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Measuring Service in Multi-Class Networks. Aleksandar Kuzmanovic and Edward W. Knightly Rice Networks Group. QoS services SLA guaranteed rate Ex. Class X serviced at minimum rate R Relative performance Ex. Class X has strict priority over class Y

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Measuring Service in Multi-Class Networks

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Measuring service in multi class networks l.jpg

Measuring Service in Multi-Class Networks

Aleksandar Kuzmanovic and Edward W. Knightly

Rice Networks Group

Background l.jpg

QoS services

SLA guaranteed rate

Ex. Class X serviced at minimum rate R

Relative performance

Ex. Class X has strict priority over class Y

Statistical service

Ex. P(class X pkt. Delay>100ms)<.001

QoS mechanisms

Priority queues

Rate-based, delay-based...


Rate limiting...


Just add more bandwidth...


Need:Tools for network clients to assess the networks QoS capabilities

Inverse qos problem l.jpg

Inverse QoS Problem

  • Is a class rate limited?

  • What is the inter-class relationship?

    • Fair/weighted fair/strict priority

  • Is resource borrowing fully allowed or not?

  • Is the service’s upper bound identical to its lower bound?

  • What are the service’s parameters?

Applications network example l.jpg

Applications - Network Example

Providers reluctant to divulge precise QoS policy (if any...)

  • SLA validation for VPNs

    • Is the SLA fulfilled?

  • Capacity planning

    • What is the relationship

      among classes?

  • Edge-based admission control [CK00] and implementation [SSYK01]

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Performance Monitoring and Resource Management

  • Single WEB server

    • CPU resource sharing

    • Listen queue differentiation

    • Admission control

  • Distributed WEB server

    • Load balancing

  • Internet Data Center

    • Machine migration

Goal:Estimate a class’ net “guaranteed rate”

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“Off-Line” Solution is Simple

  • Consider a router with unknown QoS mechanisms

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“On-Line” Case: Operational Network

  • Undesirable to disrupt on-going services

    • High rate probes to detect inter-class relationships would degrade performance

  • Impossible to force other classes to be idle

    • … to detect policers

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System Model and Problem Formulation

  • Two stage server

    • Non-work conserving elements

    • Multi-class scheduler

  • Observations

    • Arrival and

      departure times

    • Class ID

    • Packet size

Determine l.jpg


  • Infer the service discipline

    • Most likely hypothesis among WFQ, EDF and SP

  • Detect the existence of non-work conserving elements

    • Rate limiters (ex. leaky bucket policers)

  • Estimate the system parameters

    • WFQ guaranteed rates, EDF deadlines, rate limiter values

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Remaining Outline

  • Inter-class Resource Sharing Theory

  • Empirical Arrival and Service Models

  • MLE of Parameters

  • EDF/WFQ/SP Hypothesis Testing

  • Simulation Results and Conclusions

Theoretical tool statistical service envelopes qk99 l.jpg

Theoretical Tool: Statistical Service Envelopes [QK99]

  • General statistical char. for a (virtual) minimally backlogged flow

  • Flows receive additional service beyond min rate

    • Function of other flow demand

    • Function of scheduler

  • General characterization of inter-class resource sharing

  • Framework for admission control for EDF/WFQ/SP

Strategy l.jpg


  • Inter-class theory

  • Key technique:

    • Passively monitor arrivals and services at edges

    • Devise hypothesis tests to jointly:

      • Detect most likely hypothesis

      • Estimate unknown parameters

Empirical arrival model l.jpg

E*( I ) = 3



t + I

Empirical Arrival Model

  • Envelopes characterize arrivals as a function of interval length

    • Statistical traffic envelope [QK99]

  • Empirical envelope - measure first two moments of arrivals over multiple time scales


assuming Gaussian distribution for B

Empirical service model l.jpg

Empirical Service Model

  • A real-world paradigm for statistical service envelope

  • Observe: Service can be measured only when packets are backlogged

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Empirical Service Distributions

  • For each class and time scale

    • Expected service distributions

    • Service measures (data)

  • Empirical service distributions

WFQ (400 ms) SP (400 ms)

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Parameter Estimation andScheduler Inference

  • GLRT for each time scale

  • Under MLE parameters for

    each scheduler

  • Choose most likely scheduler

  • Apply majority rule over all

    time scales

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EDF/WFQ Testing

  • Correctness ratio

    True WFQ  94%

    True EDF  100%

    Importance of time scales

  • Short time scales

    • Fluid vs. packet model

  • Long time scales

    • Ratio of delay shift and time scale decreases as time scale increases (d1=25ms)

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Measurable Regions

  • What if there is no traffic in particular class?

  • What traffic load “allows” inferences?

  • Region where we are able to estimate true value within 5%

  • Typical utilization should be > 62% for 1.5 Mbps link

  • Otherwise, active probing required

Conclusions l.jpg


  • Framework for clients of multi-class services to assess a system’s core QoS mechanisms

    • Scheduler type

    • Estimate parameters (both w-c and n-w-c)

  • General multiple time-scale traffic and service model to characterize a broad set of behaviors within a unified framework

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Measuring Service in Multi-Class Networks

Aleksandar Kuzmanovic and Edward W. Knightly

Rice Networks Group

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Ongoing Work

  • Unknown cross-traffic

    • Cannot monitor all

      systems inputs/outputs

    • Treat cross-traffic statistics

      as another unknown

  • Web servers

    • Evaluation of the framework in a single web server through trace driven simulations

    • Capacity is statistically characterized

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WFQ Parameter Estimation

  • Class 1: 65-68 flows

  • Class 2: 25-28 flows

  • Large windows improve confidence level

    • T=2sec: 95% in 11% of true value

    • T=10sec: 95% in 1.4% of true value

       Flow level dynamics & non-

      stationarities must be


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Rate Limited Class State Detection

  • Can include parameter r in service envelope equations for each class

    Importance of time scales

  • Example

    • Class based fair queuing

    • C=1.5Mbps, r=1Mbps

  • Probability decreases with time scale  higher errors when measuring multi-level leaky-buckets

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Generalized Likelihood Ratio Test

  • Detection with unknowns

  • Note: we do not find a single value of that maximizes likelihood ratio

  • Under mild conditions (as ), GLRT is Uniformly Most Powerful (maximizes the probability of detection)

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