Abstract
Information can be shared across the Internet using cloud computing, a powerful paradigm for meeting the needs of individuals and organizations. To minimize access time and maximize load balancing for data nodes (DNs), a dynamic data replication algorithm is necessary. Even so, few of the existing algorithms consider each objective holistically during replication. An improved ant lion optimizer (ALO) algorithm and a fuzzy system are used in this paper to determine dynamically the number of replicas and the DNs for replication. Further, it balances the trade-offs among different objectives (e.g., service time, system availability, load, and monetary cost). The ALO algorithm has been widely applied to solve complex optimization problems due to its simplicity in implementation. However, ALO has premature convergence and can thus easily get trapped into the local optimum solution. In this paper, to overcome the shortcomings of ALO by balancing exploration and exploitation, a hybrid ant lion optimizer with Tabu search algorithm (ALO-Tabu) is proposed. There are several improvements of the ALO, in which the appropriate solutions are selected for the initial population based on chaotic maps (CMs) and opposition-based learning (OBL) strategies. On the other hand, there are many CMs, OBLs, and random walk strategies that make it difficult to select the best one for optimization. Generally, they are selected manually, which is time-consuming. As a result, this paper presents a hyper-heuristic ALO (HH-ALO-Tabu) that automatically chooses CMs, OBLs, and random walk strategies depending on the differential evolution (DE) algorithm. Based on 20 well-known test functions, the experiment results and statistical tests show that HH-ALO-Tabu can solve optimization problems effectively.
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References
Abd Elaziz M, Mirjalili S (2019) A hyper-heuristic for improving the initial population of whale optimization algorithm. Knowl Based Syst 172:42–63. https://doi.org/10.1016/j.knosys.2019.02.010
Abualigah L, Diabat A (2021) Advances in Sine Cosine Algorithm: a comprehensive survey. Artif Intell Rev 54:2567–2608. https://doi.org/10.1007/s10462-020-09909-3
Abualigah L, Dulaimi AJ (2021) A novel feature selection method for data mining tasks using hybrid Sine Cosine Algorithm and Genetic Algorithm. Clust Comput. https://doi.org/10.1007/s10586-021-03254-y
Abualigah L, Diabat A, Mirjalili S, Elaziz MA, Gandomi AH (2021a) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng. https://doi.org/10.1016/j.cma.2020.113609
Abualigah L, Yousri D, Elaziz MA, Ewees AA, Al-qaness MAA, Gandomi AH (2021b) Aquila Optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng. https://doi.org/10.1016/j.cie.2021.107250
Alami Milani B, Jafari Navimipour N (2016) A comprehensive review of the data replication techniques in the cloud environments: major trends and future directions. Netw Comput Appl 64:229–238. https://doi.org/10.1016/j.jnca.2016.02.005
Alatas B, Akin E, Ozer B (2009) Chaos embedded particle swarm optimization algorithms. Chaos Solitons Fractals 40(4):1715–1734. https://doi.org/10.1016/j.chaos.2007.09.063
Alcalá-Fdez J, Sánchez L, García S, del Jesus MJ, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas VM, Fernández JC, Herrera F (2009) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13:307–318. https://doi.org/10.1007/s00500-008-0323-y
Arena P, Caponetto R, Fortuna L, Rizzo A, La Rosa M (2000) Self-organization in non-recurrent complex system. Int J Bifurc Chaos 10(5):1115–1125. https://doi.org/10.1142/S0218127400000785
Beigrezaei M, Haghighat AT, Mirtaheri SL (2021) Improve performance by a fuzzy-based dynamic replication algorithm in grid, cloud, and fog. Math Probl Eng. https://doi.org/10.1155/2021/5522026
Borthakur D (2007) The Hadoop distributed file system: architecture and design, Hadoop Project Website
Branco Jr T, de Sá-Soaresa F, Lopez Rivero A (2017) Key issues for the successful adoption of cloud computing. In: International conference on enterprise information systems, 2017, vol 121, pp 115–122. https://doi.org/10.1016/j.procs.2017.11.016
Chunlin L, Ping WY, Hengliang T, Youlong L (2019) Dynamic multi-objective optimized replica placement and migration strategies for SaaS applications in edge cloud. Future Gener Comput Syst 100:921–937. https://doi.org/10.1016/j.future.2019.05.003
Coelho LS, Mariani VC (2008) Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Expert Syst Appl 34:1905–1913. https://doi.org/10.1016/j.eswa.2007.02.002
