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Measuring Service in MultiClass Networks

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leaky bucket policers) Estimate the system parameters ... decreases with time scale higher errors when measuring multi-level leaky-buckets ... – PowerPoint PPT presentation

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Title: Measuring Service in MultiClass Networks


1
Measuring Service in Multi-Class Networks
Aleksandar Kuzmanovic and Edward W. Knightly Rice
Networks Group
http//www.ece.rice.edu/networks
2
Background
  • 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. Delaygt100ms)lt.001
  • QoS mechanisms
  • Priority queues
  • Rate-based, delay-based...
  • Policing
  • Rate limiting...
  • Over-engineering
  • Just add more bandwidth...

Need Tools for network clients to assess the
networks QoS capabilities
3
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 services upper bound identical to its
    lower bound?
  • What are the services parameters?

4
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

5
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
6
Off-Line Solution is Simple
  • Consider a router with unknown QoS mechanisms

7
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

8
System Model and Problem Formulation
  • Two stage server
  • Non-work conserving elements
  • Multi-class scheduler
  • Observations
  • Arrival and
  • departure times
  • Class ID
  • Packet size

9
Determine...
  • 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

10
Remaining Outline
  • Inter-class Resource Sharing Theory
  • Empirical Arrival and Service Models
  • MLE of Parameters
  • EDF/WFQ/SP Hypothesis Testing
  • Simulation Results and Conclusions

11
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

12
Strategy
  • Inter-class theory
  • Key technique
  • Passively monitor arrivals and services at edges
  • Devise hypothesis tests to jointly
  • Detect most likely hypothesis
  • Estimate unknown parameters

13
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

Goal
assuming Gaussian
distribution for B
14
Empirical Service Model
  • A real-world paradigm for statistical service
    envelope
  • Observe Service can be measured only when
    packets are backlogged

15
Empirical Service Distributions
  • For each class and time scale
  • Expected service distributions
  • Service measures (data)
  • Empirical service distributions

WFQ (400 ms) SP (400 ms)
16
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

17
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 (d125ms)

18
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 gt 62 for 1.5 Mbps
    link
  • Otherwise, active probing required

19
Conclusions
  • Framework for clients of multi-class services to
    assess a systems 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

20
Measuring Service in Multi-Class Networks
Aleksandar Kuzmanovic and Edward W. Knightly Rice
Networks Group
http//www.ece.rice.edu/networks
21
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

22
WFQ Parameter Estimation
  • Class 1 65-68 flows
  • Class 2 25-28 flows
  • Large windows improve confidence level
  • T2sec 95 in 11 of true value
  • T10sec 95 in 1.4 of true value
  • ? Flow level dynamics non-
  • stationarities must be
  • considered

23
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
  • C1.5Mbps, r1Mbps
  • Probability decreases with time scale ? higher
    errors when measuring multi-level leaky-buckets

24
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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