Job online scheduling within dynamic grid environment

This paper proposes the idea of adaptive job scheduling algorithm by using hybrid Ant Colony Optimization (ACO) and Tabu algorithms. The idea behind the scheduling algorithm is evaluation of completion time of jobs in a service Grid. The algorithm comprises of two main techniques; first of all, Grid Information Service (GIS) collects information from each grid node, ACO evaluates complete time of jobs in possible grid nodes and then assigns job to appropriate grid node. ACO is used to minimize the average completion time of jobs through optimal job allocation on each node as well. While, Tabu algorithm is used to adjust performance of grid system because online jobs are submitted to grid system from time to time. This paper shows that the algorithm can find an optimal processor for each machine to allocate to a job that minimizes the tardiness time of a job when the job is scheduled in the system.

[1]  Ladislau Bölöni,et al.  A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems , 2001, J. Parallel Distributed Comput..

[2]  Luděk Matyska,et al.  Model of Grid Scheduling Problem , 2005 .

[3]  Asser N. Tantawi,et al.  Performance Analysis of Parallel Processing Systems , 1988, IEEE Trans. Software Eng..

[4]  Uwe Schwiegelshohn,et al.  Theory and Practice in Parallel Job Scheduling , 1997, JSSPP.

[5]  David Fernández-Baca,et al.  Allocating Modules to Processors in a Distributed System , 1989, IEEE Trans. Software Eng..

[6]  P. Sadayappan,et al.  Distributed job scheduling on computational Grids using multiple simultaneous requests , 2002, Proceedings 11th IEEE International Symposium on High Performance Distributed Computing.

[7]  Abdul Hanan Abdullah,et al.  Meta-scheduler in Grid environment with multiple objectives by using genetic algorithm , 2006 .

[8]  Hui Yan,et al.  An improved ant algorithm for job scheduling in grid computing , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[9]  Rajkumar Buyya,et al.  A taxonomy and survey of grid resource management systems for distributed computing , 2002, Softw. Pract. Exp..

[10]  Domenico Talia,et al.  Modeling and Supporting Grid Scheduling , 2008, Journal of Grid Computing.

[11]  Ramin Yahyapour,et al.  Design and evaluation of job scheduling strategies for grid computing , 2000, GRID.

[12]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[13]  Dror G. Feitelson,et al.  Packing Schemes for Gang Scheduling , 1996, JSSPP.

[14]  Keqin Li,et al.  Job scheduling and processor allocation for grid computing on metacomputers , 2005, J. Parallel Distributed Comput..

[15]  Jizhou Sun,et al.  An Extendable Grid Simulation Environment Based on GridSim , 2003, GCC.

[16]  Pierluigi Ritrovato,et al.  A static mapping heuristics to map parallel applications to heterogeneous computing systems: Research Articles , 2005 .

[17]  Dalibor Klusácek,et al.  Alea - Grid Scheduling Simulation Environment , 2007, PPAM.

[18]  Hongzhang Shan,et al.  Job Superscheduler Architecture and Performance in Computational Grid Environments , 2003, ACM/IEEE SC 2003 Conference (SC'03).

[19]  Rajkumar Buyya,et al.  GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing , 2002, Concurr. Comput. Pract. Exp..

[20]  Francine Berman,et al.  Heuristics for scheduling parameter sweep applications in grid environments , 2000, Proceedings 9th Heterogeneous Computing Workshop (HCW 2000) (Cat. No.PR00556).