A Pair-Task Heuristic for Scheduling Tasks in Heterogeneous Multi-Cloud Environment

Abstract

Abstract Heterogeneous multi-cloud environments make use of a collection of varied performance rich cloud resources, linked with huge-speed, performs varied applications which are of computational nature. Applications require distinct computational features for processing. Heterogeneous multi-cloud domain well suits to satisfy the computational need of very big diverse nature of collection of tasks. Mapping problem provides an optimal solution in scheduling tasks to distributed heterogeneous clouds is termed NP-complete, which leads to the ultimate establishment of heuristic problem solving technique. Identifying the heuristic which is appropriate and best still exists as a complicated problem. In this paper, to address scheduling collection of 'n' tasks in two groups among a set of 'm' clouds, we propose three heuristics PTL (Pair-Task Threshold Limit), PTMax-Min, and PTMin-Max. Firstly to determine the tasks scheduling order, proposed heuristics based on the tasks attributes calculate tasks threshold value. Tasks sorted in descending value of threshold. Group G1 comprises tasks ordered in descending value of threshold. Group G2 comprises remaining tasks ordered in ascending value of threshold. Secondly, tasks form Group 1 are scheduled first based on minimum completion time, and then tasks in Group 2 are scheduled. The proposed heuristicsare compared with existing heuristics, namely MCT, MET, Min-Min using benchmark dataset. Heuristics PTL, PTMax-Min, and PTMin-Max bring out reduced makespan compared to MCT, MET, and Min-min.