measurement-based admission control algorithms

IntroductionThree Different MBAC AlgorithmsPractical Implementations of CAC

Conclusions

Measurement-Based Admission Control Algorithms

Bob Callaway Joni Finlon Susan Stewart

North Carolina State UniversityCSC/ECE 776 - Performance Evaluation of Computer Networks

Student Research Presentation

April 27, 2004

Bob Callaway, Joni Finlon, Susan Stewart Measurement-Based Admission Control Algorithms


Conclusions

Presentation Outline

1 IntroductionGoals of Connection Admission ControlTypes of CAC AlgorithmsEffective BandwidthOverview of Measurement-Based Admission Control

2 Three Different MBAC AlgorithmsCLR Upperbound FormulaDecision-Theoretic ApproachMBAC with Aggregate Traffic EnvelopesComparison of Algorithm Characteristics

3 Practical Implementations of CACVoice Over IP (VoIP)Video ConferencingMultimedia Resource Control

4 ConclusionsBob Callaway, Joni Finlon, Susan Stewart Measurement-Based Admission Control Algorithms


Conclusions

Goals of Connection Admission ControlTypes of CAC AlgorithmsEffective BandwidthOverview of Measurement-Based Admission Control

Section Outline






Conclusions


Congestion Control

The Role of Congestion Control

−→ To protect the network and the user in order to achievenetwork performance objectives and optimize the usage of networkresources

Congestion control can be either preventive or reactive

Connection admission control is a preventive congestioncontrol method



Conclusions


Goals of Connection Admission Control

Three Main Goals of Connection Admission Control

Protect the network by preventing congestion

Meet QoS requirements of all connections

Obtain maximum statistical multiplexing gain

Uses an algorithm to decide whether to accept or reject arequest for a new connection to the network

Connection acceptance is based on two questions:

Does the new connection affect the QoS currently beingcarried by the switch?Can the switch provide the QoS requested by the newconnection?



Conclusions


Nonstatistical Connection Admission Control

Also called deterministic allocation or peak bandwidthallocation

Requires that the peak rate of the connection be reserved fora particular source

Advantages

It is easy to make adecision about whetherto accept or reject a newconnection

Disadvantages

The network will beunderutilized most of thetime (unless users aretransmitting CBR traffic)



Conclusions


Statistical Connection Admission Control

Allocated bandwidth is less than the peak rate of a source

Advantages

Network resources will bebetter utilized

Disadvantages

More difficult to implement

Can be CPU intensive

0 100 200 300 400 500 600 700 800 900 10000

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5x 105 Plot of Traffic Trace vs. Estimated Effective Bandwidths − Meter Implementation: 1 Stream

Time (sec)

Thro

ughp

ut (b

ytes

/sec

)

Actual TrafficGaussian MethodCourcoubetis MethodNorros Method



Conclusions


Motivation of Effective Bandwidth

How do we determine the number of connections to admit to thenetwork to maximize efficiency by using statistical multiplexing?

Effective Bandwidth!

Effective bandwidth estimates the amount of bandwidth thatshould be allocated to a class of network traffic in order tomeet a QoS requirement, such as a delay or loss constraint

C =1

θtlog E

[eθX [0,t]

]



Conclusions


Measurement-Based Admission Control: An Overview

Why use a measurement-based scheme?

Non-measurement-based methods use the worst case boundsand result in low utilization of the network

Zero (or a very small number of) a priori assumptions must bemade about the arrival process of the traffic, sincemeasurements are used to describe the traffic

Useful for services that do not require tight guarantees, rathermore relaxed service commitments

Results in high network utilization and an acceptable level ofservice



Conclusions

CLR Upperbound FormulaDecision-Theoretic ApproachMBAC with Aggregate Traffic EnvelopesComparison of Algorithm Characteristics

Section Outline






Conclusions


Rate Envelope Multiplexing vs. Rate Sharing Multiplexing

Rate Envelope Multiplexing (REM)

Buffering effect is not taken into account when evaluatingcell-level performance

Queueing process at the output port buffer is not considered

Provides for faster computations

Rate Sharing Multiplexing (RSM)

Requires model for queueing process at output port buffer

Can achieve higher efficiency than REM methods

Computationally complex; also dependent on input trafficmodel



Conclusions


Dynamic CAC in ATM Networks

B =

L∞∑

k=0

[k − Cs

L

]+

p(·; t) ? θn+1(k)

a(t) + san+1

Algorithm Overview

Independent of the classification of calls and does not use amodel for the arrival process

Makes admission decision by comparing measured upperbound of loss probability against QoS standard

Uses measurements to estimate the pdf of the number ofarriving cells per call in a renewal period



Conclusions


Dynamic CAC in ATM Networks (continued)

Algorithm Details

Uses exponential weighting to increase/decrease importanceof measurements/signalled parameters

If a new call request is received within the renewal period, thepdf is shifted by convolution to take the new worst-case cellarrival distribution into consideration



