CS2060 HIGH SPEED NETWORKS ECE – VII SEM MAHALAKSHMI ENGINEERING COLLEGE TIRUCHIRAPALLI – 621 213 UNIT II CONGESTION AND TRAFFIC MANAGEMENT Part – A (2 Marks) 1. Difference between multiserver queue and multiple single server ? Multiserver queues Multiple single sever queues 1.It has less waiting time 1. Waiting time is more since there are many single servers. 2.It has infinite populations and 2.Population and queue size is less infinite queue size and have significant impact on performance 2. Define Kendall's notation? The notation is given by X/Y/N where X refers to the distribution of interarrival times Y refers to the distribution of service times N refers to the number of server. 3. Define mean residence time? The average number of item residence in a system ,including the item being served(if any) and the item waiting(if any),is r ; and the average time that an item spends in the system ,waiting and being served , is T r ; referred as mean residence time. 4. List some of the common distributions made? G, general distribution of interarrival times or service times GI, general distribution of interarrival times with restriction that interarrival times are independent M, negative exponential distribution D, deterministic arrivals or fixed – length service. 5. Why Queuing Analysis? Option 1: Will wait and see what happens Option 2: Analyst may take the position impossible to project future demand and degree of certainty Option 3: Use of an Analytic model. Option 4: Use of Simulation model. 6. List some of the model characteristics?
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CS2060 HIGH SPEED NETWORKS ECE – VII SEM
MAHALAKSHMI ENGINEERING COLLEGE
TIRUCHIRAPALLI – 621 213
UNIT II
CONGESTION AND TRAFFIC MANAGEMENT
Part – A (2 Marks)
1. Difference between multiserver queue and multiple single server ?
Multiserver queues Multiple single sever queues
1.It has less waiting time 1. Waiting time is more since there
are many single servers.
2.It has infinite populations and 2.Population and queue size is less
infinite queue size and have significant impact on
performance
2. Define Kendall's notation?
The notation is given by X/Y/N where X refers to the distribution of interarrival times
Y refers to the distribution of service times
N refers to the number of server.
3. Define mean residence time? The average number of item residence in a system ,including the item being
served(if any) and the item waiting(if any),is r ; and the average time that an item spends in the system ,waiting and being served , is Tr; referred as mean residence time.
4. List some of the common distributions made? G, general distribution of interarrival times or service times
GI, general distribution of interarrival times with restriction that interarrival times are independent
M, negative exponential distribution
D, deterministic arrivals or fixed – length service.
5. Why Queuing Analysis? Option 1: Will wait and see what happens
Option 2: Analyst may take the position impossible to project future
demand and degree of certainty
Option 3: Use of an Analytic model.
Option 4: Use of Simulation model.
6. List some of the model characteristics?
CS2060 HIGH SPEED NETWORKS ECE – VII SEM
The characteristics are Item population
Queue size
Dispatching discipline.
7. List the assumption made on input and output? The assumptions made on input are, Arrival time
Service time
Number of servers
The assumptions made on output are, Items waiting
Waiting time
Items queued
Residence time.
8. What is the objective of congestion control? The objective of congestion control is to maintain the number of packets within the network
below the level at which performance falls off dramatically.
9. Difference between implicit congestion and explicit congestion?
Implicit congestion Explicit congestion
1.It deals with discard and delay 1.It deals with binary rate and
credit
2.Mainly used for connectionless or 2.It takes place in two direction
datagram configurations such as IP forward and backward
based internet
10. Define Backpressure? Backpressure is of limited use they can be applied in logical connections used for
connection oriented network, X.25 based packet switching network.
11. Define Choke packet? Choke packet is control packet generated at a congested node and transmitted back
to a source node to restrict traffic flow. Ex ICMP (Internet Control Message Protocol) and Source quench.