Coelho LS, Mariani VC (2012) Firefly algorithm approach based on chaotic Tinkerbell map applied to multivariable PID controller tuning. Comput Math Appl 64(8):2371–2382. https://doi.org/10.1016/j.camwa.2012.05.007
Dong H, Xu Y, Li X, Yang Z, Zou C (2021) An improved antlion optimizer with dynamic random walk and dynamic opposite learning. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2021.106752
dos Santos CL, Mariani VC (2008) Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Expert Syst Appl 34(3):1905–1913. https://doi.org/10.1016/j.eswa.2007.02.002
Du Z, Hu J, Chen Y, Cheng Z, Wang X (2011) Optimized QoS-aware replica placement heuristics and applications in astronomy data grid. J Syst Softw 84(7):1224–1232. https://doi.org/10.1016/j.jss.2011.02.038
Emary E, Zawbaa HM, Hassanien AE (2016) Binary ant lion approaches for feature selection. Neurocomputing 213:54–65. https://doi.org/10.1016/j.neucom.2016.03.101
Ergezer M, Simon D, Du D (2009) Oppositional biogeography-based optimization. In: IEEE international conference on systems, man and cybernetics, 2009. https://doi.org/10.1109/ICSMC.2009.5346043
Gandomi AH, Yang XS, Alavi AH (2013a) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29:17–35. https://doi.org/10.1007/s00366-011-0241-y
Gandomi AH, Yun GJ, Yang XS, Talatahari S (2013b) Chaos-enhanced accelerated particle swarm optimization. Commun Nonlinear Sci Numer Simul 18(2):327–340. https://doi.org/10.1016/j.cnsns.2012.07.017
García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci 180:2044–2064. https://doi.org/10.1016/j.ins.2009.12.010
Gopinath S, Sherly E (2018) A dynamic replica factor calculator for weighted dynamic replication management in cloud storage systems. In: International conference on computational intelligence and data science, 2018, vol 132, pp 1771–1780. https://doi.org/10.1016/j.procs.2018.05.152
Goyal T, Singh A, Agrawal A (2012) CloudSim: simulator for cloud computing infrastructure and modeling. Procedia Eng 38:3566–3572. https://doi.org/10.1016/j.proeng.2012.06.412
Han P, Du C, Jinchao C, Ling F, Du X (2021) Cost and makespan scheduling of workflows in clouds using list multiobjective optimization technique. J Syst Archit. https://doi.org/10.1016/j.sysarc.2020.101837
Hassan OA-H, Ramaswamy L, Miller J, Rasheed K, Canfield ER (2009) Replication in overlay networks: a multi-objective optimization approach. In: Collaborative computing: networking, applications and worksharing, lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2009, vol 10, pp 512–528. https://doi.org/10.1007/978-3-642-03354-4_39
Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol Comput 44:148–175. https://doi.org/10.1016/j.swevo.2018.02.013
Jayasree P, Saravanan V (2018) APSDRDO: adaptive particle swarm division and replication of data optimization for security in cloud computing. IOSR J Eng 2278–8719
Kacimi MA, Guenounou O, Brikh L, Yahiaoui F, Hadid N (2020) New mixed-coding PSO algorithm for a self-adaptive and automatic learning of Mamdani fuzzy rules. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2019.103417
Kaucic M (2013) A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization. J Glob Optim 55(1):165–188. https://doi.org/10.1007/s10898-012-9913-4
Kaveh A (2014) Dolphin echolocation optimization. Adv Metaheuristic Algorithms Optim Des Struct 59:157–193. https://doi.org/10.1016/j.advengsoft.2013.03.004
Kaveh A, Khayatazad M (2013) Ray optimization for size and shape optimization of truss structures. Comput Struct 117:82–94. https://doi.org/10.1016/j.compstruc.2012.12.010
Kaveh A, Mahdavi V (2014) Colliding bodies optimization method for optimum design of truss structures with continuous variables. Adv Eng Softw 70:1–12. https://doi.org/10.1016/j.advengsoft.2014.01.002
Kaveh A, Zolghadr A (2014) Democratic PSO for truss layout and size optimization with frequency constraints. Comput Struct 130:10–21. https://doi.org/10.1016/j.compstruc.2013.09.002
Kaveh A, Motie M, Moslehi M (2013) Magnetic charged system search: a new meta-heuristic algorithm for optimization. Acta Mech. https://doi.org/10.1007/s00707-012-0745-6
Kaveh A, Bakhshpoori T, Afshari E (2014) An efficient hybrid particle swarm and swallow swarm optimization algorithm. Comput Struct 143:40–59. https://doi.org/10.1016/j.compstruc.2014.07.012
Khalili Azimi S (2019a) A bee colony (beehive) based approach for data replication in cloud environments. Fundam Res Electr Eng. https://doi.org/10.1007/978-981-10-8672-4_80
