02 1 Qos Multimedia

8/8/2019 02 1 Qos Multimedia

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[KS], WS 2005/2006 2.1

Kommunikationssysteme [KS]

Dr.-Ing. Falko Dressler

Computer Networks and Communication SystemsDepartment of Computer Sciences

University of Erlangen-Nürnberg

http://www7.informatik.uni-erlangen.de/~dressler/

[email protected]



[KS], WS 2005/2006 2.2

Course Overview

Network InfrastructureBasics of communication networks, packet- / circuit switched networks,telephone networks, ATM

Quality of Service and Multimedia

Policing, scheduling, AQM, audio/video streaming, conferencing and VoIP,IntServ, DiffServ, ATM, MPLS

SimulationTools, accomplishment, measures

Protocol EngineeringFSM, SDL, MSC, UML

Wireless / Mobile CommunicationsGSM, UMTS, mobility issues, WLAN, WPAN, WSN

Network SecurityThreats and security goals, basic mechanisms



[KS], WS 2005/2006 2.3

Quality of Service

What is Quality of Service (QoS)?QoS is a concept for specifying how “good” services of communicationsystems arequalitative criteria : scalability, usability, compatibility, security,

manageability, robustness, …quantitative criteria : performance, reliability, availability, costs, …mechanisms to achieve QoS: system dimensioning, protocol optimization,control mechanisms, resource reservation, …

need to describe QoS requirements, workload characteristics, systemcapacity, to negotiate service level agreements, to design systems toachieve QoS, to monitor the QoS of systemsto avoid costly redesigns, QoS should be integral part of the whole systemlife cycle and not just as an afterthought : requirements analysis andspecification, design, development, testing, deployment, operation,evolution



[KS], WS 2005/2006 2.4

QoS Parameters in Networks

Data in the network

(in transit, stored in buffers)

Data in the network

(in transit, stored in buffers)

throughput

loss

load

delay



[KS], WS 2005/2006 2.5

QoS Requirements

Requirements of some applications:

Application

file transfer e-mail

Web documentsreal-time audio/video

stored audio/videointeractive gamesinstant messaging

Loss

no lossno lossno lossloss-tolerant

loss-tolerantloss-tolerantno loss

Throughput

elasticelasticelasticaudio: 5kbps-1Mbpsvideo:10kbps-5Mbpssame as abovefew kbps upelastic

Delay Sensitive

nononoyes, 100’s msec

yes, few secsyes, 100’s msecyes and no



[KS], WS 2005/2006 2.6

QoS Requirements

QoS providing

best-effort : QoS parameters are met if possible, no mechanisms to assureQoS, no guarantees

imperfect : QoS parameters are met theoretically, some systemcomponents do not allow guarantees

predicted : QoS parameters are guaranteed if load is not increased

statistically : QoS parameters are guaranteed with a certain probability

deterministically : QoS parameters are guaranteed



[KS], WS 2005/2006 2.7

QoS: Methodology Overview

Measurements(HW/SW/hybrid monitoring, benchmarks)

Modeling

Discrete-Event Simulation(OPNET, ns2, UML, ...)

Numerical Analysis(SPNs, Markov chains, ...)

Closed-Form Analysis(queuing models, ...)

Costs

P a r a m e t e r

e s

t i m a

t i o n

V a

l i d a t i o n

Abstraction



[KS], WS 2005/2006 2.8


Measurementsmonitoring (observe system passively)benchmarking (actively generate workload)

HW, SW, hybridcan measure QoS parameters: delays, throughput, resourceusage, …

A monitoring tool: tcpdumpcan dump headers and payloads of packets



[KS], WS 2005/2006 2.9


Discrete-event simulationsystem is modeled in some visual/textual formalismrandom variables are used for times and other model elementsevents occur at discrete points in time

variables change instantaneouslyevent list processing:

important issues: modeling formalism, measure specification, tool

environment, simulation speed, animation, input modeling, randomnumber generation, output analysisadvantage: no modeling restrictionsdisadvantages: slow, uncertainty in confidentiality of results

tD1 A2 A3 D2 A4 A5 D3A10



[KS], WS 2005/2006 2.10


A simulation tool: OPNETspecial-purpose simulation package in the networking area

hierarchies- network building blocks- state machines- C code

libraries of detailed protocols andapplication models: Multi-Tier Applications,Voice, HTTP, TCP, IP, OSPF, BGP, EIGRP,RIP, RSVP, Frame Relay, FDDI, Ethernet,ATM, 802.11 Wireless LANs, ...



