Advanced job scheduler based on Markov availability model and resource selection in desktop grid computing environment

Eun Joung Byun, Sung Jin Choi, Hong Soo Kim, Chong Sun Hwang, Sang-Geun Lee

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This chapter reviews dynamism in desktop Grid computing and explains the advanced stochastic scheduling scheme with the Markov Job Scheduler based on Availability (MJSA) in the environment. In recent years, Grid computing [1] has received considerable interest in the field of academics and enterprise. Numerous attempts have been made to organize cost efficient large-scale Grid computing. Desktop Grid computing [13,19,2] is a more flexible paradigm that is used to achieve high performance and high throughput with desktop resources that are less stable and has more inferior performance compared to traditional Grid. It is comprised of a diverse set of desktops interconnected with various network forms ranging from Local Area Network (LAN) to the Internet. Desktop Grid system has played a leading role in the development of large scale aggregated computing power harvested from the edge of the Internet at lower cost. The main goals of the system are to accomplish high throughput and performance by mobilizing the potential colossal computational resources of idle desktops. However, since a desktop peer is a fluctuating resource that connects to the system, performs computations and disconnects to the network at will, desktop volatility makes the system unstable and unreliable. To develop a reliable desktop Grid computing system, a scheduling scheme must consider the dynamic nature (i.e., volatility) of volunteers and a resource selection scheme should adapt to such a dynamic environment, as the selection is getting complicated due to the uncertain behavior of desktops. This chapter demonstrates desktop state change modelling and an advanced resource selection scheme, Selection of Credible Resource with Elastic Window (SCREW), to choose reliable resources in dynamic computational desktop Grid environments. Markov modelling of the dynamic state turning provides understanding of the pattern of desktop behavior while SCREW selects qualified desktops that satisfy time requirements to complete given workloads and adapts to the needs of the user and the application on the fly.

Original languageEnglish
Pages (from-to)153-171
Number of pages19
JournalStudies in Computational Intelligence
Volume146
DOIs
Publication statusPublished - 2008 Sep 18

Keywords

  • Desktop grid computing
  • Hidden Markov model
  • Markov modelling
  • Resource selection scheme
  • Stochastic scheduling

ASJC Scopus subject areas

  • Artificial Intelligence

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