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Hybrid PSACGA Algorithm for Job Scheduling to Minimize Makespan in Heterogeneous Grids

  • Amit ChhabraEmail author
  • Oshin
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 11)

Abstract

Grid provides a clear, coordinated, consistent and reliable computing medium to solve complex sequential and parallel applications through the use of idle CPU cycles. Scheduling optimizes the objective function(s) by mapping the parallel jobs to the available resources. Owing to the heterogeneity of resources in grid, scheduling associates with class of NP-hard problems due to which reaching optimal solution surpasses the time constraint. Metaheuristic algorithms take polynomial time to reach the near-optimal solutions for NP-hard problems. Major research issues in metaheuristic algorithms are solution quality and convergence speed that have been revised by using consolidation approach. This paper proposes a hybrid PSACGA algorithm that consolidate the features of Particle Swarm Optimization (PSO), Ant Colony optimization (ACO) and operators of Genetic algorithm to solve parallel job scheduling problem. Experimental results of the proposed technique are compared with existing deterministic and metaheuristic job scheduling algorithms. Experimental results have indicated that the proposed hybrid PSAGA algorithm provides better performance than existing contemporary algorithms.

Keywords

Grid Job scheduling Ant colony optimization Particle swarm optimization and genetic algorithm 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Engineering and TechnologyGuru Nanak Dev UniversityAmritsarIndia

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