A statistical network calculus for computer networks
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A Statistical Network Calculus for Computer Networks. Jorg Liebeherr Department of Computer Science University of Virginia. Collaborators. Almut Burchard Robert Boorstyn Chaiwat Oottamakorn Stephen Patek Chengzhi Li Florin Ciucu. Papers.

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A statistical network calculus for computer networks

A Statistical Network Calculus for Computer Networks

Jorg Liebeherr

Department of Computer Science

University of Virginia


Collaborators

Collaborators

  • Almut Burchard

  • Robert Boorstyn

  • Chaiwat Oottamakorn

  • Stephen Patek

  • Chengzhi Li

  • Florin Ciucu


Papers

Papers

  • R. Boorstyn, A. Burchard, J. Liebeherr, C. Oottamakorn. “Statistical Service Assurances for Packet Scheduling Algorithms”, IEEE Journal on Selected Areas in Communications. Special Issue on Internet QoS, Vol. 18, No. 12, pp. 2651-2664, December 2000.

  • A. Burchard, J. Liebeherr, and S. D. Patek. “A Calculus for End–to–end Statistical Service Guarantees.” (2nd revised version), Technical Report CS-2001-19, May 2002.

  • J. Liebeherr, A. Burchard, and S. D. Patek , “Statistical Per-Flow Service Bounds in a Network with Aggregate Provisioning”, Infocom 2003.

  • C. Li, A. Burchard, J. Liebeherr, “Calculus with Effective Bandwidth”, Technical Report CS-2003-20, November 2003.

  • F. Ciucu, A. Burchard, J. Liebeherr, ",A Network Service Curve Approach for the Stochastic Analysis of Networks”, ACM Sigmetrics 2005, to appear.


Toy models in computer networking

“Toy Models” in Computer Networking

  • Learn from Physics: Wide use of toy models

    … that capture key characteristics of studied system

    … that permit back-of-the-envelope calculations

    … that are usable by non-theorists

  • Simple models have played a major role in the evolution and development of data networks

    • Queueing Networks

    • Effective Bandwidth

    • (Deterministic) Network Calculus


Product form queueing networks

(Product Form) Queueing Networks

  • Jackson (50’s), Kelly, BCMP (70’s)

  • Flow of “jobs” in system of queues and servers

  • Applications: Provided motivation for packet-switching (Kleinrock’s PhD thesis)

Main result: Steady state probability of queue occupanceyn = (n1, n2, … , nk) :

P(n ) = P(n1) P(n2) … P(nk)

Limitations:

  • Limited to Poisson traffic

  • Limited scheduling algorithms


Effective bandwidth

Effective Bandwidth

Hui, Mitra, Kelly (90s)

  • Describes bandwidth needs of complex traffic by a number

  • Application: admission control in ATM networks

Peak rate

effectivebandwidth

Mean rate

Can consider:

  • service guarantees

  • wide variety of traffic (incl. LRD)

     statistical multiplexing

    Limitations:

     not well suited for scheduling


Network calculus

S3

S1

S2

Receiver

Sender

Snet

Network Calculus

  • Cruz, Chang, LeBoudec (90’s)

  • Worst case delay and backlog bounds for fluid flow traffic

  • Application: design of new schedulers (WFQ) new services (IntServ).

  • Main result: If S1, S2 and S3 describes the service at each node, then Snet = S1 * S2 * S3describes the service given by the network as a whole.

Limitations:

  • No random losses

  • No statistical multiplexing, therefore pessimistic


State of the art

State-of-the-art

  • No analysis methodology is widely used today.

  • Today, a lot of networking research relies on simulation and measurements to validate new designs

  • Simulation and measurement are generally not suitable for evaluation of radically new designs


Motivation develop network calculus into new toy model

Motivation: Develop network calculus into new“Toy Model”

Today, fundamental progress in networking is hampered by the lack of methods to evaluate how radically new designs will perform.

  • Opportunity: Simple (`toy') models that permit fast (`back-of-the-envelope') evaluations can become an enabling factor for breakthrough changes in networking research

  • Approach: Probabilistic version of network calculus (stochastic network calculus) is a candidate for a new class of toy models for networking


Related work small subset

Deterministic network calculusCruz `91

Effective bandwidth in network calculusChang `94

Effective Bandwidth:

J. Hui ’88Guerin et.al. ’91Kelly `91Gibbens, Hunt `91

(min,+) algebra for det. networks:

Agrawal et.al. `99Chang `98LeBoudec `98

ServiceCurvesCruz `95

  • Our goals:

  • Maintain elegance of deterministic calculus

  • Exploit statistical multiplexing

  • Try to express other models

Cruz calculus with probabilistic trafficKurose `92

Exponentially/stochasti-cally. bounded burstinessYaron/Sidi `93Starobinski/Sidi `99

RateVarianceEnvelopeKnightly `97

Stochastically bounded service curveQiu et.al.`99

Related Work (small subset)

2005

1985

1990

1995

2000


Multiplexing gain

Multiplexing Gain

Multiplexing gain is the raison d’être for packet networks.