Conclusions


A Decision-Theoretic Approach to CAC in ATM Networks

Algorithm Overview

Key aspect of algorithm istime scale decomposition

Bayesian decision-theoreticframework parameterizes thetradeoff between the costsand benefits of accepting anadditional call into thenetwork

Uses a measure of burstiness(peak to mean ratio) incalculations



Conclusions


A Decision-Theoretic Approach to CAC in ATM Networks

Algorithm Details

Makes the admission controldecision by comparing theinstantaneous load to a giventhreshold

Uses the control parameter y torepresent the tradeoff betweenutilization and cell loss

Cell loss ratio is effected by rate ofchange of the parameter p.

s =

∫ ∫[U(p, λ)− (y − 1)M(p, λ)] f (p, λ)dpdλ



Conclusions


MBAC with Aggregate Traffic Envelopes

Algorithm Overview

Measures the maximal rateenvelope of the aggregate traffic,since the extreme values of theaggregate flow are likely to lead tolosses

Takes measurements in slottedtime, and computes statistics ofthe envelope over M time scales

Can make admission decision withregards to a specified loss rateand/or a given delay bound

R1k =

1

kτmax

t−T+k≤s≤t

s∑u=s−k+1

au



Conclusions


MBAC with Aggregate Traffic Envelopes

Algorithm Details

A confidence level is derived such that for a given α, thetraffic will not exceed the maximal envelope

The admission control decision is made by testing the newaggregate envelope against the delay/loss criterion

In the worst case, this algorithm bounds the loss probability orthe maximum delay; in the best case, significant statisticalmultiplexing gains can be realized

maxk=1,2,...,T

{kτ(Rk + rk + ασk − C )

}≤ Cd

RT + rT + ασT ≤ C



Conclusions


Comparison of Algorithm Characteristics

Method Measurement Decision Memory Model AssumedSS91 O(M) O(M) O(M) None

GKK95 O(1) O(1) O(N) Poisson/ExponentialQK01 O(T) O(1) O(T) None

M = Number of Bins in Distribution N = Number of Connections T = Number of Time Slots

Comparisons

REM models are less dependent on a priori traffic assumptions

SS91 does not make any traffic assumptions, but it has thehighest computational costs

QK01 has been shown to be practically implementable intestbed experiments using RSVP



Conclusions

Voice Over IP (VoIP)Video ConferencingMultimedia Resource Control

Section Outline






Conclusions


Example of CAC: Voice Over IP (VoIP)



Conclusions


Example of MBAC: Voice Over IP (VoIP)

VoIP Examples

Cisco

IOS SoftwareGateways



Conclusions



Cisco



Conclusions



VoIP Examples

NexTone

Multiprotocol Session Controller (MSC)



Conclusions


Example of CAC: Video Conferencing

Video Conferencing Examples

Polycom

PathNavigatorTM Premier CallProcessing Server Solution

Cisco

Multimedia Conference ManagerH.323 Gatekeeper



Conclusions


Example of CAC: Multimedia Resource Control

Alcatel 5430 Session Resource Broker



Conclusions

References

Section Outline






Conclusions

References

Conclusions

Connection Admission Control

Some type of CAC is needed to ensure the QoS of existingconnections and to control additional connections to thenetwork

Measurement-based Admission Control Algorithms

How well does it ensure that the service commitments areupheld?

How high can network utilization reach while still upholdingQoS commitments?

Do the benefits of statistical multiplexing outweigh the cost ofonline measurements and other statistical computations?



Conclusions

References

H. Saito, K. Shiomoto.Dynamic Call Admission Control in ATM Networks.IEEE Journal on Selected Areas in Communications, 1991.

R. Gibbens, F. Kelly, P. Key.A Decision-Theoretic Approach to Call Admission Control inATM Networks.IEEE Journal on Selected Areas in Communications, 1995.

J. Qiu, E. Knightly.Measurement-Based Admission Control with Aggregate TrafficEnvelopes.IEEE/ACM Transactions on Networking, April 2001.

H. Perros, K. ElsayedCall Admission Control Schemes: A Review.IEEE Communications Magazine, November 1996.



Conclusions

References

K. Shiomoto, N. Yamanaka, T. Takahashi.Overview of Measurement-Based Connection AdmissionControl Methods in ATM Networks.IEEE Communication Surveys, First Quarter 1999.

E. Knightly, N. Shroff.Admission Control for Statistical QoS: Theory and Practice.IEEE Network, March/April 1999.

S. Jamin, P.B. Danzig, S.J. Shenker, L. ZhangA Measurement-based Admission Control Algorithm forIntegrated Services Packet Networks (Extended Version)IEEE/ACM Transactions on Networking, February 1997

S. Jamin, P.B. Danzig, S.J. ShenkerComparison of Measurement-based Admission ControlAlgorithms for Controlled-Load ServiceIEEE INFOCOM, 1997


measurement-based admission control algorithms

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