Multi-objective Task Scheduling Using Chaotic Quantum-Behaved Chicken Swarm Optimization (CQCSO) in Cloud Computing Environment

2020 ◽  
pp. 803-814
Author(s):  
G. Kiruthiga ◽  
S. Mary Vennila
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Computing Environment ◽  
Swarm Optimization ◽  
Cloud Computing Environment ◽  
Multi Objective ◽  
Chicken Swarm Optimization

scholarly journals HYBRID CAT SWARM OPTIMIZATION AND SIMULATED ANNEALING FOR DYNAMIC TASK SCHEDULING ON CLOUD COMPUTING ENVIRONMENT

2018 ◽  
Author(s):  
Danlami Gabi ◽  
Abdul Samad Ismail ◽  
Anazida Zainal ◽  
Zalmiyah Zakaria ◽  
Ahmad Al-Khasawneh
Keyword(s):  
Cloud Computing ◽  
Local Search ◽  
Task Scheduling ◽  
Execution Time ◽  
Computing Environment ◽  
Swarm Optimization ◽  
Cloud Computing Environment ◽  
Multi Objective ◽  
Dynamic Task ◽  
Dynamic Task Scheduling

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.  

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