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.
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