A Bee Colony based optimization approach for simultaneous job scheduling and data replication in grid environments

  • Authors:

  • Javid Taheri;Young Choon Lee;Albert Y. Zomaya;Howard Jay Siegel

  • Affiliations:

  • Center for Distributed and High Performance Computing, School of Information Technologies, J12, The University of Sydney, Sydney, NSW 2006, Australia;Center for Distributed and High Performance Computing, School of Information Technologies, J12, The University of Sydney, Sydney, NSW 2006, Australia;Center for Distributed and High Performance Computing, School of Information Technologies, J12, The University of Sydney, Sydney, NSW 2006, Australia;Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373, USA and Department of Computer Science, Colorado State University, Fort Collins, CO 80523 ...

  • Venue:
  • Computers and Operations Research
  • Year:
  • 2013

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Abstract

This paper presents a novel Bee Colony based optimization algorithm, named Job Data Scheduling using Bee Colony (JDS-BC). JDS-BC consists of two collaborating mechanisms to efficiently schedule jobs onto computational nodes and replicate datafiles on storage nodes in a system so that the two independent, and in many cases conflicting, objectives (i.e., makespan and total datafile transfer time) of such heterogeneous systems are concurrently minimized. Three benchmarks - varying from small- to large-sized instances - are used to test the performance of JDS-BC. Results are compared against other algorithms to show JDS-BC's superiority under different operating scenarios. These results also provide invaluable insights into data-centric job scheduling for grid environments.