Khalili Azimi S (2019b) A bee colony (beehive) based approach for data replication in cloud environments. Fundam Res Electr Eng. https://doi.org/10.1007/978-981-10-8672-4_80
Kılıç H, Yüzgeç U (2019) Tournament selection based antlion optimization algorithm for solving quadratic assignment problem. Eng Sci Technol Int J 22(2):673–691. https://doi.org/10.1016/j.jestch.2018.11.013
Kwame Senyo P, Addae E, Boateng R (2018) Cloud computing research: a review of research themes, frameworks, methods and future research directions. Int J Inf Manag 38(1):128–139. https://doi.org/10.1016/j.ijinfomgt.2017.07.007
Liang J, Suganthan P, Deb K (2005) Novel composition test functions for numerical global optimization. In: Proceedings 2005 IEEE swarm intelligence symposium, 2005, pp 68–75. https://doi.org/10.1109/SIS.2005.1501604
Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10:629–640. https://doi.org/10.1016/j.asoc.2009.08.031
Long SQ, Zhao YL, Chen W (2014) MORM: A Multi-objective Optimized Replication Management strategy for cloud storage cluster. J Syst Archit 60(2):234–244. https://doi.org/10.1016/j.sysarc.2013.11.012
Luo Y, Li R, Zhang L, Tian F (2004) Application of artificial immune algorithm to function optimization. In: Fifth world congress on intelligent control and automation, 2004. https://doi.org/10.1109/WCICA.2004.1341989
Mahdavi Jafari M, Khayati GR (2018) Prediction of hydroxyapatite crystallite size prepared by sol–gel route: gene expression programming approach. J Sol–Gel Sci Technol 86(1):112–125. https://doi.org/10.1007/s10971-018-4601-6
Manganaro G, de Gyvez JP (1997) DNA computing based on chaos. In: IEEE international conference on evolutionary computation, 1997, pp 255–260. https://doi.org/10.1109/ICEC.1997.592306
Mansouri N (2014) A threshold-based dynamic data replication and parallel job scheduling strategy to enhance data grid. Clust Comput 17(3):957–977. https://doi.org/10.1007/s10586-013-0330-3
Mansouri N (2016) Adaptive data replication strategy in cloud computing for performance improvement. Front Comput Sci 10(5):925–935. https://doi.org/10.1007/s11704-016-5182-6
Mansouri N, Javidi MM (2017) A new prefetching-aware data replication to decrease access latency in cloud environment. J Syst Softw 144:197–215. https://doi.org/10.1016/j.simpat.2017.06.001
Mansouri N, Javidi MM (2018) A hybrid data replication strategy with fuzzy-based deletion for heterogeneous cloud data centers. J Supercomput 74(10):5349–5372. https://doi.org/10.1007/s11227-018-2427-1
Mansouri N, Kuchaki Rafsanjani M, Javidi MM (2017) DPRS: a dynamic popularity aware replication strategy with parallel download scheme in cloud environments. Simul Model Pract Theory 77:177–196. https://doi.org/10.1016/j.simpat.2017.06.001
Mansouri N, Javidi MM, Mohammad Hasani Zade B (2020) Using data mining techniques to improve replica management in cloud environment. Soft Comput 24:7335–7360. https://doi.org/10.1007/s00500-019-04357-w
Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98. https://doi.org/10.1016/j.advengsoft.2016.01.008
Mirjalili S, Lewis A (2016) The Whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513. https://doi.org/10.1007/s00521-015-1870-7
Mohammed A, Duffuaa SO (2020) A Tabu search based algorithm for the optimal design of multi-objective multi-product supply chain networks. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2019.07.025
Mohammad Hasani Zade B, Mansouri N, Javidi MM (2021) SAEA: a security-aware and energy-aware task scheduling strategy by Parallel Squirrel Search Algorithm in cloud environment. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2021.114915
Mousa AA, El-Shorbag MA, Farag MA (2020) Steady-state sine cosine genetic algorithm based chaotic search for nonlinear programming and engineering applications. IEEE Access 8:212036–212054. https://doi.org/10.1109/ACCESS.2020.3039882
Parejo A, Ruiz-Cortés A, Lozano S, Fernandez P (2012) Metaheuristic optimization frameworks: a survey and benchmarking. Soft Comput 16(3):527–561. https://doi.org/10.1007/s00500-011-0754-8
Phan DH, Suzuki J, Carroll R (2012) Evolutionary multiobjective optimization for green clouds. In: Proceedings of the 14th annual conference companion on genetic and evolutionary computation, 2012, pp 19–26. https://doi.org/10.1145/2330784.2330788
Pradhan R, Kumar Majhi S, Ku Pradhan J, Bhusan Pati B (2018) Antlion optimizer tuned PID controller based on Bode ideal transfer function for automobile cruise control system. J Ind Inf Integr 9:45–52. https://doi.org/10.1016/j.jii.2018.01.002
