A Hyper-Heuristic for Scheduling Independent Jobs in Computational Grids

In this paper we present the design and implementation of an hyper-heuristic for efficiently scheduling independent jobs in Computational Grids. An efficient scheduling of jobs to Grid resources depends on many parameters, among others, the characteristics of the Grid infrastructure and job characteristics (such as computing capacity, consistency of computing, etc.). Existing ad hoc scheduling methods (batch and immediate mode) have shown their efficacy for certain types of Grids and job characteristics. However, as stand alone methods, they are not able to produce the best planning of jobs to resources for different types of Grid resources and job characteristics. In this work we have designed and implemented a hyper-heuristic that uses a set of ad hoc (immediate and batch mode) scheduling methods to provide the scheduling of jobs to Grid nodes according to the Grid and job characteristics. The hyper-heuristic is a high level algorithm, which examines the state and characteristics of the Grid system (jobs and resources), and selects and applies the ad hoc method that yields the best planning of jobs to Grid resources. The resulting hyper-heuristic based scheduler can be thus used to develop networkaware applications that need efficient planning of jobs to resources. The Hyper-heuristic has been tested and evaluated in a dynamic setting through a prototype of a Grid simulator. The experimental evaluation showed the usefulness of the hyper-heuristic in planning of jobs to resources as opposed to planning without knowledge of the Grid and jobs characteristics.