Measuring Service in MultiClass Networks

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

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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

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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

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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

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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

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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

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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
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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 (d125ms)

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

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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

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Measuring Service in Multi-Class Networks
Aleksandar Kuzmanovic and Edward W. Knightly Rice
Networks Group
http//www.ece.rice.edu/networks
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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
• T2sec 95 in 11 of true value
• T10sec 95 in 1.4 of true value
• ? Flow level dynamics non-
• stationarities must be
• considered

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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
• C1.5Mbps, r1Mbps
• 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)