Dynamic load balancing for I/O- and memory-intensive workload in clusters using a feedback control mechanism

Xiao Qin, Hong Jiang, Yifeng Zhu, David R. Swanson

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

One common assumption of the existing models of load balancing is that the weights of resources and I/O buffer size are statically configured. Though the static configuration of these parameters performs well in a cluster where the workload can be predicted, its performance is poor in dynamic systems where the workload is unknown. In this paper, a new feedback control mechanism is proposed to improve the overall performance of a cluster with I/O-intensive and memory-intensive workload. The mechanism dynamically adjusts the resource weights as well as the I/O buffer size. Results from a trace-driven simulation show that this mechanism is effective in enhancing the performance of a number of existing load-balancing schemes.

Original languageEnglish (US)
Pages (from-to)224-229
Number of pages6
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2790
Publication statusPublished - Dec 1 2004

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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