quantum size as it might cause fairness issues. In general if

we expect that the work complexity O(1) per packet for the MCDRR, then the quantum for a

flow should be larger than the maximum packet size from the flow so that at least one packet

per backlogged flow can be served in a round. Here we can observe “overlapping of

Rounds” to avoid delay in MCDRR which is not in the case of DRR. In DRR, the round

robin pointer moving through the set of flows defined using the deficit counters is known as

the period of one round and rounds are sequential without any overlap. In MCDRR, since

tunable transmitters are used, the rounds run in parallel and there is always overlapping of

rounds which reduces the delay.

The MCDRR scheduling algorithm overcame the unfairness characteristic of the both round-robin

algorithm and weighted round-robin algorithm. The MCDRR retains the per packet complexity value

of O(1) from the deficit round-robin scheduling.

Figure 21 : MCDRR Scheduler

In this MCDRR scheduling, every flow is allotted with a deficit counter which is initially set

to the desired quantum value. A quantum is the amount of credits in bytes assigned to each of

45

the flow within the period of one round. It is very important to decide the quantum size as it

might cause fairness issues. A quantum represents the amount of bytes a queue may require

in order to serve the packets in the flow. The deficit counter is incremented by the quantum

size in every round of the scheduler. The deficit counter is reduced by the amount of packets

being served in every visit by the scheduler. The packets are served only if the packet sizes

are lesser than the deficit counter. If the packet sizes are lesser than the deficit counter, then

the packets are backlogged and then they inspect the deficit counter in the next round. In the

next round the deficit counter value gets added to the quantum value, so the packets can be

served successfully. Once the packets are served, the deficit counter is reduced by the amount

of packets being served. If there are no packets to be served in the flow, then the deficit

counter resets to zero. If the quantum assigned to a flow is significantly lesser than the

maximum size of the packets that arrive to the system, this causes delay and burstiness. At

the same time, if the quantum assigned to a flow is significantly higher than the maximum

size of the packets that arrive to the system, this causes a short-time unfairness of the system,

leading to a higher latency bound which might increase gradually. The MCDRR algorithm

also requires the knowledge of the packet size prior to scheduling it, this information could

be available in the packet header of IP packets.

4.1 MCDRR Algorithm

In the MCDRR algorithm, every flow is assigned with a deficit counter which is initially set

to the desired quantum value. In the algorithm for ‘n’ flows, deficit counter is set to zero. The

round-robin pointer moves through all the ‘n’ flows sequentially from the first flow to the nth

flow. Once initialized, we need enqueuing module. Once the packets arrive in the flow, it

gets queued and the deficit counter is set to the quantum for each flow. In the dequeuing

module, the deficit counter is incremented by the quantum size in every round of the

scheduler. The deficit counter is reduced by the amount of packets being served in every visit

by the scheduler. The packets are served only if the packet sizes are lesser than the deficit

counter. If the packet sizes are lesser than the deficit counter, then the packets are backlogged

and then they inspect the deficit counter in the next round. In the next round the deficit

counter value gets added to the quantum value, so the packets can be served successfully.

46

Once the packets are served, the deficit counter is reduced by the amount of packets being

served. If there are no packets to be served in the flow, then the deficit counter resets to zero.

The MCDRR algorithm also requires the knowledge of the packet size prior to scheduling it,

this information could be available in the packet header of IP packets. The below table shows

the algorithm of the MCDRR.

47

Initialization; for i � 0 to W-1 do DC[i] = 0; end Arrival on the arrival of a packet p from channel i; if Enqueue(i, p) is successful then if a transmitter is available then (ptr, ch) � Dequeue(); if pkt ≠ NULL then Send(*ptr, ch); if VOQ[i] is empty then DQ[i] 0; end end end Dequeue; startQueueIndex � (currentQueueIndex + 1)%W; for i � 0 to W - 1 do idx i + startQueueIndex%W; if VOQ[idx] is not empty then DC[idx] DC[idx] + Q[idx]; if numPktsScheduled[idx] == 0 then currentQueueIndex � idx; pos � 0; ptr � &packet(V OQ[idx], pos); repeat DC[idx] � DC[idx] -length(*ptr); numPktsScheduled[idx] + +; pos + +; ptr � &packet(V OQ[idx],pos); if ptr is NULL then Exit the loop; until DQ[idx] length(*ptr); Return (&packet(V OQ[idx], 0), currentQueueIndex); end end end Return NULL;

48

Table 5 : Pseudcode of the MCDRR algorithm

This is the new scheduling algorithm called MCDRR algorithm designed for real time multi-channel systems.

