An Evaluation of Linear Models for Host Load Prediction

Abstract

This paper evaluates linear models for predicting the Digital Unix five-second load average from 1 to 30 seconds into the future. A detailed statistical study of a large number of load traces leads to consideration of the Box-Jenkins models (AR, MA, ARMA, ARIMA), and the ARFIMA models (due to self-similarity.) These models, as well as a simple windowed-mean scheme, are evaluated by running a large number of randomized testcases on the load traces. The main conclusions are that load is consistently predictable to a useful degree, and that the simpler models such as AR are sufficient for doing this prediction.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1998
Accession Number
ADA358577

Entities

People

  • David R. O'hallaron
  • Peter A. Dinda

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Application Software
  • Autocorrelation
  • Computer Science
  • Data Science
  • Databases
  • Distribution Functions
  • Frequency
  • Information Science
  • Lead Time
  • Maximum Likelihood Estimation
  • Measurement
  • Operating Systems
  • Predictive Modeling
  • Scheduling (Production)
  • Statistical Analysis
  • White Noise

Readers

  • Computational Modeling and Simulation
  • Computer Science.
  • Statistical inference.