A Relative Study of Task Scheduling Algorithms in Cloud Computing Environment

TL;DR

This paper reviews and compares various task scheduling algorithms in cloud computing, focusing on their performance based on parameters like execution time, throughput, and energy consumption.

Contribution

It provides a comprehensive comparison of existing cloud task scheduling algorithms, highlighting their strengths and limitations in meeting cloud environment requirements.

Findings

Traditional algorithms are inadequate for cloud needs

Modified algorithms improve resource utilization

Scheduling impacts energy consumption and response time

Abstract

Cloud Computing is a paradigm of both parallel processing and distributed computing. It offers computing facilities as a utility service in pay as par use manner. Virtualization, self service provisioning, elasticity and pay per use are the key features of Cloud Computing. It provides different types of resources over the Internet to perform user submitted tasks. In cloud environment, huge number of tasks are executed simultaneously, an effective Task Scheduling is required to gain better performance of the cloud system. Various Cloud Based Task Scheduling algorithms are available that schedule the task of user to resources for execution. Due to the novelty of Cloud Computing, traditional scheduling algorithms cannot satisfy the needs of cloud , the researchers are trying to modify traditional algorithms that can fulfill the cloud requirements like rapid elasticity, resource pooling and on demand self service. In this paper the current state of Task Scheduling algorithms has been discussed and compared on the basis of various scheduling parameters like execution time, throughput, make span, resource utilization, quality of service, energy consumption, response time and cost.