In real systems, the MCDRR scheduler in constant rotation selects packets from each flow as

the tunable transmitters triggers to send out to the destination. So the packets from all flows

that have queued gets a fair chance to transmit to the particular destinations. The MCDRR

maintains an active list or called as service list which keeps the record of the non-empty

queues in a round and to avoid examining the empty queues. In the MCDRR mechanism,

once the tunable transmitter triggers, the scheduling process starts, the scheduler visits each

non-empty queue in the active list and based on the quantum size, the packets are served. It

differs from the traditional round-robin because if the packet in the previous round was too

large compared to the quantum size, then the remainder from the previous quantum is added

to the quantum for the present round, so the packets can be served successfully in the present



again, that flow must wait N-1 flows to be serviced again irrespective of its weight. During

each round, a flow can transmit at once as many packets as possible if there is enough

Departure at the end of transmission on channel i; numPktsScheduled[i] - -; if numPktsScheduled[i] > 0 then ptr � &packet(V OQ[i], 0); Send(*ptr, i); if VOQ[i] is empty then DC[i] > 0; end else (ptr, ch) � Dequeue(); if ptr ≠ NULL then Send(*ptr, ch); if VOQ[ch] is empty then DC[ch] � 0; end end

49


maintained. It is very important to decide the quantum size as it might cause fairness issues.

In general if we expect that the work complexity O(1) per packet for the MCDRR, then the

quantum for a flow should be larger than the maximum packet size from the flow so that at

least one packet per backlogged flow can be served in a round.

4.2 MCDRR Example

Figure 22 : MCDRR: Start of Round 1

Once the packets arrive in the flow, the scheduling process starts. At the start of the First

Round, the tunable transmitter available triggers the scheduling process. The round robin

pointer starts from the first flow initialized. The deficit counter becomes equal to the

quantum size. If the packet size is lesser than the deficit counter and channel is available at

that instant of time, the packet is served. If the channel is not available, the pointer is moved

to the next flow. When the channel becomes available then the packet will be transmitted in

the next round.

50

In the example quantum size is considered to be 500 credits, now both the tunable

transmitters are available, the pointer starts from the Flow 1, the packet of size 110 bytes will

be served since it is less than the deficit counter 500 credits and the channel is available at

that instant of time. By default they choose tunable transmitter 1. After serving, the deficit

counter is updated, that is DC becomes 390 credits. Since the tunable transmitter 2 is also

available, the pointer moves to the Flow 2, the packet of size 250 bytes will be served since

they are less than the deficit counter 500 credits and the channel is available at that instant of

time. DC is updated. Now TX1 becomes available and triggers the scheduling process, the

pointer moves to Flow 3, the packet size is greater than the deficit counter and the flow is

skipped. DC remains the same. Still the TX1 is available, so the packet in Flow 4 of size 500

bytes is served successfully. Since the pointer has moved through all the given flows, we say

it as “Completion of one Round''.

Figure 23 : MCDRR: End of Round 1

51

Figure 24 : MCDRR Transmission Diagram after Round 1


Now TX2 becomes available and triggers the scheduling process, which is the start of next

Round, that is Second Round. The deficit counter is updated with the quantum size again i.e

quantum is added to all the deficit counters of the respective flows. DC= DC(prev) +

Quantum Size. The pointer starts from the Flow 1 again. The DC becomes 390 credits + 500

credits. In this second round, two packets in Flow 1 had arrived. According to our

description, multiple packets can be served from each flow irrespective of packet size as far

52

as they satisfy the dequeuing criteria. So the packet size of 150 bytes and 200 bytes can be

served successfully with channel available at that instant of time. After the service, the DC is

updated again. At some instant, both the tunable transmitters TX1 and TX2 can be available.

In that case by default TX1 will be chosen. In this case TX2 triggers again, the packet of 100

bytes in Flow 2 is served successfully. The packet in Flow 3 which was not served in

previous round is been served in this round using TX2 because the DC is 1000 credits now.

