Abstract
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.
Highlights
Computational grids have recently become a very popular environment for providing high-performance computing for computationally intensive applications
We show the performance of the generalized extremal optimization (GEO)-based scheduling algorithm for the scheduling problem considered in this paper
We will present the results of the conducted experiments using different instances of the scheduling problem and with the application of the GEO algorithm and the genetic algorithm (GA)
Summary
Computational grids have recently become a very popular environment for providing high-performance computing for computationally intensive applications. In our paper we will use a relatively new metaheuristic called generalized extremal optimization (GEO) (Sousa et al 2004) to solve a scheduling problem and compare its efficiency with that obtained using the most popular metaheuristic – a GA-based scheduling approach. Both GEO and GA belong to the same class of evolutionary algorithms, but they are inspired by different evolutionary processes.
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