A Fuzzy Enabled Genetic Algorithm for Task Scheduling Problem in Cloud Computing

Abstract

Background & Objective: Cloud computing emerges out as a new way of computing which enables the users to fulfill their computation need using the underlying computing resources like software, memory, computing nodes or machines without owning them purely on the basis of pay-per-use that too round the clock and from anywhere. People defined this as the extension of the existing technologies like parallel computing, distributed computing or grid computing. Lots of research have been conducted in the field of cloud computing but the task scheduling is considered to be the most fundamental problem which is still in infancy and requires a lot of attention and a proper mechanism for the optimal utilization of the underlying computing resources. Task scheduling in cloud computing environment lies into the category of NP-hard problem and many heuristics and Meta heuristics strategies have been applied to solve the problem. Methods: In this work, Fuzzy Enabled Genetic Algorithm (FEGA) is proposed to solve the problem of task scheduling in cloud computing environment as classical roulette wheel selection method has certain limitations to solve complex optimization problem. Results & Discussion: In this work, an efficient fuzzy enabled genetic algorithm based task scheduling mechanism has been designed, implemented and investigated. The efficiency of the proposed FEGA algorithm is tested using various randomly generated data sets in different situations and compared with the other meta-heuristics. Conclusion: The authors suggest that the proposed Fuzzy Enabled Genetic Algorithm (FEGA) to solve the task scheduling problem helps in minimizing the total execution time or makespan and on comparing with other Meta-heuristic like genetic algorithm and greedy based strategy found that FEGA outperforms the both in different set of experiments.