Cloud Task Scheduling using the Squirrel Search Algorithm and Improved Genetic Algorithm

• Published in: International journal of advanced computer science & applications Vol. 14; no. 3
• Main Authors: DENG, Qiuju, WANG, Ning, LU, Yang
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2023
• Subjects: Cloud computing; Energy consumption; Genetic algorithms; Load distribution (forces); Real time; Resource allocation; Search algorithms; Task scheduling
• ISSN: 2158-107X; 2156-5570; 2156-5570
• DOI: 10.14569/IJACSA.2023.01403110

With cloud computing, resources can be networked globally and shared easily between users. A range of heterogeneous needs are met on demand by software, hardware, storage, and networking. Dynamic resource allocation and load distribution pose challenges for cloud servers. In this regard, task scheduling plays a significant role in enhancing the performance of cloud computing. With the increase in the number of users and the capability of cloud computing, cloud data centers are experiencing concerns regarding energy consumption. To leverage cloud resources energy efficiently and provide real-time services to users, a viable cloud task scheduling solution is required. To address these problems, this paper proposes a new hybrid task scheduling algorithm based on squirrel search and improved genetic algorithms for cloud environments. The proposed scheduling algorithm surpasses existing scheduling algorithms across multiple parameters, including makespan, energy consumption, and execution time.