12. List the congestion control mechanism in packet switching networks? Send a control packet from congested node to some or al source nodes
Rely on routing information
Make use of an end-to-end probe packet
Allow a packet switching nodes to add congestion information to packets as they go by
13. List the objectives of frame relay congestion control Minimize frame discard
Create minimal network additional traffic
Maintain, with high probability and minimum variance
CS2060 HIGH SPEED NETWORKS ECE – VII SEM
Be simple to implement
Distribute network resource fairly among users
14. What is Discard Strategy? Discard Strategy deals with the most fundamental response to congestion; when
congestion becomes severe enough, the network is forced to discard frames
15. What is Congestion Avoidance? Congestion Avoidance is used at onset of congestion to minimize the effect on the
network. Explicit signaling mechanism from the network that will trigger the congestion avoidance .
16. What is Congestion recovery? Congestion recovery procedures are used to prevent network collapse in the face of
severe congestion. These procedure are typically initiated when the network begun to drop frames due to congestion. Ex LAPF or TCP
17. What is committed information rate (CIR)? Committed information rate is a rate, in bits per second that the network agrees to
support for a particular frame-mode connection. It is vulnerable to discard in the event of congestion
18. Define BECN?
Backward explicit congestion notification (BECN) notifies the user that congestion avoidance procedures should be initiated where applicable for traffic in the opposite direction of the received frame. It indicates that frames user transmits on this logical connection may encounter congested resources.
19. Define FECN? Forward explicit congestion notification (FECN) notifies the user that congestion
avoidance procedures should be initiated where applicable for traffic in the same direction of the received frame. It indicates that frames user transmits on this logical connection, has encountered congested resources.
20. What is network response and user response? Network response is necessary for frame handler to monitor its queuing behavior. Here
the choice is based on end user. User response is determined by the receipt of BECN or FECN .The simplest procedure
is to use BECN because other one is complex. 21. Define switch?
A switch is simply a box with some number of ports that different devices such as
workstations, routers and other switches attach to.
22. What are the techniques available to accomplish switch path control?
1. address learning
2. Spanning tree
3. Broadcast and discover
CS2060 HIGH SPEED NETWORKS ECE – VII SEM
4.link state routing
5. explicit signaling.
23. Define VLAN?
VLAN is a broadcast domain whose members use LAN switching to communicate as if they
shared the same physical segment.
24. What are the uses of VLAN?
VLAN are useful for administrative, security and broadcast control.
25. What are the two internal forwarding techniques used in LAN switch? 1. Cut through 2. Store and forward.
26. What is cut through forwarding? A switch begins to forward the packet as soon as the destination address is examined and
verified. The forwarding of the first path of the packet can begin even as the remainder of
the packet is being read into the input port switch buffers.
27. What are the advantages of using twisted pair star LAN?
1. Two wire system is susceptible to crosstalk and noise
2. A twisted pair can pass relatively wide range of frequencies.
3. Attenuation is in the range of 20db/mile at 500 khz
4. Transmission is not affected by interference.
28. What are the properties of VC connections? Each VC is identified by a VC identifier. Cells belonging to the single message follow the
same VC. Cells remain in the original order till they reach the destination.
1 Congestion statistics for this model Congestion statistics for this model
are:M/M/1, M/D/1, M/G/1 is M/M/N.
2 Arrival rate = λ Arrival rate for each server = λ/N
46. What is meant by implicit congestion signaling?
When network congestion occurs, packets get discard and acknowledgement will be
delayed. As a result, sources understand that there is congestion implicitly. Here, users are notified
about congestion indirectly.
47. What is meant by explicit congestion signaling? In this method, congestion is indicated directly by a notification. The notification may be in
backward or forward direction.
48. Define committed burst size (BC)
It is defined as the maximum number of bits in a predefined period of time that the network is committed to transfer with out discarding any frames. 49. Define committed information rate (CIR)
CIR is a rate in bps that a network agrees to support for a particular frame mode
connection. Any data transmitted in excess of CIR is vulnerable to discard in event of
congestion. CIR < Access rate
50. Define access rate.
For every connection in frame relay network, an access rate (bps) is defined. The
access rate actually depends on bandwidth of channel connecting user to network.