Price KV, Awad NH, Ali MZ, Suganthan PN (2018) The 100-digit challenge: problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization. Nanyang Technological University, Nanyang
Rahnamayan S, Tizhoosh HR, Salama MM (2007) Quasi-oppositional differential evolution. In: Proceedings of the IEEE congress on evolutionary computation, 2007, pp 2229–2236. https://doi.org/10.1109/CEC.2007.4424748
Ren B, Zhong W (2011) Multi-objective optimization using chaos based PSO. J Inf Technol 10(10):1908–1916. https://doi.org/10.3923/itj.2011.1908.1916
Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13:2592–2612. https://doi.org/10.1016/j.asoc.2012.11.026
Saha S, Mukherjee V (2018) A novel quasi-oppositional chaotic antlion optimizer for global optimization. Appl Intell 48(9):2628–2660. https://doi.org/10.1016/j.knosys.2021.106752
Saranya C, Manikandan G (2013) A study on normalization techniques for privacy preserving data mining. Int J Eng Technol 5:2701–2704
Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimization algorithm: theory and application. Adv Eng Softw 105:30–47. https://doi.org/10.1016/j.advengsoft.2017.01.004
Seif Z, Ahmadi M (2015) An opposition-based algorithm for function optimization. Eng Appl Artif Intell 37:293–306. https://doi.org/10.1016/j.engappai.2014.09.009
Sousa FRC, Machado JC (2012) Towards elastic multi-tenant database replication with quality of service. In: IEEE/ACM 5th international conference on utility and cloud computing, 2012, pp 168–175. https://doi.org/10.1109/UCC.2012.36
Suganthan PN, Hansen N, Liang JJ, Deb K, Chen Y, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real parameter optimization. Nat Comput. https://doi.org/10.1007/s11047-018-9704-z
Suneel M (2006) Chaotic sequences for secure CDMA. Ramanujan Institute for Advanced Study in Mathematics, Chennai, pp 1–4
Tang M, Sung Lee B, Kiat Yeo C, Tang X (2005) Dynamic replication algorithms for the multi-tier data grid. Future Gener Comput Syst 21(5):775–790. https://doi.org/10.1016/j.future.2004.08.001
Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, Web technologies and Internet commerce (CIMCA-IAWTIC'06), 2005. https://doi.org/10.1109/CIMCA.2005.1631345
Tos U, Mokadem R, Hameurlain A, Ayav T, Bora S (2018) Ensuring performance and provider profit through data replication in cloud systems. Clust Comput 21(3):479–1492. https://doi.org/10.1007/s10586-017-1507-y
Vulimiri A, Curino C, Godfrey B, Jungblut T, Padhye J, Varghese G (2015) Global analytics in the face of bandwidth and regulatory constraints. In: 12th USENIX symposium on networked systems design and implementation, 2015, pp 323–336
Wang T, Yao S, Xu Z, Pan S (2017) Dynamic replication to reduce access latency based on fuzzy logic system. Comput Electr Eng 60:48–57. https://doi.org/10.1016/j.compeleceng.2016.11.022
Wang M, Heidari AA, Chen M, Chen H, Zhao X, Cai X (2020) Exploratory differential ant lion-based optimization. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.113548
Wei Sun D, Chang GR, Gao S, Jin LZ, Wei Wang X (2012) Modeling a dynamic data replication strategy to increase system availability in cloud computing environments. J Comput Sci Technol 27(2):256–272. https://doi.org/10.1007/s11390-012-1221-4
Wu X (2017) Combination replicas placements strategy for data sets from cost-effective view in the cloud. Int J Comput Intell Syst 10:521–539. https://doi.org/10.2991/ijcis.2017.10.1.36
Xie F, Yan J, Shen J (2017) Towards cost reduction in cloud-based workflow management through data replication. In: Fifth international conference on advanced cloud and big data (CBD), 2017. https://doi.org/10.1109/CBD.2017.24
Yao S, Wen S, Yao B, Li XY (2018) DARS: a dynamic adaptive replica strategy under high load cloud-P2P. Future Gener Comput Syst 78:31–40. https://doi.org/10.1016/j.future.2017.07.046
Zawbaa HM, Emary E, Grosan C (2016) Feature selection via chaotic antlion optimization. PLoS ONE. https://doi.org/10.1371/journal.pone.0150652
Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci 178:3043–3074. https://doi.org/10.1016/j.ins.2008.02.014
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Mohammad Hasani Zade, B., Mansouri, N. & Javidi, M.M. A new hyper-heuristic based on ant lion optimizer and Tabu search algorithm for replica management in cloud environment. Artif Intell Rev 56, 9837–9947 (2023). https://doi.org/10.1007/s10462-022-10309-y
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DOI: https://doi.org/10.1007/s10462-022-10309-y