[KS], WS 2005/2006 2.11


Numerical analysissometimes possible to model system or system aspect as a stochasticprocess, e.g., a Markov chaineither directly or via a higher-level modeling formalisms, e.g., stochastic

Petri net (SPN) or UMLallows to represent complex interdependenciesadvantage: exact answersdisadvantages: restrictions in terms of state space and kinds of distributions allowed



[KS], WS 2005/2006 2.12


Queuing (network) modele.g., HTTP requests/responses:

state spaces can be immense, product-form solutions avoid state-spacegenerationgood for checking whether a system is roughly well dimensioned and for identification of bottlenecksmultiple classes lead to good expressivenessbut cannot model interdependencies such as synchronization, blocking, …

clientLAN

Router Web server

upstream link

downstream link



[KS], WS 2005/2006 2.13

Networking Delay Models

Basic model for a delay/loss systemCustomers (connection requests, messages, …) arrive according to somearrival patternSystem can be transmission line, multiplexer, router, network, …

Customer spends some time T in the system and then departsUnder certain conditions the system may block new customers to enter

Delay box :Multiplexer

switchnetwork

Message,packet,cellarrivals

Message,packet,celldepartures

T secondsLost or blocked



[KS], WS 2005/2006 2.14


Important performance parametersTime spent in the system: TFraction of arriving customersthat are lost or blocked : P b

Average number of customers/sthat pass through the system:throughput S

Key system variablesNumber of customersin the system: N(t)A(t) = number of arrivals in [0, t]B(t) = number of blocked customers in [0, t]D(t) = number of departures in [0, t]

01234

0 1 2 3 4 5 6 7 8 9 10

N(t)

t



[KS], WS 2005/2006 2.15


Some simple relationships

N(t) = A(t) – B(t) – D(t)

Long-term arrival rate:

Throughput:

Average number in the system:

Fraction of blocked customers:

scustomers/t

)t(Alimt ∞→

=λ

scustomers/t

)t(DlimSt ∞→

=

customers)dt'N(t't1

lim]N[Et

0t ∫ ∞→

=

)t(A)t(BlimP

tb ∞→=



[KS], WS 2005/2006 2.16

A(t)

t 0

1

2

n-1

n

n+ 1

Time of n th arrival = τ1 + τ2 + . . . + τn

ArrivalRate λ

n arrivals

τ1 + τ2 + . . . + τn seconds=

1=

1

(τ1+τ2 +...+ τn)/n E [τ]

τ1 τ2 τ3 τn τn+1

Arrival Rate = 1 / mean interarrival time

•••



[KS], WS 2005/2006 2.17


Little‘s formula

E[N] = λ E[T]

Average number of customers in the system = arrival rate x average timespent in systemA fundamental relationshipIntuitively: crowded systems have long customer delay and reverselyE.g., on a rainy day, traffic on a rush hour moves slower than average(large E[T]), while streets are more crowded (large E[N]).