51. Write Little’s formula.
Little’s formula is defined as the product of item arrive at a rate of λ, and Served time of items Tr (or) product of item arrive at a rate of λ and waiting time of an items Tw.
It is given as, r = λ Tr (or) w = λ Tw
52. List out the model characteristics of queuing models.
a) Item population.
b) Queue size
c) Dispatching discipline.
53. List out the fundamental task of a queuing analysis.
Queuing analysis as the following as a input information.
CS2060 HIGH SPEED NETWORKS ECE – VII SEM
a) Arrival rate
b) Service rate
c) Number of servers.
Provide as output information concerning:
a) Items waiting
b) Waiting time
c) Items queued
d) Residence time. 54. State Kendall’s notation.
Kendall’s notation is X/Y/N, where X refers to the distribution of the interarrival
times, Y refers to the distribution of service times, and N refers to the number of servers.
The most common distributions are denoted as follows:
G = General distribution of interarrival times or service times
GI = General distribution of interarrival times with the restriction that
Interarrival times are independent.
M = Negative exponential distribution
D = Deterministic arrivals or fixed-length service.
Thus, M/M/1 refers to a single-server queuing model with poisson arrivals
(Exponential interarrival times) and exponential service times.
55. List out the assumptions for single server queues.
a. Poisson arrival rate.
b. Dispatching discipline does not give preference to items based on service times
c. Formulas for standard deviation assume first-in, first-out dispatching.
d. No items are discarded from the queue.
56. List out the assumptions for Multiserver queues.
a. Poisson arrival rate.
b. Exponential service times
c. All servers equally loaded.
d. All servers have same mean service time.
e. First-in, first-out dispatching.
CS2060 HIGH SPEED NETWORKS ECE – VII SEM
f. No items are discarded from the queue.
57. State Jackson’s theorem. Jackson’s theorem can be used to analyse a network of queues. The theorem is based
on three assumptions:
1. The queuing network consists of m nodes, each of which provides an independent
exponential service.
2. Items arriving from outside the system to any one of the nodes arrive with a poisson rate.
3. Once served at a node, an item goes (immediately) to one of the other nodes with a fixed
probability, or out of the system.
58. Define Arrival rate and service rate.
Arrival Rate: The rate at which data enters into a queuing system i.e., inter arrival
rate. It is indicated as λ.
Service Rate: The rate at which data leaves the queuing system i.e., service rate.
It is indicated as μ.
59. What is meant by congestion avoidance and congestion recovery technique?
Congestion Avoidance: It is the procedure used at beginning stage of congestion to minimize its
effort. This procedure initiated prior to or at point A. This procedure
prevent congestion from progressing to point B.
Congestion Recovery: This procedure operates around at point B and within region of severe
congestion to prevent network collapse. Here dropped frames are reported to higher layer and
further packet delivery is stopped to recover from congestion.
60. what is the role of de in frame relay? This bit it indicates frame priority. The DE can taken value of 0 or 1. DE=0 means frame
network element; it can be discard the frame during periods of congestion. DE=1, for generally
considered as high priority frames.
61. How does frame relay report congestion? When the particular portion of the network is heavily congestion. It is Desirable to route packets
around rather than through the area of congestion.
62. Define Qos. Refers to the properties of a network that contribute to the degree of satisfaction that user
perceive, relative to the network performance four service
categories are typically under this term capacity, data rate, latency, delay & traffic losses.
63. Define committed burst size The max. amount data that the network agrees to transfer under normal Condition over a
measurement interval T, these data may or may not be contiguous.
64. Define excess burst size The max amount of data in excess of BC that the network will attempt to transfer under
normal condition over a measurement interval T. these data are uncommitted.
