Load analysis and prediction for responsive interactive applications
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Load Analysis and Prediction for Responsive Interactive Applications. Peter A. Dinda David R. O’Hallaron Carnegie Mellon University. Overview. Load Analysis. Time Series Modelling. Measurement. History-based Load Prediction. Communication. Computation. Execution Time Predicition.

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Load Analysis and Prediction for Responsive Interactive Applications

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Load analysis and prediction for responsive interactive applications

Load Analysis and Predictionfor Responsive Interactive Applications

Peter A. Dinda

David R. O’Hallaron

Carnegie Mellon University


Overview

Overview

Load Analysis

Time Series Modelling

Measurement

History-based Load Prediction

Communication

Computation

Execution Time Predicition

Remote Execution

Best Effort Real-time

Responsive Interactive Applications

(eg, BBN OpenMap)


Openmap bbn

OpenMap (BBN)

“Move North”

Integrator

New map data

Choice of Host

Bounded

Response

Time

Replicated

Specialists

Terrain

Terrain

Terrain


Context

Context

Advanced Mobility Platform

Logistics Anchor Desk

METOC Anchor Desk

Other

Applications

JTF Planner

TRACE2ES

Applications

...

Frameworks

OpenMap (BBN)

QuO (BBN)

Adaptation

Load Prediction (CMU)

Prediction

Remos (CMU)

Measurement

Distributed system

Distributed system


Load analysis and prediction

Appropriate

Time Series

Models

Load Trace

Collection

Statistical

Analysis

Fitted

Models

Evaluation/

Comparison

On-line

Predictors

Load Analysis and Prediction

  • Goal:accurate short term predictions

    • Few seconds for non-stale data

  • Evaluation/comparison issues

    • Load generation vs. Load prediction

      • Have to discover which properties are important

    • Performance measure

      • Mean squared prediction error

      • Lack of lower bound to compare against

      • Simple, reasonable algorithm for comparison


Load trace analysis

Load Trace Analysis

  • Digital Unix one minute load average

  • Four classes of hosts (38 machines)

  • 1 Hz sample rate, >one week traces, two sets at different times of the year

  • Analysis results to appear in LCR98

  • Load is self-similar

  • Load exhibits epochal behavior


Self similarity statistics

Self-similarity Statistics


Why is self similarity important

Why is Self-Similarity Important?

  • Complex structure

    • Not completely random, nor independent

    • Short range dependence

      • Excellent for history-based prediction

    • Long range dependence

      • Possibly a problem

  • Modeling Implications

    • Suggests models

      • ARFIMA, FGN, TAR


Load exhibits epochal behavior

Load Exhibits Epochal Behavior


Epoch length statistics

Epoch Length Statistics


Why is epochal behavior important

Why is Epochal Behavior Important?

  • Complex structure

    • Non-stationary

  • Modeling Implications

    • Suggests models

      • ARIMA, ARFIMA, etc.

      • Non-parametric spectral methods

    • Suggests problem decomposition


Time series prediction of load

Time Series Prediction of Load

Nonlinear

Linear

Markov

TAR

Parametric

Non-parametric

Stationary

Non-stationary

ARMA, AR, MA

Self-similar

Non-self-similar

“Best Mean”

ARFIMA, FGN

ARIMA


T 1 predictions

t+1 Predictions


T 5 prediction

t+5 Prediction


Conclusions

Conclusions

  • Load has structureto exploit for prediction

    • Structure is complex (self-similarity, epochs)

  • Simple time series models are promising

    • Benefits of more sophisticated models are unclear

  • Current research questions

    • What are the benefits of more sophisticated models?

    • How to characterize prediction error to user?

    • Is there a measure of inherent predictability?

    • How to incorporate load prediction into systems?


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