[KS], WS 2005/2006 2.18


Derivation of Little’s formulaAssume no blocking,FIFO service, N(0)=0

No blocking, hence N(t) = A(t) – D(t)We can draw A(t) and D(t) in one graph, N(t) is the space between:

A(t) D(t)FIFO Delay Box

N(t)

T

A(t)

D(t)T 1

T 2

T 3T 4

T 5

T 6 T 7 Assumes

first-infirst-out

C 1 C 2 C 3 C 4 C 5 C 6 C 7

C 1 C 2 C 3 C 4 C 5 C 6 C 7

Arrivals

Departures



[KS], WS 2005/2006 2.19


Derivation of Little’s formula (cont.d)Let t 0 be a time where D(t) = A(t), thus N(t 0) = 0(e.g., when C 5 leaves)The area under N(t) is also givenby summing up the delays:

A little manipulation gives

By taking limits onboth sides and by assuming that

… we arrive at the desired expression E[N] = λ E[T]Can be shown also without assumptions

∑∫ =

=)t(A

1 j j

0

t

00

00

Tt1

)dt'N(t't1

⎭⎬⎫

⎩⎨⎧= ∑∫

=

)t(A

1 j j

00

0t

00

00

T)t(A

1t

)t(A)dt'N(t't1

⎭⎬

⎫

⎩⎨

⎧=∑=∞→

)t(A

1 j j0)t(A

0

0

T)t(A

1lim)T(E



[KS], WS 2005/2006 2.20


Application of Little‘s formulaValid for arbitrary systems which can delay and store customers but notloose or duplicate them

For blocking systems the customers A(t) - B(T) that are not blocked arethe arriving customers to the system, Little’s formula getsE[N] = λ (1-P b) E[T]

arrivals departuresE[N], T



[KS], WS 2005/2006 2.21


Queuing modelsExtend the delay box model by a queue and one or more serversThe arrival process and the service times are characterized stochastically

1

2

c

A(t)t

D(t)t

B(t)

Queue

ServersArrival process

X Service time

τi τi+1 …



[KS], WS 2005/2006 2.22


Arrival processSimple case: constant interarrival times = deterministic arrivalsMore interesting: interarrivals are independent random variables from aprobability distribution

Popular case: Poisson processarrival rate λ: requests per secondIn each small time interval Δ: P[new request ] = λΔthe interarrival times are then distributed according the exponentialdistribution F(t) = P[X ≤ t] = 1-e -λt and density f(t) = λe -λt

f(x) F(x)

x x

λ 1



[KS], WS 2005/2006 2.23


Popular case: Poisson process (cont.d)With exponentiallydistributed interarrival times we getThis is called the Poisson probability

Therefore this arrival process is called Poisson processMuch more involved arrival processes can be defined by using other interarrival distributions, dependent random variables, …

Self-similarity and long-range dependency are important aspectsconsidered todayHowever, exponential timing allows for easier analysisThe service times can also be deterministic, exponentially distributed,

distributed according to another distribution, or more complicatedwe use μ customers/s for the service rateFor packet switching: service time = packet length / bitrate

tet!t

t)][0,inarrivalsn[P λ−λ=



[KS], WS 2005/2006 2.24


Queuing system classification

Service times X M = exponentialD = deterministic

G = generalService Rate:μ = 1/ E [ X ]

Arrival Process / Service Time / Servers / Max Occupancy

Interarrival times τM = exponentialD = deterministic

G = generalArrival Rate:λ = 1/ E [τ]

1 server c serversinfinite

K customersunspecified if unlimited

Multiplexer Models: M/M/1/ K , M/M/1, M/G/1, M/D/1Trunking Models: M/M/ c /c , M/G/ c /c User Activity: M/M/ ∞, M/G/ ∞

N ki D l M d l



[KS], WS 2005/2006 2.25


Queuing system variables

1

2

c

X

N q(t)N s(t)

N(t) = N q(t) + N s(t)

T = W + X

W

λ P b

λλ (1 – P b)

N(t) = number in system

N q(t) = number in queue

N s(t) = number in service

T = total delay

W = waiting time

X = service time

…

N ki D l M d l



[KS], WS 2005/2006 2.26


Generality of Little‘s formula: it can be applied tothe queue: E[N q] = λ (1-P b) E[W]the servers: E[N S] = λ (1-P b) E[X]the complete system: E[N] = λ (1-P b) E[T]