Now TX1 becomes available and the packet of 150 bytes in Flow 4 is served, that is the End

of Round.


53

Figure 27 : MCDRR Transmission Diagram after Round 2


The TX1 becomes available and triggers the scheduling process, which is the start of the next

round, that is the Third Round. The DC is updated with the quantum size again. The pointer

starts from the Flow 1. The packet of 200 bytes in the Flow 1 is served and after some instant

again TX1 becomes available and the packet in Flow 2 is served. Since the packet size is

small, TX1 becomes available at the earliest compared to the TX2. The packet (200 bytes) in

Flow 3 cannot be served at this instant though the tunable transmitter is available because

channel is not available, it means that the packet is still being served from the previous

54

round. So the pointer moves to the next flow and packets arrived in this round means no

packets to be served in this Third round i.e Flow is empty, in that case DC is reset to zero for

fairness issues. So that is the end of this round. Since TX1 is still available, the next round

starts from the Flow 1 and the process continues till all the flows completely become empty

which is sequential.

This example covers all the details such as packet size lesser than the deficit counter with

channel available and channel not available at some instant of time, then packet size greater

than the deficit counter with channel available and not available, flow being empty in one

particular round.


55

Figure 30 : MCDRR Transmission DIagram after Round 3

Figure 31 : MCDRR Transmission Diagram after the Rounds

56

Figure 32 : MCDRR Transmission Phase Diagram

The transmission phase diagram shows the scheduling results at each round. The packets

scheduled and transmitted and the packets which are not scheduled.

Figure 33 : MCDRR Tunable Transmitter Selection Diagram

57

The tunable transmitter selection diagram describes visually the tunable selection process for the data packets from the flows to the destination through channels where ‘F’ means flows, ‘T’ means tunable transmitters and ‘C’ means Channels.

4.3 Summary

The DRR which is also one of the frame-based scheduler for the single channel case is

mainly used in the thesis because of its efficiency and simplicity, it is extended in the thesis

as MCDRR. This chapter described the MCDRR algorithm and an example to illustrate the

process of MCDRR scheduling. Now MCDRR is also a frame-based scheduling algorithm to

overcome the unfairness characteristic caused by the variable packet sizes by the different

flows in the multi-channel case. The scheduling of packets in switches and routers has been

studied mainly in the context of single-channel communication with fixed transceivers. We

extended the packet scheduling problem in the case of multi-channel communication with

tunable transmitters and fixed receivers. The next chapter is about the simulation model and the

results obtained.

58

Chapter 5

SIMULATION

5.1 Methods to Evaluate a Scheduling algorithm

According to [25] there are three main methods to evaluate a Scheduling algorithm.

Method 1 : Deterministic modelling:

The different algorithms are performed and tested against a predetermined workload. Then

the performance results obtained are compared.

Method 2 : Stochastic modelling/Queuing models:

The different algorithms are evaluated analytically in a mathematical way.

Method 3 : Implementation/Simulation:

It is the most versatile method of testing scheduling algorithms with real time data and

conditions.

In this thesis the third method of evaluation methodology will be considered using

OMNet++.

5.2 OMNeT++ Modeller

OMNeT++ is an Objective Modular Network Testbed in C++ which is an object-oriented

modular discrete event simulator [26]. OMNeT++ is a general purpose tool and not

specifically designed for network simulations. It provides a development environment for

modeling of communication networks and it supports tools for all phases of study, including

model design, data collection, simulation, and data analysis.

Components for network simulations are provided by the frameworks. Modules for optical

network simulations are provided by INET frameworks.

The OMNeT++ simulation model consists of modules. Modules communicate by exchanging

messages.The reception of a message is an event. Modules implement application-specific

behaviour. Modules are C++ objects. Connections are defined using the NED (network

topology description) language. Gates are well-defined interfaces.

59

The Omnet++ simulator can be used for modeling:

• Communication protocols

• Computer networks and traffic modeling

• Multi-processor and distributed systems

• Administrative systems

• Other system where the discrete event approach is suitable.