CS2060 HIGH SPEED NETWORKS ECE – VII SEM
Part – B (16 Marks)
1.Explain queuing analysis and the different queuing models.
Queing analysis
In queueing theory, a queueing model is used to approximate a real queueing situation
or system, so the queueing behaviour can be analysed mathematically. Queueing
models allow a number of useful steady state performance measures to be determined,
including:
the average number in the queue, or the system, the average time spent in the queue, or
the system,
the statistical distribution of those numbers or times, the probability the queue is full,
or empty, and
the probability of finding the system in a particular state.
These performance measures are important as issues or problems caused by queueing
situations are often related to customer dissatisfaction with service or may be the root
cause of economic losses in a business. Analysis of the relevant queueing models
allows the cause of queueing issues to be identified and the impact of any changes that
might be wanted to be assessed.
Notation Queueing models can be represented using Kendall's notation:
A/B/S/K/N/Disc
where:
A is the interarrival time distribution
B is the service time distribution
S is the number of servers
K is the system capacity
N is the calling population
Disc is the service discipline assumed
Some standard notation for distributions (A or B) are:
M for a Markovian (exponential) distribution
Eκ for an Erlang distribution with κ phases
D for Deterministic (constant)
G for General distribution
PH for a Phase-type distribution
Models
Construction and analysis Queueing models are generally constructed to represent the steady state of a queueing
CS2060 HIGH SPEED NETWORKS ECE – VII SEM
system, that is, the typical, long run or average state of the system. As a consequence,
these are stochastic models that represent the probability that a queueing system will be
found in a particular configuration or state.
A general procedure for constructing and analysing such queueing models is:
1. Identify the parameters of the system, such as the arrival rate, service time, Queue
capacity, and perhaps draw a diagram of the system.
2. Identify the system states. (A state will generally represent the integer number of
customers, people, jobs, calls, messages, etc. in the system and may or may not be
limited.)
3. Draw a state transition diagram that represents the possible system states and identify the
rates to enter and leave each state. This diagram is a representation of a Markov chain.
4. Because the state transition diagram represents the steady state situation between state
there is a balanced flow between states so the probabilities of being in adjacent states can
be related mathematically in terms of the arrival and service rates and state probabilities.
5. Express all the state probabilities in terms of the empty state probability, using the inter-
state transition relationships.
6. Determine the empty state probability by using the fact that all state probabilities always
sum to 1.
Whereas specific problems that have small finite state models are often able to be
analysed numerically, analysis of more general models, using calculus, yields useful
formulae that can be applied to whole classes of problems.
Single-server queue Single-server queues are, perhaps, the most commonly encountered queueing situation
in real life. One encounters a queue with a single server in many situations, including
business (e.g. sales clerk), industry (e.g. a production line), transport (e.g. a bus, a taxi
rank, an intersection), telecommunications (e.g. Telephone line), computing (e.g.
processor sharing). Even where there are multiple servers handling the situation it is
possible to consider each server individually as part of the larger system, in many
cases. (e.g A supermarket checkout has several single server queues that the customer
can select from.) Consequently, being able to model and analyse a single server
queue's behaviour is a particularly useful thing to do.
Poisson arrivals and service M/M/1/∞/∞ represents a single server that has unlimited queue capacity and infinite
calling population, both arrivals and service are Poisson (or random) processes,
meaning the statistical distribution of both the inter-arrival times and the service times
follow the exponential distribution. Because of the mathematical nature of the
exponential distribution, a number of quite simple relationships are able to be derived
for several performance measures based on knowing the arrival rate and service rate.
This is fortunate because, an M/M/1 queuing model can be used to approximate many
queuing situations.
