Universiti Teknologi Malaysia Institutional Repository

Hybrid cat swarm optimization and simulated annealing for dynamic task scheduling on cloud computing environment

Gabi, Danlami and Ismail, Abdul Samad and Zainal, Anazida and Zakaria, Zalmiyah and Al-Khasawneh, Ahmad (2018) Hybrid cat swarm optimization and simulated annealing for dynamic task scheduling on cloud computing environment. Journal of Information and Communication Technology, 17 (3). pp. 435-467. ISSN 1675-414X

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The unpredictable number of task arriving at cloud datacentre and the rescaling of virtual processing elements can affect the provisioning of better Quality of Service expectations during task scheduling in cloud computing. Existing researchers have contributed several task scheduling algorithms to provide better QoS expectations but are characterized with entrapment at the local search and high dimensional breakdown due to slow convergence speed and imbalance between global and local search, resulting from lack of scalability. Dynamic task scheduling algorithms that can adjust to long-time changes and continue facilitating the provisioning of better QoS are necessary for cloud computing environment. In this study, a Cloud Scalable Multi-Objective Cat Swarm Optimization-based Simulated Annealing algorithm is proposed. In the proposed method, the orthogonal Taguchi approach is applied to enhance the SA which is incorporated into the local search of the proposed CSMCSOSA algorithm for scalability performance. A multi-objective QoS model based on execution time and execution cost criteria is presented to evaluate the efficiency of the proposed algorithm on CloudSim tool with two different datasets. Quantitative analysis of the algorithm is carried out with metrics of execution time, execution cost, QoS and performance improvement rate percentage. Meanwhile, the scalability analysis of the proposed algorithm using Isospeed-efficiency scalability metric is also reported. The results of the experiment show that the proposed CSM-CSOSA has outperformed Multi-Objective Genetic Algorithm, Multi-Objective Ant Colony and Multi-Objective Particle Swarm Optimization by returning minimum execution time and execution cost as well as better scalability acceptance rate of 0.4811-0.8990 respectively. The proposed solution when implemented in real cloud computing environment could possibly meet customers QoS expectations as well as that of the service providers.

Item Type:Article
Uncontrolled Keywords:cat swarm optimization, cloud computing, multi-objective optimization, simulated annealing, task scheduling
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:85444
Deposited By: Yanti Mohd Shah
Deposited On:30 Jun 2020 16:45
Last Modified:30 Jun 2020 16:45

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