5.2.1 The Simulation System Components

Figure 34 : Architecture of OMNet++ Simulation Programs

The Simulation System Components consists of

• Simulation Kernel

• Library

• User Interface

The simulation Kernel contains the code that manages the simulation and its execution. The

library used is written in C++. The user interface is used in simulation execution, debugging

etc.

60

5.2.2 General Procedure to Implement a Model

The general procedure to implement a model are:

1. Define modules and network topology (.ned)

2. Define messages (.msg)

3. Implement the behaviour of simple modules (.cc)

4. Define the parameters for the simulation (omnetpp.ini)

5. Define metrics to be observed during simulation

4. Compile the project

5. Run the project and the results are recorded as scalar statistics in the finish() method (→

.sca file) or vector file or both.

Figure 35 : Heirarchy of the executable files

61

5.2.3 GUI Features Tkenv is a graphical runtime environment for executing simulations. Tkenv allows us to

understand and follow what is exactly happening inside the network. It gives us the detailed

picture of the execution of the model. Tkenv supports both simulation execution and

debugging.

The most important features are:

• Animation of the message flow.

• Scheduled messages can be monitored as the simulation is in progresses.

• Event-by-event execution is displayed.

• Output scalars and vectors graphical display can be viewed during simulation

executions.

• Results can be displayed as histograms and time-series diagrams.

• Simulations can be restarted

5.3 Simulated Scenario

5.3.1 System Model

We refer the paper [27], Integration of OMNeT++ Hybrid TDM/WDM-PON Models into

INET Framework. The models were initially developed for the study of Stanford University

aCCESS-Hybrid PON (SUCCESS-HPON) architecture into the INET framework and now

we are using it for general Hybrid TDM/WDM-PON Models.

5.3.2 Simulation Set-up

The operation between the OLT and ONT is done using the WDM multiplex and is then

divided by the arrayed waveguide gratings (AWG) demultiplexer into individual

wavelengths, which are then sent to the topology.

62

Figure 36 : MCDRR Graphical NED Editor

The Host’s, ONU’s , AWG, OLT and Server are designed in the graphical NED editor. We

define modules and network topology using “.ned” file.

Figure 37 : MCDRR Top Level Network

In the top level network, we can see the networks, modules and the submodules. We can inspect the network by double clicking on the node.

63

Figure 38 : MCDRR Graphical Tkenv Runtime environment

The MCDRR graphical Tkenv Runtime environment is the main window. It has toolbar and

status bar, timeline, object tree, log area. Mainly we use toolbar and status bar. In toolbar we

can step, run, stop, restart or finish the simulation. In status bar we can find the total number

of messages created during the simulation.

64

5.3.3 Simulation Parameters The Simulation parameters used in our simulation are as follows:

Simulation Parameters

Number of Wavelengths/channel Number of Tunable Transmitters

Quantum Size Line Rate of each channel

Basic Rate Table 6 : Simulation Parameters

5.3.4 Simulation Results

Scenario 1

Throughput for 16 flows for two different sets of conditions for inter-fame times and

frame sizes.

PARAMETERS VALUE

Number of Wavelengths/channel 16 Number of Tunable Transmitters 2

Quantum Size 1518 Bytes Line Rate of each channel 1 Mbps

Basic Rate 100 Mbps Table 7 : Simulation Parameters for Scenario 1

65

Figure 39 : Throughput for 16 flows - Case 1

Figure 40 : Throughput for 16 flows - Case 2

66

To demonstrate the performance of the proposed MCDRR scheduling algorithm, we carried

out simulation experiments with a model for a hybrid TDM/WDM link with tunable

transmitters and fixed receivers shown in Figure (20). We set the number of

wavelengths/channels (W), the line rate of each channel, and the number of tunable

transmitters (M) to 16, 1 Gb/s, and 2, respectively. We assume that the scheduling is done at

the data link layer with Ethernet frames and ignore the tuning time of tunable transmitters in

simulation. Each VOQ can hold up to 1000 frames. We measure the throughput of each flow

at a receiver for 10 mins of simulation time. Figure (39) and (40) show the throughput for 16

flows for two different sets of conditions for inter-fame times and frame sizes.

