Task Scheduling for Computational Grids Using NSGA II with Fuzzy Variance Based Crossover

Scheduling algorith ms have essential ro le in co mputational grids for managing jobs, and assigning them to appropriate resources. An efficient task scheduling algorith m can reduce the total Time and Price for jobs execution and improve the Load balancing between resources in the grid. In this paper, we address scheduling problem of independent tasks in the market-based grid environment. We use NSGA-II to optimize task scheduling problem in grid. For decreasing computation, we considered Load balancing problem and improved it in task scheduling indirectly using fu zzy system without imp lementing third objective function. For the first time, we proposed Variance based Fuzzy Crossover operator for this purpose and more variety in Pareto-optimal solutions. Two functions are defined to generate two inputs for fuzzy system. Variance of Costs and presence of resources in scheduling are used to specify probability of crossover intelligently. Second fuzzy function with cooperation of Makespan objective satisfies load balancing objective indirect ly. Our method conducts the algorith m toward best and most appropriate solutions with load balancing in less iteration. Results obtained proved that our innovative algorithm converges to Pareto-optimal solutions faster and with more quality.

[1]  Ming Wu,et al.  Quality of Service of Grid Computing: Resource Sharing , 2007, Sixth International Conference on Grid and Cooperative Computing (GCC 2007).

[2]  Fatma A. Omara,et al.  Genetic algorithms for task scheduling problem , 2010, J. Parallel Distributed Comput..

[3]  K..  Multiobjective Optimization Using a Pareto Differential Evolution Approach , 2022 .

[4]  Jingyi Ma.  A Novel Heuristic Genetic Load Balancing Algorithm in Grid Computing , 2010, 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics.

[5]  M. C. Bhuvaneswari,et al.  NSGA - II with Controlled Elitism for Scheduling Tasks in Heterogeneous Computing Systems , 2011 .

[6]  Albert Y. Zomaya,et al.  Observations on Using Genetic Algorithms for Dynamic Load-Balancing , 2001, IEEE Trans. Parallel Distributed Syst..

[7]  Bora Uçar,et al.  Task assignment in heterogeneous computing systems , 2006, J. Parallel Distributed Comput..

[8]  G. Sudha Sadasivam,et al.  An Efficient Approach to Task Scheduling in Computational Grids , 2010, Int. J. Comput. Sci. Appl..

[9]  Yuhang Yang,et al.  A Hybrid Load Balancing Strategy of Sequential Tasks for Computational Grids , 2009, 2009 International Conference on Networking and Digital Society.

[10]  Shiv Prakash,et al.  Load Balancing in Computational Grid Using Genetic Algorithm , 2012 .

[11]  SOUMYAKANT PADHEE,et al.  Multi-objective parametric optimization of powder mixed electro-discharge machining using response surface methodology and non-dominated sorting genetic algorithm , 2012, Sadhana.

[12]  R. K. McConnell,et al.  Load Balancing , 2021, Encyclopedia of Algorithms.

[13]  N. Madavan.  Multiobjective optimization using a Pareto differential evolution approach , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[14]  Kalyanmoy Deb,et al.  Multiobjective optimization , 1997 .

[15]  Wolfgang Meyer,et al.  Load balancing algorithms based on gradient methods and their analysis through algebraic graph theory , 2008, J. Parallel Distributed Comput..

[16]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[17]  Reza Entezari-Maleki,et al.  A Hybrid Genetic Algorithm and Variable Neighborhood Search for Task Scheduling Problem in Grid Environment , 2012 .

[18]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[19]  Jürgen Branke,et al.  Interactive Multiobjective Evolutionary Algorithms , 2008, Multiobjective Optimization.

[20]  H. Motameni,et al.  Task scheduling with Load balancing for computational grid using NSGA II with fuzzy mutation , 2012, 2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing.

[21]  Bharadwaj Veeravalli,et al.  A multi-dimensional scheduling scheme in a Grid computing environment , 2007, J. Parallel Distributed Comput..

[22]  Nawwaf N. Kharma,et al.  A high performance algorithm for static task scheduling in heterogeneous distributed computing systems , 2008, J. Parallel Distributed Comput..

[23]  Jagdish Chandra Patni,et al.  Load balancing strategies for Grid computing , 2011, 2011 3rd International Conference on Electronics Computer Technology.

[24]  Yacine Challal,et al.  Performance Evaluation of Load Balancing in Hierarchical Architecture for Grid Computing Service Middleware , 2011 .

[25]  David Abramson,et al.  An Evaluation of Economy-based Resource Trading and Scheduling on Computational Power Grids for Parameter Sweep Applications , 2000 .

[26]  Zengjian Hu,et al.  A new analytical method for parallel, diffusion-type load balancing , 2006, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.

[27]  Qian Li,et al.  One Kind of Improved Load Balancing Algorithm in Grid Computing , 2011, 2011 International Conference on Network Computing and Information Security.