CS2060 HIGH SPEED NETWORKS ECE – VII SEM
Poisson arrivals and general service M/G/1/∞/∞ represents a single server that has unlimited queue capacity and infinite
calling population, while the arrival is still Poisson process, meaning the statistical
distribution of the inter-arrival times still follow the exponential distribution, the
distribution of the service time does not. The distribution of the service time may
follow any general statistical distribution, not just exponential. Relationships are still
able to be derived for a (limited) number of performance measures if one knows the
arrival rate and the mean and variance of the service rate. However the derivations a
generally more complex.
A number of special cases of M/G/1 provide specific solutions that give broad insights
into the best model to choose for specific queueing situations because they permit the
comparison of those solutions to the performance of an M/M/1 model.
Multiple-servers queue Multiple (identical)-servers queue situations are frequently encountered in
telecommunications or a customer service environment. When modelling these
situations care is needed to ensure that it is a multiple servers queue, not a network of
single server queues, because results may differ depending on how the queuing model
behaves. One observational insight provided by comparing queuing models is that a single queue with multiple servers performs better than each server having their own queue and that a single large pool of servers performs better than two or more smaller pools, even though there are the same total number of servers in the system.
One simple example to prove the above fact is as follows: Consider a system having 8 input lines, single queue and 8 servers.The output line has a capacity of 64 kbit/s. Considering the arrival rate at each input as 2 packets/s. So, the total arrival rate is 16 packets/s. With an average of 2000 bits per packet, the service rate is 64 kbit/s/2000b = 32 packets/s. Hence, the average response time of the system is 1/(μ-λ) = 1/(32-16) = 0.0667 sec. Now, consider a second system with 8 queues, one for each server. Each of the 8 output lines has a capacity of 8 kbit/s. The calculation yields the response time as 1/(μ-λ) = 1/(4-2) = 0.5 sec. And the average waiting time in the queue in the first case is ρ/(1-ρ)μ = 0.25, while in the second case is 0.03125.
Infinitely many servers While never exactly encountered in reality, an infinite-servers (e.g. M/M/∞) model is a convenient theoretical model for situations that involve storage or delay, such as parking lots, warehouses and even atomic transitions. In these models there is no queue, as such, instead each arriving customer receives service. When viewed from the outside, the model appears to delay or store each customer for some time.
Queueing System Classification
With Little's Theorem, we have developed some basic understanding of a queueing system. To further our understanding we will have to dig deeper into characteristics of a queueing system that impact its performance. For example, queueing requirements of a restaurant will depend upon factors like:
CS2060 HIGH SPEED NETWORKS ECE – VII SEM
How do customers arrive in the restaurant? Are customer arrivals more during lunch and dinner time (a regular restaurant)? Or is the customer traffic more uniformly distributed (a cafe)? How much time do customers spend in the restaurant? Do customers typically leave the restaurant in a fixed amount of time? Does the customer service time vary with the type of customer? How many tables does the restaurant have for servicing customers?
The above three points correspond to the most important characteristics of a queueing system. They are explained below:
Arrival Process The probability density distribution that determines the customer arrivals in the system.
In a messaging system, this refers to the message arrival probability distribution.
Service Process The probability density distribution that determines the customer service times in the
system.
In a messaging system, this refers to the message transmission time distribution. Since
message transmission is directly proportional to the length of the message, this
parameter indirectly refers to the message length distribution.
Number of Servers Number of servers available to service the customers.
In a messaging system, this refers to the number of links between the source and
destination nodes. Based on the above characteristics, queueing systems can be classified by the following convention:
A/S/n Where A is the arrival process, S is the service process and n is the number of servers. A and S are can be any of the following: M (Markov) Exponential probability density
D (Deterministic) All customers have the same value
G (General) Any arbitrary probability distribution
Examples of queueing systems that can be defined with this convention are:
M/M/1: This is the simplest queueing system to analyze. Here the arrival and service
time are negative exponentially distributed (poisson process). The system consists of
only one server. This queueing system can be applied to a wide variety of problems as
any system with a very large number of independent customers can be approximated
as a Poisson process. Using a Poisson process for service time however is not
applicable in many applications and is only a crude approximation. Refer to M/M/1
Queueing System for details.
