Estimation of network characteristics and its use in improving performance of network applications

Ahmed Ashir, Glenn Mansfield, Norio Shiratori

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Network applications such as FTP, WWW, Mirroring etc. are presently operated with little or no knowledge about the characteristics of the underlying network. These applications could operate more efficiently if the characteristics of the network are known and/or are made available to the concerned application. But network characteristics are hard to come by. The IP Performance Metrics working group (IETP-IPPM-WG) [6] is working on developing a set of metrics that will characterize Internet data delivery services (networks). Some tools are being developed for measurements of these metrics [5]. These generally involve active measurements or require modifications in applications [16]. Both techniques have their drawbacks. In this work, we show a new and more practical approach of estimating network characteristics. This involves gathering and analyzing the network's experience. The experience is in the form of traffic statistics, information distilled from management related activities and ubiquitously available logs (squid access logs, mail logs, ftp logs etc.) of network applications. An analysis of this experience provides an estimate of the characteristics of the underlying network. To evaluate the concept we have developed and experimented with a system wherein the network characteristics are generated by analyzing the logs and traffic statistics. The network characteristics are made available to network clients and administrators by Network Performance Metric (NPM) servers. These servers are accessed using standard network management protocols. Results of the evaluation are presented and a framework for efficient operation of network operations, using the network characteristics is outlined.

Original languageEnglish
Pages (from-to)747-755
Number of pages9
JournalIEICE Transactions on Information and Systems
VolumeE82-D
Issue number4
Publication statusPublished - Jan 1 1999
Externally publishedYes

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All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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