quality of service in telecommunícation networks… · quality of service in telecommunication...

Quality of Service inTelecommunication Networks

Åke Arvidsson, Ph.D.Ericsson Core Network Development, Sweden

© Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson Quality of Service in Telecommunícation Networks 2005-11-082

Main Message

Traffic theory:– QoS is (partly) about congestion.– Congestion is partly caused by temporary overload.– Temporary overloads can be handled by statistics.– QoS can be controlled by statistics.

Typical issues for regulators:– Is an operator serious about quality of service?– What is the fair price to carry additional traffic?

Typical issues for operators:– How should a network be engineered?– Can I just go for “over-provisioning”!


Overview

Background and motivation.The significance of variations.Big variations means expenses.

– Relationship to shared networks.

Small variations means savings.– Relationship to shared networks.

Hot topic: Relevance to IP and integrated services.


Background (1)

Voice and data traffic exhibit significant variations.Variable demand but fixed resources.

– Traffic may be lost.– Traffic may be delayed.– Traffic may be subject to other impairments.

Mathematically tractable by traffic theory.


Background (2)

Can be used to control quality of service.Essential part of competition.

– Competitive advantage.– Add value to services.

Essential part of regulation/competition.– Fair treatment of new players by former monopolies.– Fair criteria for customers to choose service provider.


Background (3)

Significance:– Society: Reliable services.– Operators: Stable systems, fair competition.– Users: Value for money.


Background (4)

Requirements:– Prerequisite: The busy hour concept.– Delays: dialing tone, through connection, speech, ...

ITU-T, ETSI and others.– Blocking: lost traffic, ...

Typically operator dependent.– The above apply to voice services.

Data services specified but less used.


Variations

The “problem” lies in variations:– Ideal: Fixed inter-arrival times and fixed service times:

Time

– Real: Variable inter-arrival times (and variable service times):

Time


Traffic Theory

Statistical analysis of service systems:– Waiting times.

Example: Dialing tone delay in a switch.– Congestion probabilities.

Example: Traffic rejected from a trunk.– Utilisations.

Example: Load on a processor or link.


Quality of service vs. supply

0,0

0,2

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0,6

0,8

1,0

0,6 0,7 0,8 0,9 1,0 1,1 1,2 1,3 1,4

Capacity/Demand

Prob

abili

ty o

f suf

ficie

ncy

Better QoStakes more

capacityOnly0.53

No “over-supply”


Conclusions (1)

Dimension for average:– Variations unaccounted for.– Related impairments ignored.– No control of QoS!

Traffic theory necessary for QoS.– Correct provisioning aims at targeted QoS.– Over-provisioning is more than that, not just more than the

mean.


Randomness (1)

More randomness means more problems.– More delay, higher blocking, etc.

Examples:– Circuit oriented traffic.

Erlang: Number of parallel connections.– Packet oriented traffic.

Load: A metric between zero and unit.


Blocking and Variations

0.000

0.050

0.100

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0.200

0.250

0.300

0.350

0.00 2.00 4.00 6.00 8.00 10.00

Traffic variation (peakedness)

Blo

ckin

g pr

obab

ility

1 erlang

10 erlang

100 erlang


Waiting Time and Variations

0

50

100

150

200

0 2 4 6 8 10

Service time variation

Mea

n w

aitin

g tim

e

Load 0.5

Load 0.8

Load 0.2


Conclusions (2)

More randomness means more problems.– Split costs depend on traffic variations.

Simple “dimensioning” based on factors:– Correct answers for (at most) one working point only.

“Over-dimensioning” is not as simple as it may sound.– How much is too much?


Randomness (2)

Less randomness means less problems.– Less delay, lower blocking, etc.

Examples:– Circuit oriented traffic.

Erlang: Number of parallel connections.– Packet oriented traffic.

Load: A metric between zero and unit.


Circuits per Erlang

Circuit oriented traffic.– How many circuits are needed for one Erlang?– Depends on performance and the scale of the system!

0.50.0

1

1.01.52.02.53.03.54.04.55.0

10 100 1000 10000

Traffic

Circ

uits

per

erla

ng

High grade of service (0.5%)

Low grade of service (5%)


Packet Delay

Packet oriented traffic.– What is the delay for a packet/signal?– Depends on the load and the scale of the system!

0.00001

0.001

0.1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Load

Del

ay (s

)

30 parallel 64 kbps links;random/optimal sharing(20 octet packets)One 30×64 kbps link(20 octet packets)


Conclusions (3)

Less randomness means less problems.– Sharing networks reduces marginal costs.

“The bigger the better”.– One big system is better than many small systems.

