A hyper-heuristic for adaptive scheduling in Computational Grids

Title:

A hyper-heuristic for adaptive scheduling in Computational Grids

Creator:

Xhafa, Fatos

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:db3b088b-bfd3-4b7c-9c7d-ec8a3be9e8a0
uuid:db3b088b-bfd3-4b7c-9c7d-ec8a3be9e8a0

Subject:

Hyper-heuristic, scheduling, Grid computing, heuristic methods, immediate mode, batch mode, and Grid simulator

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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 resources and jobs (such as computing capacity, consistency of computing, workload, etc.). Moreover, these characteristics change over time due to the dynamic nature of Grid environment, therefore the planning of jobs to resources should be adaptively done. Existing ad hoc scheduling methods (batch and immediate mode) have shown their efficacy for certain types of resource 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 resources 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. The resulting hyper-heuristic based scheduler can be thus used to develop network-aware 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 for planning of jobs to resources as compared to planning without knowledge of the resource and job characteristics.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2007 Volume:17 | Number:6

Harvested from:

CDK

Metadata only:

false