Orthogonal Taguchi-based cat algorithm for solving task scheduling problem in cloud computing

et al. 2020

Neural Comput & Applic

Self Cite

With growing demand on resources situated at the cloud datacenters, the need for customers' resource selection techniques becomes paramount in dealing with the concerns of resource inefficiency. Techniques such as metaheuristics are promising than the heuristics, most especially when handling large scheduling request. However, addressing certain limitations attributed to the metaheuristic such as slow convergence speed and imbalance between its local and global search could enable it become even more promising for customers service selection. In this work, we propose a cloud customers service selection scheme called Dynamic Multi-Objective Orthogonal Taguchi-Cat (DMOOTC). In the proposed scheme, avoidance of local entrapment is achieved by not only increasing its convergence speed, but balancing between its local and global search through the incorporation of Taguchi orthogonal approach. To enable the scheme to meet customers' expectations, Pareto dominant strategy is incorporated providing better options for customers in selecting their service preferences. The implementation of our proposed scheme with that of the benchmarked schemes is carried out on CloudSim simulator tool. With two scheduling scenarios under consideration, simulation results show for the first scenario, our proposed DMOOTC scheme provides better service choices with minimum total execution time and cost (with up to 42.87%, 35.47%, 25.49% and 38.62%, 35.32%, 25.56% reduction) and achieves 21.64%, 18.97% and 13.19% improvement for the second scenario in terms of execution time compared to that of the benchmarked schemes. Similarly, statistical results based on 95% confidence interval for the whole scheduling scheme also show that our proposed scheme can be much more reliable than the benchmarked scheme. This is an indication that the proposed DMOOTC can meet customers' expectations while providing guaranteed performance of the whole cloud computing environment.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: The Need For Cat Swarm Optimization Improvementmentioning

confidence: 99%

“…The main objective of adopting the Taguchi approach is to find an optimal solution in a reasonable amount of time [51,55]. Detail about the Taguchi method can be found in [33].…”

Section: Taguchi Orthogonal Arraymentioning

confidence: 99%

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Cloud customers service selection scheme based on improved conventional cat swarm optimization

et al. 2020

Neural Comput & Applic

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“…Some of the examples of SI techniques used by existing researchers to address task scheduling problem are; Particle Swarm Optimization (PSO) (Ramezaini et al, 2013;Awad et al, 2015;Jena, 2015), Ant Colony Optimization (ACO), (Shengjun et al, 2015;Anradha & Selvakumar, 2015), Artificial Bee Colony (ABC) (Kumar & Gunasekaram, 2014;Li & Pan, 2015;Gao et al, 2015), BAT algorithm (Gandomi & Yang, 2014;Jacob, 2014;George, 2015) & Cat Swarm Optimization (CSO) (Bilgaiyan et al, 2015;Gabi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Cat Swarm Optimization and Simulated Annealing for Dynamic Task Scheduling on Cloud Computing Environment

Journal of Information and Communication Technology

et al. 2018

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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.

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Quality of Service (QoS) Task Scheduling Algorithm with Taguchi Orthogonal Approach for Cloud Computing Environment

Recent Trends in Information and Communication Technology

et al. 2017