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Soft Computing

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21.06.2019 | Methodologies and Application

A comparative review of meta-heuristic approaches to optimize the SLA violation costs for dynamic execution of cloud services

verfasst von: Ajay Kumar, Seema Bawa

Erschienen in: Soft Computing | Ausgabe 6/2020

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Abstract

This paper presents comparative analysis results of research work done using the five most popular meta-heuristic techniques to optimize the service-level agreement (SLA) violation cost in cloud computing. The meta-heuristic algorithms have the ability to handle multifarious types of constraints and offer better results. The Quality of Service criteria, SLA penalty cost and the cloud-domain-specific constraints have been mathematically formulated in this paper. The sole motivation of this paper is that the constraints of feasible domain must be satisfied and the profit of cloud service provider should be maximized. An effort has been made to experimentally demonstrate the comparative performance of five meta-heuristic algorithms, namely Ant Colony Optimization, Particle Swarm Optimization, Genetic Algorithm, Gray Wolf Optimizer and Harmony Search. Eleven test benchmark functions have been applied to demonstrate the efficiency and performance. The best solutions of each meta-heuristic technique have been reported in four performance metric cases: worst, best, average and standard deviation.

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Abdullah M, Othman M (2014) Simulated annealing approach to cost-based multi-quality of service job scheduling in cloud computing enviroment. Am J Appl Sci 11(6):872

Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795

Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047–4071

Abualigah LM, Khader AT, Hanandeh ES (2018) A hybrid strategy for krill herd algorithm with harmony search algorithm to improve the data clustering? Intell Decis Technol 12(1):3–14

Abualigah LMQ (2018) Feature selection and enhanced Krill Herd algorithm for text document clustering, vol 816. Springer, Berlin

Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5(1):19

Alomari OA, Khader AT, Al-Betar MA, Abualigah LM (2017) Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm. Int J Data Min Bioinform 19(1):32–51

Bai Q (2010) Analysis of particle swarm optimization algorithm. Comput Inform Sci 3(1):180

Chen Q, Liu B, Zhang Q, Liang J, Suganthan P, Qu B (2014) Problem definitions and evaluation criteria for cec 2015 special session on bound constrained single-objective computationally expensive numerical optimization. Technical Report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Technical Report, Nanyang Technological University

Cheng B (2012) Hierarchical cloud service workflow scheduling optimization schema using heuristic generic algorithm. Prz Elektrotechniczny 88(2012):92–95

Choi Y, Lim Y (2016) Optimization approach for resource allocation on cloud computing for iot. Int J Distrib Sens Netw 2016:23

Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70

Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2–3):243–278MathSciNetMATH

Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC 99, vol 2. IEEE, pp 1470–1477

Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the sixth international symposium on micro machine and human science, IEEE, pp 39–43

Fister Jr I, Yang XS, Fister I, Brest J, Fister D (2013) A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186

Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35

Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

Greensmith J, Aickelin U (2008) The deterministic dendritic cell algorithm. In: International conference on artificial immune systems, Springer, pp 291–302

Grover J, Hanmandlu M (2018) New evolutionary optimization method based on information sets. Appl Intell 48(10):3394–3410

Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inform Sci 222:175–184MathSciNet

Holland JH et al (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, Cambridge

Jamil M, Yang XS (2013) A literature survey of benchmark functions for global optimisation problems. Int J Math Model Numer Optim 4(2):150–194MATH

Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (abc) algorithm and applications. Artif Intell Rev 42(1):21–57

Karaboga D, Ozturk C (2011) A novel clustering approach: artificial bee colony (abc) algorithm. Appl Soft Comput 11(1):652–657

Karimkashi S, Kishk AA (2010) Invasive weed optimization and its features in electromagnetics. IEEE Trans Antennas Propag 58(4):1269–1278

Kennedy J (2011) Particle swarm optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning, Springer, Boston, MA

Kephart JO, et al (1994) A biologically inspired immune system for computers. In: Artificial life IV: proceedings of the fourth international workshop on the synthesis and simulation of living systems, pp 130–139

Koza JR (1994) Genetic programming as a means for programming computers by natural selection. Stat Comput 4(2):87–112

Krishna PV (2013) Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl Soft Comput 13(5):2292–2303

Kumar A, Bawa S (2012) Virtualization of large-scale data storage system to achieve dynamicity and scalability in grid computing. In: Wyld DC, Zizka J, Nagamalai D (eds) Advances in computer science, engineering & applications. Springer, pp 323–331

Kumar A, Bawa S (2018) Generalized ant colony optimizer: swarm-based meta-heuristic algorithm for cloud services execution. Computing, pp 1–24

Leitner P, Ferner J, Hummer W, Dustdar S (2013) Data-driven and automated prediction of service level agreement violations in service compositions. Distrib Parallel Databases 31(3):447–470

