Multi-Objective Job Scheduling Model Based on NSGA-Ⅱ for Grid Computing

김솔지; 김태호; LEE， Hong Chul

Grid computing is a new generation computing technology which organizes virtual high-performance computing system by connecting and sharing geographically distributed heterogeneous resources, and performing large-scaled computing operations. In order to maximize the performance of grid computing, job scheduling is essential which allocates jobs to resources effectively. Many studies have been performed which minimize total completion times, etc. However, resource costs are also important, and through the minimization of resource costs, the overall performance of grid computing and economic efficiency will be improved. So in this paper, we propose a multi-objective job scheduling model considering both time and cost. This model derives from the optimal scheduling solution using NSGA-Ⅱ, which is a multi objective genetic algorithm, and guarantees the effectiveness of the proposed model by executing experiments with those of existing scheduling models such as Min-Min and Max-Min models. Through experiments, we prove that the proposed scheduling model minimizes time and cost more efficiently than existing scheduling models.

Citation status

KCI Citation Counts (1)
열림/닫힘 버튼
REFERENCES (18)
열림/닫힘 버튼
* 2021년 이후 발행 논문의 참고문헌은 현재 구축 중입니다.

[book] I. Foster / 1998 / The Grid: Blueprint for a New Computing Infrastructure / Morgan Kaufmann Publishers

[confproc] Junzhou Luo / 2004 / Resource management and task scheduling in grid computing / Proceedings of The 8th International Conference on Computer Supported Cooperative Work in Design : 431~436

[journal] O. H. Ibarra / 1977 / Heuristic Algorithm for Scheduling Independent Tasks on Nonidentical Processors / Journal of the ACM 24(2) : 280~289

[confproc] M. Maheswaran / 1999 / Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems / Proceedings of 8th Heterogeneous Computing Workshop : 30~44

[confproc] L. D. Briceno / 2007 / Study of an Iterative Technique to Minimize Completion Times of Non-Makespan Machines / Proceedings of 2007 IEEE International Parallel and Distributed Processing Symposium : 1~14

[confproc] K. Etminani / 2007 / A Min-Min Max-Min selective algorithm for grid task scheduling / Proceedings of the 3rd IEEE/IFIP International Conference in Central Asia on Internet : 1~7

[confproc] M. Y. Wu / 2000 / Segmented Min-Min: A Static Mapping Algorithm for Meta-Tasks on Heterogeneous Computing Systems / 9th IEEE Heterogeneous Computing Workshop : 375~385

[journal] He XiaoShan / 2003 / QoS Guided Min-Min Heuristic for Grid Task Scheduling / Journal of Computer Science and Technology 18(4) : 442~451

[confproc] Z. Xu / 2003 / Ant algorithm-based task scheduling in grid computing / Proceedings of 2003 IEEE Canadian Conference on Electrical and Computer Engineering : 1110~2003

[confproc] S. Fidanova / 2006 / Simulated Annealing for Grid Sched uling Problem / Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA'06) : 41~45

[report] J. Andersson / 2000 / A survey of multiobjective optimiz ation in engineering design / Linkoping University

[book] Kim Yeo Geun / 1997 / Meta Heuristics / Young-Ji Books : 3~150

[journal] IEEE Transactions on Evolutionary Computation / 2002 / A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ / IEEE Transactions on Evolutionary Computation,Vol.6,No.2,pp.182-197,Apr 6(2) : 182~197

[journal] S. T. Khu / 2005 / Multi-objective calibr ation with Pareto preference ordering: An application to rainfall-runoff model calibration / Water Resources Research 41(3)

[web] / JMetal / http://jmetal.sourceforge.net

[web] / GridSim / http://www.gridbus.org/gridsim

[journal] R. Buyya / 2002 / GridSim: A Toolkit for the Modeling and Simulation of Distributed Resource Management and Scheduling for Grid Computing / The Journal of Concurrency and Computation: Practice and Experience 14(13) : 1175~1220

[web] / WWG testbed / http://www.buyya.com/ecogrid/wwg

KJC Korea
Journal Central

Statistics

Tools

Sharing

Issue List

Citation status