CS2060 HIGH SPEED NETWORKS ECE – VII SEM
M/D/n: Here the arrival process is poisson and the service time distribution is
deterministic. The system has n servers. (e.g. a ticket booking counter with n
cashiers.) Here the service time can be assumed to be same for all customers)
G/G/n: This is the most general queueing system where the arrival and service time
processes are both arbitrary. The system has n servers. No analytical solution is known
for this queueing system.
Markovian arrival processes
In queuing theory, Markovian arrival processes are used to model the arrival customers to queue. Some of the most common include the Poisson process, Markovian arrival process and the batch Markovian arrival process.
Markovian arrival processes has two processes. A continuous-time Markov process j(t), a Markov process which is generated by a generator or rate matrix, Q. The
other process is a counting process N(t), which has state space
(where is the set of all natural numbers). N(t) increases every time there is a
transition in j(t) which marked.
Poisson process The Poisson arrival process or Poisson process counts the number of arrivals, each of which has a exponentially distributed time between arrival. In the most general case this can be represented by the rate matrix,
Markov arrival process The Markov arrival process (MAP) is a generalisation of the Poisson process by having non-exponential distribution sojourn between arrivals. The homogeneous case has rate matrix,
Little's law
In queueing theory, Little's result, theorem, lemma, or law says:
The average number of customers in a stable system (over some time interval), N, is
equal to their average arrival rate, λ, multiplied by their average time in the system, T,
or:
Although it looks intuitively reasonable, it's a quite remarkable result, as it implies that this behavior is entirely independent of any of the detailed probability distributions involved, and hence requires no assumptions about the schedule according to which customers arrive or are serviced, or whether they are served in the order in which they arrive.
It is also a comparatively recent result - it was first proved by John Little, an Institute Professor and the Chair of Management Science at the MIT Sloan School of Management, in 1961.
Handily his result applies to any system, and particularly, it applies to systems within
CS2060 HIGH SPEED NETWORKS ECE – VII SEM
systems. So in a bank, the queue might be one subsystem, and each of the tellers another subsystem, and Little's result could be applied to each one, as well as the whole thing. The only requirement is that the system is stable -- it can't be in some transition state such as just starting up or just shutting down.
Mathematical formalization of Little's theorem
Let α(t) be to some system in the interval [0, t]. Let β(t) be the number of departures from the same system in the interval [0, t]. Both α(t) and β(t) are integer valued
increasing functions by their definition. Let Tt be the mean time spent in the system (during the interval [0, t]) for all the customers who were in the system during the
interval [0, t]. Let Nt be the mean number of customers in the system over the duration of the interval [0, t].
If the following limits exist,
and, further, if λ = δ then Little's theorem holds, the limit exists and is given by Little's theorem,
CS2060 HIGH SPEED NETWORKS ECE – VII SEM
2. Explain the effects of congestion and the different congestion control
methodologies in packet switching networks.
Ideal Performance
Effects of Congestion
‘
CS2060 HIGH SPEED NETWORKS ECE – VII SEM
Congestion-Control Mechanisms
Backpressure
– Request from destination to source to reduce rate
– Useful only on a logical connection basis
– Requires hop-by-hop flow control mechanism
Policing
– Measuring and restricting packets as they enter the network
Choke packet
– Specific message back to source
– E.g., ICMP Source Quench
Implicit congestion signaling
– Source detects congestion from transmission delays and lost packets and reduces flow Explicit congestion signaling
Frame Relay reduces network overhead by implementing simple congestion-notification
mechanisms rather than explicit, per-virtual-circuit flow control. Frame Relay typically is
implemented on reliable network media, so data integrity is not sacrificed because flow
control can be left to higher-layer protocols. Frame Relay implements two congestion-