Sources of multiplexing gain:

  • Traffic characterization and conditioning

  • Scheduling

  • Statistical Multiplexing


Traffic conditioning

Traffic Conditioning

Traffic Conditioning

  • Traffic conditioning is typically done at the network edge

  • Reshaping traffic increases delays and/or losses


Scheduling

Scheduling

  • Scheduling algorithm determines the order in which traffic is transmitted

  • Examples:

    • Different loss priorities  priority scheduling

    • Traffic with rate guarantees  rate-based scheduling (WFQ, WRR)

    • Delay constraints  deadline-based scheduling (EDF)


Multiplexing gain1

Worst-casebacklog

Backlog

Backlog

Multiplexing Gain

Without statistical multiplexing

Worstcasearrivals

Flow 1

Flow 2

Flow 3

Time

With statistical multiplexing

Arrivals

Flow 1

Flow 2

Flow 3

Time

Backlog


Example of statistical multiplexing retirement savings

Example of Statistical Multiplexing: Retirement Savings

Life expectancy: Normal(m=75, s=10) years

Retiring Age:65 years

Interest: 0%

Withdrawal: $50,000 per year

How much money does a person need to save (with confidence of 95% or 99%)?

Life expectancy in a group of N people is Normal(m, s / N).

N=1 person (Individual Savings): 95% confidence: 10 + 2s = 30 years  $1.5 Mio.99% confidence: 10 + 2s = 40 years  $2 Mio.

N=100 people (Pooled Savings): 95% confidence: 10 + 2s = 12 years  $600,00099% confidence: 10 + 2s = 13 years  $650,000


The importance of statistical multiplexing

The importance of Statistical Multiplexing

  • At high data rates, statistical multiplexing gain dominates the effects of scheduling and traffic characterization


Traffic characterization

Traffic Characterization

  • Arrivalsfrom a flow j are a random process

  • Stationarity: The are stationary random processes

  • Independence: The and are stochastically independent


Regulated arrivals

Regulated Arrivals

Flow 1

.

.

.

C

Flow N

Each flow isregulated

Buffer

with Scheduler

Regulated

arrivals

Traffic is constrained by a subadditive deterministic envelope such that

Leaky Buckets:


Effective envelope

Effective envelope

Define a function that bounds traffic with high probability  “Effective Envelope”

Definition:Effective envelope for is a function such that

Note: Effective envelope is not a sample path bound. Often, we need a stronger version of the effective envelope!


Sample paths and envelopes

Stronger effective envelope

At most one sample path is violated

Deterministic envelope

Never violated

Samplepaths

Effective envelope

At any time, at most one sample path is violated

Sample Paths and Envelopes

Note: All envelopes are non-random functions


Probabilistic sample path bound

Probabilistic Sample Path Bound

A strong effective envelope for an interval of length is a function which satisfies

Relationship between the envelopes is established as follows:

with


Aggregating arrivals

Aggregating Arrivals

Flow 1

.

.

.

C

Flow N

Traffic Conditioning

Buffer

with Scheduler

Regulated

arrivals

Arrivals from multiple flows:

Deterministic Network Calculus: Worst-case of multiple flows is sum of the worst-case of each flow


Effective envelopes for aggregated flows

Effective Envelopes for aggregated flows

Stochastic Calculus: Exploit independence and extract statistical multiplexing gain by calculating

  • For example, using the Chernoff Bound, we can obtain


Effective vs deterministic envelope envelopes

Effective vs. Deterministic Envelope Envelopes

Type 1 flows:

P =1.5 Mbps

r = .15 Mbps

s =95400 bits

Type 2 flows:

P = 6 Mbps

r = .15 Mbps

s = 10345 bits

strong effective

envelopes

Type 1 flows


Effective vs deterministic envelope envelopes1

Effective vs. Deterministic Envelope Envelopes

Traffic rate at t = 50 msType 1 flows


Scheduling algorithms

Deterministic Service

Never a delay bound violation if:

Statistical Service

Delay bound violation with if:

Scheduling Algorithms

  • Work-conserving scheduler with unit rate that serves Q classes

  • Class-q traffic has delay bound dq

  • Scheduling algorithm:

.