Traffic loadTraffic load : λ customers/s x E[X] s/customers = λ/μ ErlangsErlang is a dimensionless unit, 1 Erlang corresponds to a load that wouldoccupy a single system all of the time

Utilization ρAverage fraction of servers in use

)P1(

cc

]N[Eb

S −μ

λ==ρ

M/M/1 Q



[KS], WS 2005/2006 2.27

M/M/1 Queue

Poisson arrivals (exponential interarrivals), exponential service,inifinite buffer

Basic model for packet multiplexers

Poisson arrivalsrate λ

Exponential servicetime with rate μInfinite buffer

M/M/1 Q



[KS], WS 2005/2006 2.28

M/M/1 Queue

Analysis of the M/M/1 queuepossible by mapping on a continuous-time Markov chainstate-transition diagram:

...0 1 2

λ λ λ

μ μ μ

i-1 i i+1

λ λ λ λ

μ μ μ μ...

state: customers in system

arrival

service service rate

arrival rate

M/M/1 Queue



[KS], WS 2005/2006 2.29

M/M/1 Queue

Solution

state probabilities :

balance equations : for each state, balance of flow in and flow out:

λπ0 = μπ1

(λ+μ)πi = λπi-1 + μπi+1, i = 1, 2, ...

...0 1 2 i-1 i i+1

λ λ λ λ λ λ λ

μ μ μ μ μ μ μ...

)n)t(N(Plimtn ==π

∞→

M/M/1 Queue



[KS], WS 2005/2006 2.30

M/M/1 Queue

Steady-state solution (cont.)from the first balance equation we get π1 = ρ π0

from the second balance equation π2 = ρ2 π0

...

generally: πi = ρi π0, i = 0, 1, 2, ...using the normalization condition

we get π0 = 1 - ρ

and πi = ρi(1-ρ) for i = 0, 1, 2, ...

the modified geometric distribution

11 1 00i

0i

0ii =πρ−=πρ=π ∑∑

∞

=

∞

=

0 1 2 3 4 5 i

πi

M/M/1 Queue



[KS], WS 2005/2006 2.31

M/M/1 Queue

Measures of interestaverage number of customers in the system (queue + service unit)

average number of customers in the queueρ−

ρ==ρρ−=π= ∑∑∞

=

∞

= 1

)1(ii]N[E0i

i

0i

iL

ρ−ρ

=ρ−ρ−ρ

=π−−=π−π=π−= ∑ ∑∑∞

=

∞

=

∞

= 11)1(]N[Ei)1i(]N[E

2

1i 01i ii1i iq

M/M/1 Queue



[KS], WS 2005/2006 2.32

M/M/1 Queue

With Little’s formulain steady-state, throughput = arrival rate = λthe delay in the system is

the delay in the queue is

ρ−μ=λ= 1 /1]N[E]T[E

ρ− ρμ=λ=1

)/1(]N[E]W[E q

M/M/1 Queue



[KS], WS 2005/2006 2.33

M/M/1 Queue

Average delay vs. utilizationaverage delay E[T] in the M/M/1 queue as a function of ρ

linear growth for small utilizations, unbounded growth as utilizationapproaches unity!

0.2 0.4 0.6 0.80

5

10

15

ρ

E[T]

M/M/1 Queue



[KS], WS 2005/2006 2.34

M/M/1 Queue

Typical design questionparallel systems with separate buffers and servers or onecentralized fast one?relevant in networking, IT infrastructures, ...

