Teletraffic Lessons for the Future Internet Presenter: Moshe Zukerman ARC Centre for Ultra-Broadband Information Networks Electrical and Electronic Engineering.

Teletraffic Lessons for the Future Internet

Presenter: Moshe Zukerman ARC Centre for Ultra-Broadband

Information NetworksElectrical and Electronic Engineering

Dept., The University of Melbourne

Outline

• My Research• Background: Evolution, services, network design

optimization, cost and carbon cost, Internet growth, link utilization, Internet congestion control

• Optical Internet model and design options• Example of an optical network performance

analysis problem • Results• Conclusion

My research

• Queueing theory – bursty traffic – link dimensioning

• Optical network performance and design

• Medium access control – protocol performance analysis and enhancement

• Other topics: TCP, Wireless/Mobile networks

New services - research directions

• Internet of things (mice)– make it work from a traffic point of view

– light weight protocols

– traffic implications - network dimensioning

• HD-IPTV, Virtual reality (elephants)– Streaming vs download

– network dimensioning

– multi-service internet

– traffic shaping/policing

• Others, in between, e.g. wideband voice

Moore’s law and Internet equivalence

• Moore's Law: power and speed of computers will double every 18-24 months.

• Internet backbone traffic grew from one Tbit/sec in 1990 to 3,000 Tbit/sec in 1997.

• Number of Internet hosts more than doubled every year between 1980-2000.

Trend Doubling Period

semiconductor performance 18 months (Moore’s)

computer performance/$ 21 months (Roberts’)

communications bit/$ before 95 79 months

communications bit/$ with DWDM 12 months

max. Internet trunk speed in service 22 months

Internet traffic growth 69-82 21 months

Int. traffic growth 83 (TCP/IP) - 97 9 months

Internet traffic growth 97-2000 6 months (bubble)

router/switch max. speed pre 97 22 months

router/switch max. speed post 97 6 months

Source: L. G. Roberts, Computer, January 2000

World Internet Statistics

World Population: 6,676,120,288

Number of Internet Users 1,407,724,920

Penetration 21.1%

%Growth between 2000-2008 290.0%

Source: www.internetworldstats.com

Design Optimization

Aim: To provide services at

Minimal Cost

Subject to:

Meeting required quality of service

And other practical constraints (including availability of power)

Google Data Center

Competing with Microsoft on dominance but the practical constraint is power

The Dalles, Oregon

Source: LA Times (14-6-2006) By JOHN MARKOFF and SAUL HANSELL

“Hiding in Plain Sight, Google Seeks More Power”

Power consumption ~200 MW (RS Tucker)

Google Data Centre (cont.)

Source: www.techbanyan.com/archives/140

Network Power Distribution

Reference: “Data Centers Network Power Density Challenges” By Alex Vukovic, Ph.D., P.Eng. ASHRAE Journal, (Vol. 47, No. 4, April 2005).

•Switching and Routing 34%•Regeneration 27%•Processing 22%•Storage 10%•Transport 7%

Internet Power Usage

TOTAL Population: 6,676,120,288

Number of Internet Users 1,407,724,920

Penetration 21.1 %

%Growth between 2000-2008 290.0 %

Source: www.internetworldstats.com

Internet Power Usage (cont.)

Today Internet (excluding PCs, customersequipment, mobile terminals etc.) uses ~1% of total world electricity usage.

If 2 Billion people have broadband access(1Mb/s) then ~5%.

If 2 Billion people have broadband access (10 Mb/s) then ~50%.

Source: R.S Tucker, “A Green Internet”

May 2007, CUBIN Seminar, The University of Melbourne

Design Optimization

Aim: To provide services at Minimal Cost (do not forget to consider alsodirect energy $ + indirect carbon $)Subject to:Meeting required quality of service And other practical constraints (including availability of power)

The other aspect is utilization (traditional teletraffic concept)

Link Utilization

Utilization = Proportion of time the link is Busy.

measure for system efficiency and profit for telecom providers.

The traditional teletraffic aim has been to maximize utilization subject to meeting queuing delay (and loss) requirements.

It’s all about using the scraps!

Bursty traffic = low utilization and bad service

Smooth traffic = high utilization and good service

time

time

A Simple model

P(X > C) < Quality measure

time

CX

E[X] = 150 Mbit/sec

frequency

bit rate

C = 1000 Mbit/sec

Bursty traffic

many standard deviations

E[X] = 850 Mbit/sec

frequency

Bit rate

C = 1000 Mbit/sec

Smooth trafficGaussian many sources

Chebyshev’s Inequality

P(|X-E[X]| > S) ≤ Var(X)/S2

Internet end-to-end protocols

Transmission Control Protocol (TCP) – Non-Real Time Traffic

User Datagram Protocol (UDP)for Real-Time Traffic

Towards All-Optical Internet“Old” Electronic Internet:

Capacity expensive, buffering cheap

Introduction of DWDM makes capacity cheap

Electronic Bottleneck: O-E-O

but maybe the bottleneck is not this E but the other one (Energy, or P = Power)

Future All-Optical Internet (?):

Link capacity plentiful, buffering painful (cost, power, space) and also wavelength conversion (espacially for OPS) is costly

An Internet Model

Access

Optical Core

Bufferless Optical Burst/Packet Switching

• Packet Switching but without buffers;• Packets cannot be delayed along the way.• Delay is possible at the edges. • Some multiplexing is possible.• Between packet switching and circuit

switching.• How efficient can it be?