.

.

Scheduler

  • Static Priority (SP):

  • Earliest Deadline First (EDF):


A statistical network calculus for computer networks

Statistical multiplexing makes a big difference

Scheduling has small impact

Statistical Multiplexing vs. Scheduling

Example: MPEG videos with delay constraints at C= 622 Mbps

Deterministic service vs. statistical service (e = 10-6)

dterminator=100 ms dlamb=10 ms

Thick lines: EDF SchedulingDashed lines: SP scheduling


More interesting traffic types

More interesting traffic types

  • So far: Traffic of each flow was regulated

  • Next: Consider different traffic types:

    • On-Off traffic

    • Fraction Brownian Motion (FBM) traffic

  • Approach: Exploit literature on Effective Bandwidth

    • Describes traffic in terms of a function

    • Expressions have been derived for many traffic types


Effective envelopes and effective bandwidth

Effective Envelopes and Effective Bandwidth

Effective Bandwidth (Kelly 1996)

Given , an effective envelope is given by


Effective envelopes and effective bandwidth1

Effective Envelopes and Effective Bandwidth

Comparisons of statistical service guarantees for different schedulers and traffic types

Schedulers:

SP- Static PriorityEDF – Earliest Deadline FirstGPS – Generalized Processor Sharing

Traffic:

Regulated – leaky bucketOn-Off – On-off sourceFBM – Fractional Brownian Motion

C= 100 Mbps, e = 10-6


Statistical network calculus with min plus algebra

Statistical Network Calculus with Min-Plus Algebra

D(t)

A(t)

S(t)


Convolution and deconvolution operators

Convolution and Deconvolution operators

  • Convolution operation:

  • Deconvolution operation


Deterministic min network calculus

Deterministic (min,+)Network Calculus

Cruz `95:A service curve for a flow is a function S such that:

(min,+) results(Cruz, Chang, LeBoudec)

  • Output Envelope: is an envelope for the departures

  • Backlog bound: is an upper bound for the backlog

  • Delay bound: An upper bound for the delay is


Stochast network calculus

Stochast Network Calculus

An effective service curve for a flow is a function such that:

(min,+) results

  • Output Envelope: is an envelope for the departures with probability e

  • Backlog bound: is an upper bound for the backlog with probability e

  • Delay bound: An upper bound for the delay with probability eis


Statistical per flow service bounds

Sender

Receiver

Statistical Per-Flow Service Bounds

Allocated capacity C

  • Given:

  • Service guarantee to aggregate (C ) is known

  • Total Traffic is known

  • What is a lower bound on the service seen by a single flow?


Statistical per flow service bounds1

Sender

Receiver

Statistical Per-Flow Service Bounds

Allocated capacity C

Can show:

is an effective service curve for a flow where is a strong effective envelope and is a probabilistic bound on the busy period


Number of flows that can be admitted

Number of flows that can be admitted

Type 1 flows:

Goal: probabilisticdelay bound

d=10ms


Network service curves

Snet

Network Service Curves

S3

S1

Receiver

S2

Sender

Deterministic Network Service Curve (Cruz, Chang, LeBoudec):

If are service curves for a flow at nodes, then

Snet = S1 * S2 * S3

is a service curve for the entire network.


Network service curve in a stochastic calculus

Unfortunately, this network service is not very useful!

Finding a suitable network service curve has been a longstanding open problem. A solution is presented in an upcoming ACM Sigmetrics 05 paper.

Network Service Curve in a Stochastic Calculus

Network Service Curve:

If S1,, S2 , … SH , are effective service curves for a flow, then for all

.


Effective network service curve

Effective Network Service Curve

  • Revise the definition of the effective service curve to

  • Define

    Theorem: A network service curve is given by

    with

    where are free parameters


Application of network service curve

Application of Network Service Curve

  • Analyze end-to-end delay of through flows for Markov Modulated On-Off Traffic

  • Compare delay with network service curve to a summation of per-node bounds


Example

Example

  • C = 100 Mbos

  • Cross traffic = through traffic

  • e = 10-9

  • Peak rate: P = 1.5 MbpsAverage rate: r = 0.15 Mbps

  • T= 1/m + 1/l = 10 msec

  • Addition of per-node bounds grows O(H3)

  • Network service curve bounds grow O(H log H)


Conclusions

Conclusions

  • Presented aspects of stochastic network calculus

  • Preserves much (but not all) of the deterministic calculus

  • Can express many existing results on:

    • Deterministic calculus

    • Effective bandwidth

    • Other models (EBB, not shown)

  • Many open issues


Conclusions1

Conclusions


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