Scheduling parallel batch jobs in grids with evolutionary metaheuristics
In this paper we propose an efficient offline job scheduling algorithm working in a grid environment that is based on a relatively new evolutionary metaheuristic called generalized extremal optimization (GEO). We compare our experimental results with those obtained using a very popular evolutionary metaheuristic, the genetic algorithm (GA). The scheduling algorithm implies two-stage scheduling. In the first stage, the algorithm allocates jobs to suitable machines of a grid; GEO/GA is used for this purpose. In the second stage, jobs are independently scheduled on each machine; this is performed with a variant of a list scheduling algorithm. Both GEO and GA belong to the class of evolutionary algorithms, but GEO is much simpler and requires the tuning of only one parameter, whereas GA requires the tuning of several parameters. The results of the experimental study show that GEO, despite its simplicity, outperforms the GA in a whole range of scheduling instances and is much easier to use.
generalized extremal optimization genetic algorithm two-stage scheduling grid computing parallel job