Law of large numbers.– The more samples, the less variation.


Classic Traffic Theory

Circuit switched services.– Erlang-B.

Agner Krarup Erlang(1878 – 1929)

KTAS, Copenhagen, 1917.

Pleased tomeet you!

– Wide range of extensions.

Packet switched services.– Signal networks.– Early arpanet community.

How about present IP?– Before: Best effort only.– Now: QoS required.


Quality of Service in IP

Profitability:– Charging on QoS to boost income.

Integration:– Service integration to cut expenses.

Efficiency:– Utilise expensive wireless resources.


Recent Developments (1)

Data (IP) traffic models:– Packets are like calls in telephone traffic.– Poisson process.– “Burstier” processes.

Ethernet (and other) measurements:– High spread (heavy tails).– Slow variations (long range dependence).– Time scale independence (self similarity).

Earlier models far too optimistic!– Forget the old economy, the new economy is here!


Results (1)

Heaviesttail

Lightesttail

Load≈71%

Queue10,000

Noqueue

Load≈ 50%



Objections:– Most data traffic is subject to flow control.

Queues cannot grow without bounds.– TCP applies dynamic flow control to maximise throughput.

Links may operate near (local) saturation.

Earlier diagram based on measured traffic.– Measurements are reality but– the experiments are not real since flow control is “frozen”.


Results (2)

Earlier diagram Active flow control


Conclusions (4)

“New” discoveries not so dramatic.However, traffic and systems interaction complicates:

– Measurements:What does link load mean to user performance?

– Modeling:Simple steady state Poisson not generally applicable.

– Dimensioning:Traffic and performance mutually dependent.



Research at Ericsson:– Mathematical methods.

Performance criterion:– Downloading time (useful throughput).

Examples:– Bottleneck identification and removal.– “Peak factor” calculations.


Example: Bottlenecks (1)

05

101520253035404550

Access ratePacket dropPropagationW

indow sizeFile size

+ %

10 packetsFile size:

4 packetsWindow size:

100 msPropagation:

2 %Packet drop:

256 kbpsAccess rate:



50 msPropagation:

2 %Packet drop:




50 msPropagation:

2 %Packet drop:




50 msPropagation:

2 %Packet drop:




50 msPropagation:

2 %Packet drop:


94 kbpsThroughput: 130 kbpsThroughput:



05

101520253035404550


indow sizeFile size



50 msPropagation:

2 %Packet drop:




50 msPropagation:

2 %Packet drop:

256 kbpsAccess rate:+ %



50 msPropagation:

2 %Packet drop:




50 msPropagation:

2 %Packet drop:




50 msPropagation:

2 %Packet drop:


05

101520253035404550


indow sizeFile size




05

101520253035404550


indow sizeFile size



50 msPropagation:

2 %Packet drop:




50 msPropagation:

2 %Packet drop:

512 kbpsAccess rate:+ %



50 msPropagation:

2 %Packet drop:




50 msPropagation:

2 %Packet drop:




50 msPropagation:

2 %Packet drop:


05

101520253035404550


indow sizeFile size



Example: “Peak Factor” (1)

Network:– Packet loss probability 1%.– Round trip time 300 ms.

Users:– Access rate: 33% 64/128 kbps; 67% 64/384 kbps.– Packet size: 25% 536 bytes; 75% 1540 bytes.– Window size: 16.384 kbyte.

Traffic:– 170 kbyte/busy hour.– WAP, WWW, MMS, Mail.



Dimension core network:– A connection sees 5 links in tandem end-to-end.– 5% additional RTT acceptable (15 ms means 3 ms per link).– Different links have different load.

One peak factor?– Twice the average (peak factor 2, load 50%).– Is this too much (over-dimensioning)?– Or do we need more (e, π, ...)?



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0 2000 4000 6000 8000 10000

Sharing users

Peak

fact

or


Conclusions (5)

IP (TCP) fractal properties often overrated.– Relationship to variation conceptually the same.

This does not mean that dimensioning is simple:– Traffic and network are mutually dependent.– Traffic exhibits large variations over time.– User perceived performance different from directly

measurable.– A magnitude of parameters means unclear bottlenecks.– High burstiness may still enforce low utilisation.


Main Message

Traffic theory:– QoS is (partly) about congestion (loss and delay).– Congestion is partly caused by temporary overload

(variations).– Temporary overloads can be handled by statistics (traffic

model).– QoS can be controlled by statistics (queuing theory).

Typical issues:– Is an operator serious about quality of service? Goals!– What is the fair price to carry additional traffic? Depends!– How should a network be engineered? Methods!– Can I trust “over-provisioning”! No!

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