An efficient approach to the map-reduce framework and genetic algorithm based whale optimization algorithm for task scheduling in cloud computing environment

Abstract

In the digital era, cloud computing has emerged a significant service in the IT sector. It ensures pooling of resources and provided services on-demand over the web. The scheduling of appropriate tasks is an important aspect in cloud computing in which many researches are carried out. The users demand for resources are volatile in nature and hence when a large count of resources is requested, the computational overhead in cloud is supposed to effectively allocate resources and also to complete these tasks. The research issue includes as how a VM can schedule these tasks in an effective manner. This paper proposes an efficient approach using the MAP reducing framework and GA-WOA for efficient scheduling of tasks in the given cloud. Initially, the task features are extracted from the client’s task. Then, the features are reduced by using the MRQFLDA algorithm. After that, the large tasks are separated into sub-tasks using a map-reduce framework. Finally, the tasks are efficiently scheduled by using the GA-WOA algorithm. The experimental simulations are carried out using the cloudsim environment. The results show that the proposed method GA-WOA outperforms the other methods in terms of various metrics used for the evaluation.