Static strategy and dynamic adjustment: An effective method for Grid task scheduling

Authors:
Peijie Huang;Hong Peng;Piyuan Lin;Xuezhen Li
Affiliations:
College of Informatics, South China Agricultural University, Guangzhou 510642, PR China;College of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, PR China;College of Informatics, South China Agricultural University, Guangzhou 510642, PR China;Department of Computer and Information Engineering, Guangdong Technical College of Water Resources and Electric Engineering, Guangzhou 510635, PR China
Venue:
Future Generation Computer Systems
Year:
2009

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Abstract

Task scheduling is the key technology in Grid computing. Hierarchical organization is suitable for the computational Grid because of the dynamic, heterogeneous and autonomous nature of the Grid. Although a number of Grid systems adopt this organization, few of them has dealt with task scheduling for the hierarchical architecture. In this paper, we present an effective method, fully taking into account both historical Grid trade data and dynamic variation of the Grid market to improve the task scheduling for a hierarchical Grid market. The main idea of the proposed method is a combination of an off-line static strategy using time series prediction and an on-line dynamic adjustment using reinforcement learning. The superiority of this new scheduling algorithm, in improving the inquiry efficiency for resource consumers, getting better load balancing of the whole hierarchical Grid market, and achieving higher success rate of the Grid service request, is demonstrated by simulation experiments.