A Bio-inspired Job Scheduling Algorithm for Monte Carlo Applications on a Computational Grid
نویسندگان
چکیده
In this paper, we present a bio-inspired job scheduling mechanism that enables the adaptation of largescale, naturally parallel and compute-intensive Monte Carlo tasks to clustered computational farms. Examples of such farms include large-scale computational grids, with heterogeneous and dynamic performance. The kernel of this scheduling mechanism is a swarm intelligent algorithm, which is inspired from ants’ behavior in their social insect colony. We apply the bio-inspired adaptive mechanism in a simulated computational grid and compare it with static scheduling algorithms. Our preliminary results show good performance, adaptability, and robustness in a dynamic computational grid with respect to a simple scheduling mechanism and the N-out-of-M scheduling mechanism.
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