]T[Em1

m/m1m/1

]T[E separatedcentralize =μλ−

μ=

mseparatesystems

or onecentralizedsystem

μλ−μ=/1

/1]T[E seperate

central system has less idle times, faster, multiplexing gain

λ

λ

λ

μ

μ

μ

mλmμ

M/M/1 Queue



[KS], WS 2005/2006 2.35

M/M/1 Queue

Estimation of packet delays in networksapplication of Little‘s formula both on the network level and the switch leveltogether with results for the M/M/1 queueand some assumptions

rough estimation of packet delays for given paths of packet flowsapplied in routing, control, management

M/M/1 Queue



[KS], WS 2005/2006 2.36

Q

Estimation of packet delays in networksλnet = packet arrival rate to the networkλm = packet arrival rate to switch mμm = service rate of switch m

Little’s formula for the network:

E[N net ] is composed of switch queue sizes:

Applying Little’s formula for the switches and insertion:

M/M/1 mean system delay:

And insertion yields the mean packet delay in the network:

netnetnet /]N[E]T[E λ=

∑∑ λ==m

mmm

mnet ]T[E]N[E]N[E

∑λλ

=m

mmnet

net ]T[E1

]T[E

m

mm 1

/1]T[E ρ−

μ=

∑ρ−

ρ

λ=

m m

m

net

net

1

1]T[E

M/M/c/c Queue



[KS], WS 2005/2006 2.37

Q

Poisson arrivals, exponential service, c service units, no buffer

blocked calls are cleared from the system; no waiting allowed.basic model for dimensioning telephone trunksperformance parameter: P b = fraction of arrivals that are blocked

1

2

c

N(t )

λ P b

λ λ (1– P b)

E[X] =1/ μ

…

M/M/c/c Queue



[KS], WS 2005/2006 2.38

Q

Analysis of the M/M/c/c queuestate-transition diagram of the underlying Markov chain:

the rates of exponential state transitions can be superposed, multipleservice units therefore correspond to multiples of the service rate

... c-1 c

λ λ

(c−1)μ

cμ

0 1 2

λ λ λ

μ2μ

3μ

M/M/c/c Queue



[KS], WS 2005/2006 2.39

Solution

balance equations : for each state, balance of flow in and flow out:λπ0 = μπ1 ⇒ π1 = ρ π0

(λ+μ)π1 = λπ0 + 2 μπ2 ⇒ π2 = ρ2/2 π0

(λ+2 μ)π2 = λπ1 + 3 μπ3 ⇒ π3 = ρ3/3! π0

…(λ+(c-1) μ)πc-1 = λπc-2 + c μπc ⇒ πc = ρc/c! π0

cμπc = λπc-1

gives πn = ρn/n! π0 for 0 ≤ n ≤ c

the normalization condition givesthe solution is hence ∑

∑∑=

== ρ=π⇒=πρ=π c

0i

i0

c

0i0

ic

0ii

!i/

11

!i

cn0for !i/

!n/c

0i

i

n

n ≤≤ρ

ρ=π

∑=

M/M/c/c Queue



[KS], WS 2005/2006 2.40

Erlang B formulablocking probability P b =

proportion the system is fullalso valid for M/G/c/c: service time distribution need not beexponential, the solution valid for any service time distribution withmean 1/ μknown as the Erlang B formulaheavily used for dimensioning of telephone systems, also callcenters

∑=

ρρ=π c

0i

i

c

c

!i/

!c/



[KS], WS 2005/2006 2.41

Examples

Telephone Network (TrafficManagement & Overload Control)

Traffic Management & Overload Control



[KS], WS 2005/2006 2.42

Telephone calls come and goPeople activity follow patternsMid-morning & mid-afternoon at officeEvening at home

Summer vacationOutlier Days are extra busy

Mother’s Day, Christmas, …

Disasters & other events cause surges in trafficNeed traffic management & overload control

Traffic Concentration



[KS], WS 2005/2006 2.43

Traffic fluctuates as calls initiated & terminatedDriven by human activity

Providing resources soCall requests always met is too expensiveCall requests met most of the time cost-effective

Switches concentrate traffic onto shared trunksBlocking of requests will occur from time to timeTraffic engineering provisions resources to meet blockingperformance targets

