Abstract

With exponential growth in the number of customers accessing the cloud services, scheduling tasks at cloud datacenter poses the greatest challenge in meeting end-user’s quality of service (QoS) expectations in terms of time and cost. Recent research makes use of metaheuristic task scheduling techniques in addressing this concern. However, metaheuristic techniques are attributed with certain limitation such as premature convergence, global and local imbalance which causes insufficient task allocation across cloud virtual machines. Thus, resulting in inefficient QoS expectation. To address these concerns while meeting end-users QoS expectation, this paper puts forward a non-preemptive chaotic cat swarm optimization (NCCSO) scheme as an ideal solution. In the developed scheme, chaotic process is introduced to reduce entrapment at local optima and overcome premature convergence and Pareto dominant strategy is used to address optimality problem. The developed scheme is implemented in the CloudSim simulator tool and simulation results show the developed NCCSO scheme compared to the benchmarked schemes adopted in this paper can achieve 42.87%, 35.47% and 25.49% reduction in term of execution time, and also 38.62%, 35.32%, 25.56% in term of execution cost. Finally, we also unveiled that a statistical significance on 95% confidential interval has shown that our developed NCCSO scheme can provide a remarkable performance that can meet end-user QoS expectations.

Keyword

Cloud computing, Cat swarm optimization, Chaotic process, Pareto dominance.

Cite this article

Gabi D, Dankolo NM, Ismail AS, Zainal A, Zakaria Z

Refference

[1][1]Gui Z, Yang C, Xia J, Huang Q, Liu K, Li Z, et al. A service brokering and recommendation mechanism for better selecting cloud services. PloS one. 2014; 9(8): e105297.

[2][2]Adebisi AA, Adekanmi AA, Oluwatobi AE. A study of cloud computing in the university enterprise. International Journal of Advanced Computer Research. 2014; 4(15):450-8.

[3][3]Soni A, Hasan M. Pricing schemes in cloud computing: a review. International Journal of Advanced Computer Research. 2017; 7(29):60-70.

[4][4]Gabi D, Ismail AS, Zainal A, Zakaria Z. Solving task scheduling problem in cloud computing environment using orthogonal Taguchi-cat algorithm. International Journal of Electrical and Computer Engineering (IJECE). 2017; 7(3):1489-97.

[5][5]Meena M, Bharadi VA. Performance analysis of cloud-based software as a service (SaaS) model on public and hybrid cloud. In symposium on colossal data analysis and networking (CDAN) 2016 (pp. 1-6). IEEE.

[6][6]Gabi D, Ismail AS, Zainal A. Systematic review on existing load balancing techniques in cloud computing. International Journal of Computer Applications. 2015; 125(9):16-24.

[7][7]Zhang Y, Qian C, Lv J, Liu Y. Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor. IEEE Transactions on Industrial Informatics. 2017; 13(2):737-47.

[8][8]Zuo L, Shu L, Dong S, Zhu C, Hara T. A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. IEEE ACCESS. 2015; 3:2687-99.

[9][9]Chu SC, Tsai PW. Computational intelligence based on the behavior of cats. International Journal of Innovative Computing, Information and Control. 2007; 3(1):163-73.

[10][10]Gabi D, Ismail AS, Zainal A, Zakaria Z, Abraham A. Orthogonal Taguchi-based cat algorithm for solving task scheduling problem in cloud computing. Neural Computing and Applications. 2018; 30(6):1845-63.

[11][11]Ramezani F, Lu J, Hussain F. Task scheduling optimization in cloud computing applying multi-objective particle swarm optimization. In international conference on service-oriented computing 2013 (pp. 237-51). Springer, Berlin, Heidelberg.

[12][12]Liu G, Li J, Xu J. An improved min-min algorithm in cloud computing. In proceedings of the international conference of modern computer science and applications 2013 (pp. 47-52). Springer, Berlin, Heidelberg.

[13][13]Xu Z, Xu X, Zhao X. Task scheduling based on multi-objective genetic algorithm in cloud computing. Journal of Information & Computational Science. 2015; 12(4):1429-38.

[14][14]Kahejvand V, Pedram H, Zandieh M. Multi-objective and scalable heuristic algorithm for workflow task scheduling in utility grids. Journal of optimization in industrial engineering. 2014; 7(14):27-36.

[15][15]Pradhan PM, Panda G. Solving multiobjective problems using cat swarm optimization. Expert Systems with Applications. 2012; 39(3):2956-64.

[16][16]Gabi D, Ismail AS, Zainal A, Zakaria Z, Al-Khasawneh A. Hybrid cat swarm optimization and simulated annealing for dynamic task scheduling on cloud computing environment. Journal of ICT. 2018; 17(3):435-67.

[17][17]Saule C, Giegerich R. Pareto optimization in algebraic dynamic programming. Algorithms for Molecular Biology. 2015; 10(1):1-20.

[18][18]Kalyanmoy D. Multi-objective optimization using evolutionary algorithms: an introduction. KanGAL Report. 2011(2011003).

[19][19]Li X, Xu J, Yang Y. A chaotic particle swarm optimization-based heuristic for market-oriented task-level scheduling in cloud workflow systems. Computational Intelligence and Neuroscience. 2015; 2015:81.

[20][20]Abdullahi M, Ngadi MA, Dishing SI. Chaotic symbiotic organisms search for task scheduling optimization on cloud computing environment. In ICT international student project conference (ICT-ISPC) 2017 (pp. 1-4). IEEE.

[21][21]Chang HC, Chen YP, Liu TK, Chou JH. Solving the flexible job shop scheduling problem with makespan optimization by using a hybrid Taguchi-genetic algorithm. IEEE Access. 2015; 3:1740-54.

[22][22]Liu J, Pacitti E, Valduriez P, De Oliveira D, Mattoso M. Multi-objective scheduling of scientific workflows in multisite clouds. Future Generation Computer Systems. 2016; 63:76-95.

[23][23]Farahabady MR, Lee YC, Zomaya AY. Pareto-optimal cloud bursting. IEEE Transactions on Parallel and Distributed Systems. 2014; 25(10):2670-82.

[24][24]Ramezani F, Lu J, Taheri J, Hussain FK. Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments. World Wide Web. 2015; 18(6):1737-57.

[25][25]Pachorkar N, Ingle R. Affinity aware VM colocation mechanism for cloud. International Journal of Advanced Computer Research. 2014; 4(17):956-60.

[26][26]Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience. 2011; 41(1):23-50.

[27][27]Eberhart RC, Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization. In proceedings of the congress on evolutionary computation. CEC00 2000 (pp. 84-88). IEEE.

[28][28]Hosmer DW, Lemeshow S. Confidence interval estimation of interaction. Epidemiology (Cambridge, Mass.). 1992; 3(5):452-6.