Open Access

ARTICLE

Task Scheduling Optimization in Cloud Computing by Rao Algorithm

A. Younes^1,*, M. Kh. Elnahary¹, Monagi H. Alkinani², Hamdy H. El-Sayed¹

1 Faculty of Computers and Information, Department of Computer Science, Sohag University, Sohag, 82524, Egypt
2 Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21959, Saudi Arabia

* Corresponding Author: A. Younes. Email:

Computers, Materials & Continua 2022, 72(3), 4339-4356. https://doi.org/10.32604/cmc.2022.022824

Received 19 August 2021; Accepted 03 December 2021; Issue published 21 April 2022

Abstract

Cloud computing is currently dominated within the space of high-performance distributed computing and it provides resource polling and on-demand services through the web. So, task scheduling problem becomes a very important analysis space within the field of a cloud computing environment as a result of user's services demand modification dynamically. The main purpose of task scheduling is to assign tasks to available processors to produce minimum schedule length without violating precedence restrictions. In heterogeneous multiprocessor systems, task assignments and schedules have a significant impact on system operation. Within the heuristic-based task scheduling algorithm, the different processes will lead to a different task execution time (makespan) on a heterogeneous computing system. Thus, a good scheduling algorithm should be able to set precedence efficiently for every subtask depending on the resources required to reduce (makespan). In this paper, we propose a new efficient task scheduling algorithm in cloud computing systems based on RAO algorithm to solve an important task and schedule a heterogeneous multiple processing problem. The basic idea of this process is to exploit the advantages of heuristic-based algorithms to reduce space search and time to get the best solution. We evaluate our algorithm's performance by applying it to three examples with a different number of tasks and processors. The experimental results show that the proposed approach significantly succeeded in finding the optimal solutions than others in terms of the time of task implementation.

---

Keywords

---