An Efficient Firefly Algorithm for Optimizing Task Scheduling in Cloud Computing Systems

Authors

Ahmed Y. Hamed, Department of Computer Science, Faculty of Computers and Artificial Intelligence, Sohag University, Sohag, 82524, EgyptFollow
M. Kh. Elnahary, Department of Computer Science, Faculty of Computers and Artificial Intelligence, Sohag University, Sohag, 82524, EgyptFollow
Hamdy H. El-Sayed, Department of Computer Science, Faculty of Computers and Artificial Intelligence, Sohag University, Sohag, 82524, EgyptFollow
Louai Maghrabi, Department of Software Engineering, College of Engineering, University of Business and Technology, Jeddah, Saudi ArabiaFollow

Abstract

As user service demands change constantly, task scheduling becomes an extremely significant study area within the cloud environment. The goal of scheduling is distributing the tasks on available processors in order to achieve the shortest possible makespan while adhering to priority constraints. In heterogeneous cloud computing resources, task scheduling has a large influence on system performances. The various processes in the heuristic-based algorithm of scheduling will result in varied makespans when heterogeneous resources are utilized. As a result, a smart method of scheduling must be capable of establishing precedence efficacy for each task to decrease makespan time. In our study, we develop a novel efficient method of scheduling tasks according to the firefly algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem. We evaluate the performance of our algorithm by putting it through three situations with changing amounts of processors and numbers of tasks. The findings of the experiment reveal that our suggested technique found optimal solutions substantially more frequently in terms of makespan time when compared with other methods.

Recommended Citation

Y. Hamed, Ahmed; Kh. Elnahary, M.; H. El-Sayed, Hamdy; and Maghrabi, Louai (2023) "An Efficient Firefly Algorithm for Optimizing Task Scheduling in Cloud Computing Systems," Information Sciences Letters: Vol. 12 : Iss. 3 , PP -.
Available at: https://digitalcommons.aaru.edu.jo/isl/vol12/iss3/47