Int. J. of Business Intelligence and Data Mining   »   2018 Vol.13, No.1/2/3



Title: Epsilon-fuzzy dominance sort-based composite discrete artificial bee colony optimisation for multi-objective cloud task scheduling problem


Authors: B. Gomathi; Karthikeyan Krishnasamy; B. Saravana Balaji


Department of Information Technology, Hindusthan College of Engineering and Technology, Coimbatore, 641 032, India
Department of Information Technology, Karpagam College of Engineering, Coimbatore, 641 032, India
Department of Computer Science and Engineering, S.Veerasamy Chettiar College of Engineering and Technology, Puliyangudi, 627 855, India


Abstract: Cloud computing environment provides on-demand virtualised resources for cloud application. The scheduling of tasks in cloud application is a well-known NP-hard problem. The task scheduling problem is more complicated while satisfying multiple objectives, which are conflict in nature. In this paper, Epsilon-fuzzy dominance based composite discrete artificial bee colony (EDCABC) approach is used to generate Pareto optimal solutions for multi-objective task scheduling problem in cloud. Three conflicting objectives, such as makespan, execution cost and resource utilisation, are considered for task scheduling problem. The Epsilon-fuzzy dominance sort approach is used to choose the best solutions from the Pareto optimal solution set in the multi-objective domain. EDCABC with composite mutation strategies and fast local search method are used to enrich the local searching behaviours which help to avoid the premature convergence. The performance and efficiency of the proposed algorithm is compared with NSGA-II and MOPSO algorithms. The simulation results express that proposed EDCABC algorithm substantially minimises the makespan, execution cost and ensures the proper resource utilisation when compare to specified existing algorithm.


Keywords: task scheduling; discrete artificial bee colony; cloud computing; makespan; execution cost; fuzzy dominance; load balancing.


DOI: 10.1504/IJBIDM.2017.10004803


Int. J. of Business Intelligence and Data Mining, 2018 Vol.13, No.1/2/3, pp.247 - 266


Available online: 03 Nov 2017



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