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RETRACTED ARTICLE: MHO: meta heuristic optimization applied task scheduling with load balancing technique for cloud infrastructure services

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This article was retracted on 23 May 2022

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Abstract

The cloud computing provides on demand access to shared resources over internet in a cloud platform powerfully adaptable and metered way. Cloud computing empowers the user get to wherever to a shared pool of configurable resources and gives different administrations to the resource assignment like scientific operations, services computing through virtualization. To give guaranteed productive execution to clients, tasks ought to be proficiently mapped to accessible resources. In this manner, Task Scheduling is noteworthy issue in the cloud infrastructure administrations. The essential target of task execution planning includes reserving the infrastructure assets and limiting the goal of the execution plan. In this research work, we proposed metaheuristic optimization technique with load balancing to enhance the cloud infrastructure service provider’s performance there by depleting the scheduling issues. The proposed technique is pertinent for static and dynamic task condition, where static methods VM parameters are fixed, dynamic means parameters are chosen runtime. The proposed algorithm consists of two phases MHOS-S and MHO-D for dealing with static and dynamic properties of the task submitted. The result analysis by comparing with few traditional metaheuristic algorithms proves that the proposed technique performs better in complex environments.

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References

  • Abrishami S, Naghibzadeh M, Epema DH (2013) Deadline-constrained workflow scheduling algorithms for infrastructure as a service clouds. Future Gener Comput Syst 29:158–169

    Article  Google Scholar 

  • Achary R, Vityanathan V, Raj P, Nagarajan S (2015) Dynamic job scheduling using ant colony optimization for mobile cloud computing. Intelligent distributed computing. Springer, Berlin, pp 71–82

    Google Scholar 

  • Alkhanak EN, Lee SP, Khan SUR (2015) Cost-aware challenges for workflow scheduling approaches in cloud computing environments: taxonomy and opportunities. Future Gener Comput Syst 50:3–21

    Article  Google Scholar 

  • Beegom AA, Rajasree M (2015) Genetic algorithm framework for bi-objective task scheduling in cloud computing systems. Distributed computing and internet technology. Springer, Berlin, pp 356–359

    Google Scholar 

  • Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp (SPE) 41(1):23–50 (ISSN: 0038-0644)

    Article  Google Scholar 

  • Chaudhary D, Kumar B (2019) Cost optimized hybrid genetic-gravitational search algorithm for load scheduling in cloud computing. Appl Soft Comput 83:1–18

    Article  Google Scholar 

  • Dutta D, Joshi RC (2011) A genetic: algorithm approach to cost-based multi-QoS job scheduling in cloud computing environment. In: Proceedings of the international conference & workshop on emerging trends in technology. ACM, pp 422–427

  • Fernando N, Loke SW, Rahayu W (2012) Mobile cloud computing: a survey. Future Generat Comput Syst 29(1):84–106

    Article  Google Scholar 

  • Geng J, Huang M-L, Li M-W, Hong W-C (2015) Hybridization of seasonal chaotic cloud simulated annealing algorithm in a SVR-based load forecasting model. Neurocomputing 151:1362–1373

    Article  Google Scholar 

  • Hosen MA, Khosravi A, Nahavandi S, Creighton D (2015) Improving the quality of prediction intervals through optimal aggregation. IEEE Trans Ind Electron 62(7):4420–4429

    Article  Google Scholar 

  • Javad S, Payman M, Hamidreza K (2017) Training echo estate neural network using harmony search algorithm. Int J Artif Intell 15(1):163–179

    Google Scholar 

  • Jin G, Liu L, Zhang P, Yu M (2015) Cost constrain load balanced ant colony scheduling of cloud environment. J Inf Comput Sci 12:1045–1054

    Article  Google Scholar 

  • Karger D, Stein C, Wein J (2010) Scheduling algorithms. Algorithms and theory of computation handbook: special topics and techniques. Chapman & Hall/CRC, London

    Google Scholar 

  • Kashan AH (2009) League championship algorithm: a new algorithm for numerical function optimization. In: 2009 international conference of soft computing and pattern recognition. IEEE, pp 43–48

  • Kaur A, Kaur B (2019) Load balancing optimization based on hybrid heuristic-metaheuristic techniques in cloud environment. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2019.02.010

    Article  Google Scholar 

  • Kolodziej J, Khan SU, Xhafa F (2011) Genetic algorithms for energy-aware scheduling in computational grids. In: 2011 international conference on P2P, parallel, grid, cloud and internet computing. IEEE, pp 17–24

  • Kulkarni AK, Annappa B (2019) Context aware VM placement optimization technique for heterogeneous IaaS cloud. IEEE Access 7:89702–89713

