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

Javid Taheri*, Young Choon Lee, Albert Y. Zomaya, Howard Jay Siegel

*Corresponding author for this work

Research output: Contribution to journalArticle

58 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1564-1578
Number of pages15
JournalComputers and Operations Research
Issue number6
Publication statusPublished - Jun 2013
Externally publishedYes



  • Bee colony optimization
  • Data replication
  • Grid computing
  • Job scheduling
  • Resource allocation

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