Fewer

trunks

Many

lines

Fluctuation in Trunk Occupancy



[KS], WS 2005/2006 2.44

1

2

3

45

6

7

T r u n

k n u m

b e r

N(t)

t

All trunks busy, new call requests blockedNumber of busy trunks

active

active

active

activeactive

activeactive

active

active

active

Modeling Traffic Processes



[KS], WS 2005/2006 2.45

Find the statistics of N(t) the number of calls in the system

ModelCall request arrival rate : λ requests per secondIn a very small time interval Δ,

Prob[ new request ] = λΔProb[no new request] = 1 - λΔ

The resulting random process is a Poisson arrival process:

Holding time : Time a user maintains a connection

X a random variable with mean E(X)Offered load : rate at which work is offered by users:

a = λ calls/sec * E( X ) seconds/call (Erlangs)

( λ T)k e – λ T

k! Prob( k arrivals in time T ) =

Blocking Probability & Utilization



[KS], WS 2005/2006 2.46

c = Number of TrunksBlocking occurs if all trunks are busy, i.e. N(t)=c If call requests are Poisson, then blocking probability P b is given byErlang B Formula

The utilization is the average # of trunks in use

P b =

a c

c!

k !∑ a k

k =0

c

Utilization = λ (1 – P b) E [ X ]/c = (1 – P b ) a /c

Blocking Performance



[KS], WS 2005/2006 2.47

aTo achieve 1% blocking probability:a = 5 Erlangs requires 11 trunksa = 10 Erlangs requires 18 trunks

Multiplexing Gain



[KS], WS 2005/2006 2.48

Load Trunks@1% Utilization

1 5 0.20

2 7 0.29

3 8 0.38

4 10 0.40

5 11 0.45

6 13 0.46

7 14 0.50

8 15 0.53

9 17 0.53

10 18 0.56

30 42 0.71

50 64 0.78

60 75 0.80

90 106 0.85

100 117 0.85

At a given P b, the system becomesmore efficient in utilizing trunks withincreasing system sizeAggregating traffic flows to sharecentrally allocated resources is moreefficientThis effect is called Multiplexing Gain

Routing Control



[KS], WS 2005/2006 2.49

Routing control: selection of connection pathsLarge traffic flows should follow direct route because they are efficient in useof resourcesUseful to combine smaller flows to share resourcesExample: 3 close CO’s & 3 other close COs

10 Erlangs between each pair of COs

17 trunks for 10 Erlangs9x17=153 trunksEfficiency = 90/153=53%

106 trunks for 90 ErlangsEfficiency = 85%

E

F

D

B

C

A(a)

10 Erlangs between each pair

Tandemswitch 2

Tandemswitch 1

B CA

(b)Trunkgroup

E FD

90 Erlangs when combined

Alternative Routing



[KS], WS 2005/2006 2.50

Deploy trunks between switches with significant traffic volumeAllocate trunks with high blocking, say 10%, so utilization is highMeet 1% end-to-end blocking requirement by overflowing to longer paths over tandem switchTandem switch handles overflow traffic from other switches so it can operateefficientlyTypical scenario shown in next slide

Switch SwitchHigh-usage route

Tandemswitch

Alternative route

Typical Routing Scenario



[KS], WS 2005/2006 2.51

High-usage route B-E

Tandemswitch 1

Alternative routesfor B-E, C-F

High-usage route C-F

Switch B

Switch C

Switch E

Switch D

Switch F

Tandemswitch 2

Switch A

Dynamic Routing



[KS], WS 2005/2006 2.52

High-usage route

Alternative routes

Switch A Switch B

Tandemswitch 3

Tandemswitch 1

Tandemswitch 2

Traffic varies according to time of day, day of weekEast coast of North America busy while West coast idle

Network can use idle resources by adapting route selection dynamicallyRoute some intra-East-coast calls through West-coast switches

Try high-usage route and overflow to alternative routes

Overload Control



[KS], WS 2005/2006 2.53

C a r r

i e d l o a

d

Offered load

Network capacity

Overload SituationsMother’s Day, Xmas

CatastrophesNetwork Faults

StrategiesDirect routes firstOutbound firstCode blocking

Call request pacing

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02 1 Qos Multimedia