Statistical Performance Evaluation of Various Metaheuristic Scheduling Techniques for Cloud Environment
Ambika Aggarwal, Priti Dimri, Amit Agarwal
Abstract
Cloud computing has become the need of the hour as almost all businesses have started using the pay per use model proposed by cloud architecture instead of buying their own resources. Scheduling tasks to these sharable resources is a critical aspect of cloud computing and an area which is attracting many researchers. Scheduling workflows on a cloud architecture becomes even more critical as it contains a set of dependant tasks, and is considered an NP-hard problem. In this paper, various traditional meta-heuristic scheduling techniques have been implemented and their performance has been evaluated based on two parameters, Flowtime and Makespan. The various algorithms like PSO, DE, ETC, ABC, GA and FFOA are implemented using CloudSim and their performance is statistically evaluated in order to obtain minimized Flowtime and Makespan.