Li W, Liu X, Zhang X, Zhang X (2015) Dynamic fair allocation of multiple resources with bounded number of tasks in cloud computing systems. Multiagent Grid Syst 11(4):245–257

Mezmaz M, Melab N, Kessaci Y, Lee YC, Talbi EG, Zomaya AY, Tuyttens D (2011) A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J Parallel Distrib Comput 71(11):1497–1508

Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

Mondal B, Dasgupta K, Dutta P (2012) Load balancing in cloud computing using stochastic hill climbing—a soft computing approach. Procedia Technol 4:783–789

Motieghader H, Najafi A, Sadeghi B, Masoudi-Nejad A (2017) A hybrid gene selection algorithm for microarray cancer classification using genetic algorithm and learning automata. Inform Med Unlocked 9:246–254

Mousavi S, Mosavi A, Varkonyi-Koczy AR, Fazekas G (2017) Dynamic resource allocation in cloud computing. Acta Polytechnica Hungarica 14(4):83–104

Muhammad K, Gao S, Qaisar S, Abdul M, Muhammad A, Usman A, Aleena A, Shahid A (2018) Comparative analysis of meta-heuristic algorithms for solving optimization problems. In: 2018 8th international conference on management, education and information (MEICI 2018). Atlantis Press

Neshat M, Sepidnam G, Sargolzaei M, Toosi AN (2014) Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif Intell Rev 42(4):965–997

Njenga K, Garg L, Bhardwaj AK, Prakash V, Bawa S (2019) The cloud computing adoption in higher learning institutions in kenya: hindering factors and recommendations for the way forward. Telemat Inform 38:225–246

Palm R, Bouguerra A (2013) Particle swarm against market-based optimisation for obstacle avoidance. Electron Lett 49(22):1378–1379

Pandey S, Wu L, Guru SM, Buyya R (2010) A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: 2010 24th IEEE international conference on Advanced information networking and applications (AINA), IEEE, pp 400–407

Riveni M, Nguyen TD, Dustdar S (2017) Sla-based management of human-based services in business processes for socio-technical systems. In: International conference on business process management, Springer, pp 361–373

Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the 1999 congress on evolutionary computation-CEC 99, vol 3, IEEE, pp 1945–1950

Singh B, Bawa S (2007) Aco based optimized scheduling algorithm for computational grids. In: Proceedings of the third conference on IASTED international conference, pp 283–286

Van Laarhoven PJ, Aarts EH (1987) Simulated annealing. In: Simulated annealing: Theory and applications, Springer, pp 7–15

Weile DS, Michielssen E (1997) Genetic algorithm optimization applied to electromagnetics: a review. IEEE Trans Antennas Propag 45(3):343–353

Wilcoxon F (1945) Individual comparisons by ranking methods. Biometr Bull 1(6):80–83

Xue S, Liu F, Xu X (2014) An improved algorithm based on nsga-ii for cloud pdts scheduling. JSW 9(2):443–450

Yan GW, Hao ZJ (2013) A novel optimization algorithm based on atmosphere clouds model. Int J Comput Intell Appl 12(01):1350002

Yan JY, Ling Q, Sun Dm (2006) A differential evolution with simulated annealing updating method. In: 2006 International conference on machine learning and cybernetics, IEEE, pp. 2103–2106

Ll Yang, Wy Qian, Zhang Q (2011) Central force optimization. J Bohai Univ (Natural Science Edition) 32(3):203–206

Yang XS (2011) Bat algorithm for multi-objective optimisation. Int J Bio Inspir Comput 3(5):267–274

Yang XS, Karamanoglu M (2013) Swarm intelligence and bio-inspired computation: an overview. In: Swarm intelligence and bio-inspired computation, Elsevier, pp 3–23

Yang XS, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim 46(9):1222–1237MathSciNet

Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

Yuan S, Deng G, Feng Q, Zheng P, Song T (2017) Multi-objective evolutionary algorithm based on decomposition for energy-aware scheduling in heterogeneous computing systems. J Univ Comput Sci 23(7):636–651

Zhang G, Zhou F, Huang X, Cheng J, Gheorghe M, Ipate F, Lefticaru R (2012) A novel membrane algorithm based on particle swarm optimization for solving broadcasting problems. J UCS 18(13):1821–1841MATH

Zhang Z, Hu F, Zhang N (2018) Ant colony algorithm for satellite control resource scheduling problem. Appl Intell 48(10):3295–3305

Zhu Z, Chen L, Yuan C, Xia C (2018) Global replacement-based differential evolution with neighbor-based memory for dynamic optimization. Appl Intell 48(10):3280–3294

Titel: A comparative review of meta-heuristic approaches to optimize the SLA violation costs for dynamic execution of cloud services
verfasst von: Ajay Kumar
Seema Bawa
Publikationsdatum: 21.06.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 6/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-04155-4

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