    Article  Google Scholar 

  • Li K, Xu G, Zhao G, Dong Y, Wang D (2011) Cloud task scheduling based on load balancing ant colony optimization. In: 2011 sixth annual ChinaGrid conference. IEEE, pp 3–9

  • Li J, Qiu M, Ming Z, Quan G, Qin X, Gu Z (2012) Online optimization for scheduling preemptable tasks on IaaS cloud systems. J Parallel Distrib Comput 72:666–677

    Article  Google Scholar 

  • Malar ACJ, Kowsigan M, Krishnamoorthy N, Karthick S, Prabhu E, Venkatachalam K (2020) Multi constraints applied energy efficient routing technique based on ant colony optimization used for disaster resilient location detection in mobile ad-hoc network. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01767-9

    Article  Google Scholar 

  • Masdari M, Salehi F, Jalali M, Bidaki M (2017) A survey of PSO-based scheduling algorithms in cloudcomputing. J Netw Syst Manag 25:122–158

    Article  Google Scholar 

  • Mathew T, Sekaran KC, Jose J (2014) Study and analysis of various task scheduling algorithms in the cloud computing environment. In: Proceedings of the international conference on advances in computing, communications and informatics, Sept 24–27. IEEE Xplore Press, New Delhi, India, pp 658–664

  • Mohammed AS, Balaji BS, Basha SMS, Asha PN, Venkatachalam K (2020) FCO—fuzzy constraints applied cluster optimization technique for wireless adhoc networks. Comput Commun 154:501–508

    Article  Google Scholar 

  • Nagadevi S, Satyapriya K, Malathy D (2013) A survey on economic cloud schedulers for optimized task scheduling. Int J Adv Eng Technol 4:58–62

    Google Scholar 

  • Precup RE, David RC, Szedlak-Stinean AL, Petriu EM, Dragan F (2017) An easily understandable grey wolf optimizer and its application to fuzzy controller tuning. Algorithms 10:68

    Article  MathSciNet  Google Scholar 

  • Rahimi M, Ren J, Liu C, Vasilakos A, Venkatasubramanian N (2014) Mobile cloud computing: a survey state of art and future directions. Mobile Netw Appl 19(2):133–143

    Article  Google Scholar 

  • Sidhu J (2015) Ant colony optimization algorithm for independent task scheduling in cloud computing. Int J Appl Eng Res 10(1):535–544

    Google Scholar 

  • Singh K, Alam M, Sharma SK (2015) A survey of static scheduling algorithm for distributed computing system. Int J Comput Appl 129:25–30

    Google Scholar 

  • Srikanth GU, Maheswari VU, Shanthi A, Siromoney A (2015) Task scheduling model. Indian J Sci Technol 8:33–42

    Article  Google Scholar 

  • Tawfeek MA, El-Sisi A, Keshk AE, Torkey FA (2013) Cloud task scheduling based on ant colony optimization. In: 2013 8th international conference on computer engineering & systems (ICCES). IEEE, pp 64–69

  • Tsai CW, Rodrigues JJ (2014) Metaheuristic scheduling for cloud: a survey. IEEE Syst J 8:279–291

    Article  Google Scholar 

  • Tsai C-W, Huang W-C, Chiang M-H, Chiang M-C, Yang C-S (2014) A hyper-heuristic scheduling algorithm for cloud. IEEE Trans Cloud Comput 2:236–250

    Article  Google Scholar 

  • Vrkalovic S, Lunca E-C, Borlea I-D (2018) Model-free sliding mode and fuzzy controllers for reverse osmosis desalination plants. Int J Artif Intell 16(2):208–222

    Google Scholar 

  • Widmer M, Hertz A, Costa D (2008) Metaheuristics and scheduling. In: Lopez CP, Roubellat F (eds) Production scheduling. Wiley, Hoboken

    Google Scholar 

  • Yousif A, Abdullah AH, Nor SM, Bashir MB (2012) Optimizing job scheduling for computational grid based on firefly algorithm. In: 2012 IEEE conference on sustainable utilization and development in engineering and technology (STUDENT). IEEE, pp 97–101

  • Zhong SB, He ZS (2010) The scheduling algorithm of grid task based on PSO and cloud model. Key Eng Mater 439–440:1487–1492

    Article  Google Scholar 

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Correspondence to S. Peer Mohamed Ziyath.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-03948-0"

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Ziyath, S.P.M., Senthilkumar, S. RETRACTED ARTICLE: MHO: meta heuristic optimization applied task scheduling with load balancing technique for cloud infrastructure services. J Ambient Intell Human Comput 12, 6629–6638 (2021). https://doi.org/10.1007/s12652-020-02282-7

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  • DOI: https://doi.org/10.1007/s12652-020-02282-7

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