Optical switch

trunktrunk

Optical switch without buffers and without wavelengths conversion

trunktrunk links

A trunk can be composed of 10 cablesEach cable comprises 100 wavelengthsSo a trunk will have 1000 links

Trunks and Links

Let us focus on one output trunk

Markov chain analysis is a common approach to evaluate loss probability

Models - no buffers many Pipes

M / M / k / k

Arrivalprocess

Servicedistribution

Number of servers

Buffer placesincluding at servers

M / M / infinity A = arrival rate (λ) / service rate (µ)

A = arrivals per service time

M/M/k/k was developed for telephony

“We are sorry; all circuits are busy now; will you try your call again later”.

Old message from a local exchange of:

Blocking probability for traffic A and n channels

Erlang B Formula gives the the probability that a call is blocked under the M/M/k/k model.

Recursion for Erlang B Formula:

E0(A)=1

A k % Utilization

10,000 10272 0.97

1,000 1100 0.91

100 137 0.73

10 24 0.42

Multiplexing Benefit

Target Blocking probability = 0.0001

If a trunk is composed of 10 cables andeach cable comprises 100 wavelengthsso a trunk has 1000 links

With wavelength conversion, the bottleneck trunk has 1000 links (achieves 91% Utilization).

Without wavelength conversion it is divided into 100 mutually exclusive sets each of a particular wavelength that has 10 links (22% Utilization).

With and Without wavelength conversion

Why if larger A increases utilization?

If the number of busy servers (Q) in an M/M/k/k system is almost always less than total number of output links k, the M/M/k/k behaves (almost) like M/M/infinity.

For M/M/infinity, Q is Poisson distributed with parameter A.Thus, E[Q] = Var [Q] = A.Poisson => Normal as A (and k) increase.So σ[Q]/ E[Q] => 0 as A increases. The spare capacity (k-E[Q]) , e.g. 5σ[Q], becomes negligible

relative to E[Q] (Recall E[Q] =A).

This is similar to what we saw before.

Bursty traffic

As A increases we go from:

150 Mbit/sec

frequency

Bit rate

1000 Mbit/secSpare capacity

850 Mbit/sec

frequency

Bit rate1000 Mbit/sec

Smooth traffic

To:

M/M/k/k modeling of OPS/OBS over WDM

Time

wavelength 1

wavelength 2

wavelength 3

Blocking probability is obtainedby the Erlang B Formula

• Limited number of input links. => Engset Model - Still telephony (1918)

• Frozen time when a packet is dumped.=> Generalized Engset Model (Cohen 1957)

• Optical buffers.• Frozen time - packet is inserted into the buffer.• Hybrid circuit/packet switching.• Hybrid electronic/optical switching(!)• Optical burst switching• Network with multiple bottlenecks.• TCP on top.

Extensions and technology choices

One optical network modelCore switches: symmetrical

Edge routers: infinite buffers;

Access links: smaller bandwidth than core links;

TCP sources: saturated; no maximum window limit; (conservative, large send and receive buffers)

Focus on one output trunk

Notation M: total number of input links,

K: number of output links,

B: buffer size,

: service rate of a single output link

= reciprocal of mean packet time.

PD: packet loss probability.

Analytical model

PD

λI = 1/[inter packet time per link]

λI

Model of TCP throughput

Relationship between TCP bottleneck throughput and packet loss probability:

Ragg : the aggregate TCP throughput,

N : the number of TCP flows,

M : the number of input links,

RTTH : the harmonic average round-trip time

)(

/5.1

M

RTT

PNR

H

Dagg

I

I

Generalized Engset with Buffer (GEB)

* = 1/(1/ I +PD/), fixed-point solution is needed.

Related models• Engset with buffer (EB)

– Use I instead of * in GEB (no need for a fixed point solution).

• M/M/K/K+B

Fix-point equations

binary search algorithm => fixed point solution

Open loop

Model Validation 16 input trunks

Zero Buffer – Scaling Effect

# Sources

No wavelength conversion

ConclusionTeletraffic models can be used to provide insight into the economics of the optical-Internet.

Power usage and related cost must be considered.

In the optical Internet buffering can be pushed to the edges efficiently as traffic, number of sources and capacity (number of wavelengths per cable) increases, if cost effective optical wavelength conversion is available.