In Figure (39), the interframe times are exponentially distributed with the averages of 16 µs

and 48 µs for the first flow and the rest of the flows respectively, while the frame sizes are

uniformly distributed between 64 and 1518 bytes for all the flows. In Figure (40), the

interframe times are exponentially distributed with the averages of 16 µs and 32 µs for the

first flow and the rest of the flows, while the frame sizes are fixed to 1000 bytes for the first

flow and 500 bytes for the rest of the flows. For both the cases, the first flow sends frames at

four times the rate of other flows. The combined traffic rates (The inter-frame gap (IFG) of

12 bytes is taken into account) are 2.409 Gb/s for Figure (39) and 2.375 Gb/s for Figure (40),

which slightly overload the link. Raj Jain's fairness index [28] for the results of Figure (39)

and (40) are 0.9999756 and 0.9999998, respectively. For fairness, we use a quantity called

Fairness Measure to determine whether the users are receiving a fair share of system

resources and Raj Jain’s fairness index is given by J (x1, x2,……… xn) = (∑ � )��

��

�.(∑ � )��

Where, n = users, xi = throughput for the ith connection. The result ranges from �

� (worst

case) to 1 (best case), and it is maximum when all users receive the same allocation. So in

our results the fairness index is near to 1 which means all the users are receiving a fair share

of system resources.

67

From the scenario 1 simulation results, we found that the proposed MCDRR scheduling

algorithm provides nearly perfect fairness even with ill-behaved flows for different sets of

conditions for interframe times and frame sizes.

We can test the delay performance for different quantum sizes, different inter-fame times and

frame sizes. Similarly we can test the throughput of the system for different quantum sizes,

different inter-fame times and frame sizes.

Scenario 2

DELAY PERFORMANCE (To investigate the delay performance)

We investigate the delay performance of the system.

PARAMETERS VALUE


Quantum Size 1000B, 1500B, 2000B, 2500B Line Rate of each channel 1 Mbps


68

Figure 41 : Mean Delay for different Quantum Size

End-to-end mean delay remains the same for the different quantum size (1000 Bytes, 1500

Bytes, 2000 Bytes and 2500 Bytes) while the frame sizes are fixed to 1000 Bytes in the

condition 1 and 500 Bytes in the condition 2.

End-to-end mean delay remains the same for the different quantum size (1000 Bytes, 1500

Bytes, 2000 Bytes and 2500 Bytes while the frame sizes are uniformly distributed between

64 and 1518 bytes.

End-to-end mean delay varies only with the interframe times while the frame sizes are fixed

to 1000 Bytes in the condition 1 and 500 Bytes in the condition 2.

End-to-end mean delay varies only with the interframe times while the frame sizes are

uniformly distributed between 64 and 1518 bytes.

69

PARAMETERS VALUE


Quantum Size 1518B Line Rate of each channel 1 Mbps


Figure 42 : Mean Delay for fixed frame size of 500 and 1000 Bytes

End-to-end mean delay varies only with the interframe times while the frame sizes are fixed

to 1000 Bytes in the condition 1 and 500 Bytes in the condition 2.

End-to-end mean delay varies only with the interframe times while the frame sizes are

uniformly distributed between 64 and 1518 bytes.

70

From the scenario 2, End-to-end mean delay varies only with the interframe times while the

frame sizes are uniformly distributed between 64 and 1518 bytes. But for the given

throughput and fairness, delay is negligible.

Scenario 3

THROUGHPUT (To investigate the throughput of the system)

We investigate the throughput of the system. The throughput is maximum when the frame

sizes are fixed to 1000 Bytes in the condition 1 and 500 Bytes in the condition 2. Next, we

investigate setting the inter frame time to 16µsec, 64µsec, 80µsec and 128µsec, the

throughput decreases as the inter-frame time is set high.

PARAMETERS VALUE


Quantum Size 1518B Line Rate of each channel 1 Mbps


71

Figure 43 : Throughput for fixed frame size of 1000 Bytes and 500 Bytes

PARAMETERS VALUE


Quantum Size 1000B, 1500B, 2000B, 2500B Line Rate of each channel 1 Mbps


72

Figure 44 : Throughput for different Quantum Size

From the scenario 3, The throughput is maximum when the frame sizes are fixed to 1000

Bytes in the condition 1 and 500 Bytes in the condition 2. When the inter frame time is set to

16µsec, 64µsec, 80µsec and 128µsec, the throughput decreases as the inter-frame time is set

high. So in our thesis, throughput is maximum when different frame-sizes are considered.

