A survey on cloud computing scheduling algorithms

Article type: Research Article

Authors: Malekimajd, Marzieh^* | Safarpoor-Dehkordi, Ali

Affiliations: Iran University of Science and Technology, Tehran, Iran

Correspondence: [*] Corresponding author: Marzieh Malekimajd, Iran University of Science and Technology, Tehran, Iran. E-mail: malekimajd@iust.ac.ir.

Abstract: Cloud computing has emerged as one of the hottest topics in technology and has quickly become a widely used information and communication technology model. Performance is a critical component in the cloud environment concerning constraints like economic, time, and hardware issues. Various characteristics and conditions for providing solutions and designing strategies must be dealt with in different situations to perform better. For example, task scheduling and resource allocation are significant challenges in cloud management. Adopting proper techniques in such conditions leads to performance improvement. This paper surveys existing scheduling algorithms concerning the macro design idea. We classify these algorithms into four main categories: deterministic algorithms, metaheuristic algorithms, learning algorithms, and algorithms based on game theory. Each category is discussed by citing appropriate studies, and the MapReduce review is addressed as an example.

Keywords: Cloud computing, scheduling, deterministic algorithms, optimization algorithms, metaheuristic algorithms, learning algorithms

DOI: 10.3233/MGS-220217

Journal: Multiagent and Grid Systems, vol. 18, no. 2, pp. 119-148, 2022