An engineering approach to dynamic prediction of network performance from application logs

Zalal Uddin Mohammad Abusina, Salahuddln Muhammad Salim Zabir, Ahmed Ashir, Debasish Chakraborty, Takuo Suganuma, Norio Shiratori

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

Network measurement traces contain information regarding network behavior over the period of observation. Research carried out from different contexts shows predictions of network behavior can be made depending on network past history. Existing works on network performance prediction use a complicated stochastic modeling approach that extrapolates past data to yield a rough estimate of long-term future network performance. However, prediction of network performance in the immediate future is still an unresolved problem. In this paper, we address network performance prediction as an engineering problem. The main contribution of this paper is to predict network performance dynamically for the immediate future. Our proposal also considers the practical implication of prediction. Therefore, instead of following the conventional approach to predict one single value, we predict a range within which network performance may lie. This range is bounded by our two newly proposed indices, namely, Optimistic Network Performance Index (ONPI) and Robust Network Performance Index (RNPI). Experiments carried out using one-year-long traffic traces between several pairs of real-life networks validate the usefulness of our model.

Original languageEnglish
Pages (from-to)151-162
Number of pages12
JournalInternational Journal of Network Management
Volume15
Issue number3
DOIs
Publication statusPublished - May 2005
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Networks and Communications

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