The results obtained cannot be compared with the other related literature because the

algorithms from the literature papers are for single channel case and results obtained in the

project are for multi-channel case.

The simulation results of the investigation of the throughput, fairness and delay are really good for the proposed new multi-channel deficit round robin scheduling for multi-channel case with tunable transmitters are fixed receivers in hybrid TDM/WDM optical networks.

73

5.4 Summary

The simulation results of the investigation of the throughput, fairness and delay are

considered. From the scenario 1 simulation results, we found that the proposed MCDRR

scheduling algorithm provides nearly perfect fairness even with ill-behaved flows for

different sets of conditions for interframe times and frame sizes. So in our results the fairness

index is near to 1 which means all the users are receiving a fair share of system resources.

From the scenario 2, End-to-end mean delay varies only with the interframe times while the

frame sizes are uniformly distributed between 64 and 1518 bytes. But for the given

throughput and fairness, delay is negligible. From the scenario 3, The throughput is

maximum when the frame sizes are fixed to 1000 Bytes in the condition 1 and 500 Bytes in

the condition 2. When the inter frame time is set to 16µsec, 64µsec, 80µsec and 128µsec, the

throughput decreases as the inter-frame time is set high. So in our thesis, throughput is

maximum when different frame-sizes are considered. From the simulation results, the

throughput, fairness and delay are really good for the proposed new multi-channel deficit

round robin scheduling for multi-channel case with tunable transmitters are fixed receivers in

hybrid TDM/WDM optical networks.

74

Chapter 6

Conclusion and Future Research

6.1 Conclusion

Scheduling algorithms is one of the current research issues. Designing a scheduler that is less

complex, more efficient and provides a superior Quality of Service is of great importance to

the next-generation optical access.

In this thesis, a brief description was given for the Deficit Round Robin scheduling. The

DRR scheduling algorithm has been described in depth. After that we attempted to enhance

the throughput of the system with regard to the multi-channel system. Considering the system

complexity, the DRR was mainly chosen to be enhanced since it provides O(1) per packet

complexity. This can assure that the implementation of this scheduler should not be complex

or expensive.

In this thesis we have proposed and investigated the performance of the MCDRR scheduling

algorithm for a multi-channel link with tunable transmitters and fixed receivers, which is

based on the DRR, the well-known single-channel scheduling algorithm. In extending the

DRR to the case of multi-channel scheduling, we try to efficiently utilize the network

resources (i.e., channels and tunable transmitters) by overlapping rounds, while maintaining

its low complexity i.e. O(1). The nearly perfect fairness provided by the MCDRR has been

demonstrated through simulation experiments.

The simulation results of the investigation of the throughput, fairness and delay are really good for the proposed new multi-channel deficit round robin scheduling for multi-channel case with tunable transmitters are fixed receivers in hybrid TDM/WDM optical networks.

75

The low complexity offered by the scheduling system allows a simple and cheap

implementation in hardware. Finally, with all the mentioned advantages, the MCDRR

scheduling framework is a practical solution for the next-generation optical access. However,

further simulations must be carried out in order to assure that the scheduler is also able to

provide the QoS constraints with a larger number of ONU's.

Unfortunately, for a large number of ONU's, we were not able to carry out in our thesis,

mainly because a normal Personal Computer (PC) and Laptop was used for the purpose of

simulating the scheduler. For a large number of ONU’s, we need to use cluster to obtain the

best results. Nevertheless, we attempted to simulate a 30 ONU’s which took more than 6

hours to finish, hence the simulation was aborted for the safety of the system.

6.2 Future Works

A detailed study must be carried out about the end-to-end performance of a network of

MCDRR. We can try to achieve 100% throughput by providing tight bounds. The deficit

counter can be set to negative scale by borrowing credits when the channel is available with

tunable transmitter triggering the scheduling process. This can allow us to minimize the delay

bounds. In the basic DRR and in our MCDRR thesis deficit counter never becomes negative.

Establishing mathematical bounds for the fairness and latency of the MCDRR is an important

future work and also the comparison with other multi-channel scheduling algorithms can be

carried out.

76

Bibliography

[1] Stephens, Donpaul, Zhang, Hui and J. C. Bennett, "http://www.cs.cmu.edu/~hzhang/papers/JSAC99.pdf," [Online].

[2] K. S. Kim, D. Gutierrez, F.-T. An and L. G. Kazovsky, "Design and performance analysis of scheduling algorithms for WDM-PON under SUCCESS-HPON architecture," vol. 23, no. 11, Nov. 2005.

[3] G.Varghese and M. Shreedhar, "Efficient fair queueing using deficit round robin," vol. 25, no. 4, 1995.

[4] P. E. McKenney, "Stochastic fairness queueing," vol. 2, Jan 1991.

[5] C. Wang, W. Wei, W. Zhang, H. Jiang, C. Qiao and T. Wang, "Optimal wavelength scheduling for hybrid WDM/TDM passive optical networks," vol. 3, no. 6, Jun 2011.

[6] B.O''zden and J. M. Blanquer, "Fair queuing for aggregated multiple links," Aug 2011.

[7] Y.Jiang and H. Xiao, "Analysis of multi-server round robin scheduling disciplines," Vols. E87-B, no. 12, Dec-2004.

[8] K.S.Kim, "iat-hnrl.swan.ac.uk," [Online].

[9] A. Banerjee, G. Kramer, Y. Ye, S. Dixit and B. Mukherjee, "Advances in Passive Optical Networks (PONS)," Emerging Optical Network Technologies, pp. 51-73, 2005.

[10] D. Ahamed, "A NOVEL VIEW ON PASSIVE OPTICAL NETWORK STRATEGIES IN THE COMPUTER COMMUNICATION," International Journal of Engineering Science and Technology, vol. 2, pp. 3597-3602, 2010.

[11] J. CHEN, "Design, Analysis and Simulation of Optical Access and Wide-area Networks," Sweden, 2009.

[12] S. J. Park, Hybrid WDM/TDM-PON Presentation, 2008.

[13] "www.ing-steen.se," [Online].

[14] l. n. R.Jain, A Survey of Scheduling and queue management, University of Ohio, 2007.

77

[15] l. n. Mark Handley, Scheduling and Queue Management, University of London, 2006.

[16] J. A. Flores, "Contention Period Management and a Price-Based Scheduling Framework for IEEE 802.16 Networks".

[17] T. J. Al-Khasib, "Mini Round Robin: An Enhanced Frame-Based Scheduling Algorithm for Multimedia Networks".

[18] J.Nagle, "On packet Switches with infinite storage," 1985.

[19] S. Baban, "Design and Implementation of a Scheduling Algorithm for the IEEE 802.16e (Mobile WiMAX) Network".

[20] "http://en.wikipedia.org/wiki/Weighted_fair_queuing," [Online].

[21] L. Zhang, "VirtualClock: A New Traffic Control Algorithm for Packet Switching Networks".

[22] A. Demers, S. Keshav and S. Shenker, "Analysis and Simulation of a fair queuing algorithm".

[23] S. S. a. C. C. M. Katevenis, "Weighted round-robin cell multiplexing in a general-purpose

ATM switch chip," p. 1265-1279, 1991.

[24] M. Sathiyanarayanan and K. S. Kim, "http://iat-hnrl.swan.ac.uk/~kks/publications/mcdrr_foan2012.pdf," 2012. [Online].

[25] J. Kubiatowicz, "http://www.cs.berkeley.edu/~kubitron/courses/cs162-F06/Lectures/lec10-scheduling.pdf," 2006. [Online].

[26] "www.omnetpp.org," OMNet++ User Guide. [Online].

[27] K.S.Kim, "Integration of OMNeT++ Hybrid TDM/WDM-PON Models into INET Framework".

[28] R.Jain, D.Chiu and W.Have, "A quantitative measure of fairness and discrimination for resource allocation in shared computer systems," Vols. DEC-TR-301, Sep. 1984..

[29] J. A. C. a. M. Lin, "A theory of multi-channel schedulers for quality of service," vol. 12, no. 1-2, 2002.

[30] J. S. V. a. M. D. Logothetis, "Passive Optical Network Configurations-Performance

78

Analysis".

Multi-channel Scheduling Algorithm (PG thesis)

Documents