Abstract
The cloud computing environment provides computing assets in a pay-per-use way for IT service providers. Guaranteeing QoS amid job scheduling is a most noticeable need. This paper proposed an algorithm that expects to accomplish all-around adjusted load crosswise over virtual machines for minimizing makespan time. The proposed algorithm provides balanced scheduling solutions by employing the honey bee load balancing and improvement detection operator to conclude which low-level heuristic is to be utilized to search improved candidate solutions. The consequences of the proposed task scheduling algorithm are matched with existing heuristic-based scheduling procedures. The experimental consequences demonstrate that our approach is efficient when it is compared with the existing algorithms.
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27 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04238-5
Abbreviations
- ϕ max :
-
Maximum amount of iterations performed by the selected low-level algorithm
- ϕ ni :
-
Maximum numbers of iterations solutions are not improved
- P :
-
Population of solutions
- H i :
-
Heuristic algorithm from candidate pool
- F1 :
-
Improvement detection operator
- VmLoad :
-
Load on virtual machine (VM)
- N :
-
Quantity of tasks
- Task length :
-
Length of the task
- VM Mips :
-
Million instructions per second (MIPS) of the virtual machine
- Vm Capacity :
-
Capacity of VM
- PE Number :
-
Quantity of processing elements in VM
- PE Mips :
-
MIPS speed of processing element of VM
- VM BW :
-
Bandwidth linked with VM
- PT_VM i :
-
Processing time of virtual machine
- σ:
-
Standard deviation of load
- X :
-
Average processing time of virtual machine
- Supply Vm :
-
Supply of VM
- Demand Vm :
-
Demand of VM
- Processing Time :
-
Total processing time
- D i :
-
Degree of imbalance
References
Agarwal M, Srivastava GMS (2016) A genetic algorithm inspired task scheduling in cloud computing. Proc IEEE Int Conf Comput Commun Autom. https://doi.org/10.1109/CCAA.2016.7813746
Al-Olimat HS, Alam M, Green R, Lee JK (2015) Cloudlet scheduling with particle swarm optimization. Proc IEEE Int Conf Commun Syst Netw Technol. https://doi.org/10.1109/CSNT.2015.252
Babu LD, Venkata Krishna P (2013) Honey bee behavior inspired the load balancing of tasks in cloud computing environments. J Appl Soft Comput 13(5):2292–2303. https://doi.org/10.1016/j.asoc.2013.01.025
Chiang M, Hsieh H, Tsai W, Ke M (2017) An improved task scheduling and load balancing algorithm under the heterogeneous cloud computing network. Proc IEEE Int Conf Aware Sci Technol. https://doi.org/10.1109/ICAwST.2017.8256465
Elhady G, Tawfeek M (2016) A comparative study of swarm intelligence algorithms for dynamic task scheduling in cloud computing. Proc IEEE Int Conf Intell Comput Inf Syst. https://doi.org/10.1109/IntelCIS.2015.7397246
Fakhfakh F, Kacem H, Kacem A (2014) Workflow scheduling in cloud computing: a survey. Proc IEEE Int Enterp Distrib Object Comput Conf Workshops Demonstr. https://doi.org/10.1109/EDOCW.2014.61
Goswami N, Garala K, Maheta P (2015) Cloud load balancing based on ant colony optimization algorithm. J Comput Eng (IOSR-JCE) 1(1):11–18. https://doi.org/10.13140/RG.2.1.4914.8001
Kumar P, Verma A (2012) Scheduling using improved genetic algorithm in cloud computing for independent tasks. Proc Int Conf Adv Comput Commun Inform. https://doi.org/10.1145/2345396.2345420
Li K, Xu G, Zhao G, Dong Y, Wang D (2011) Cloud Task scheduling based on load balancing ant colony optimization. Proc IEEE Annu China Grid Conf. https://doi.org/10.1109/ChinaGrid.2011.17
Liu Z, Wang X (2012) A PSO-based algorithm for load balancing in virtual machines of cloud computing environment. Proc Spring Int Conf Adv Swarm Intell. https://doi.org/10.1007/978-3-642-30976-2_17
Praveenchandar J, Tamilarasi A (2020) Dynamic resource allocation with optimized task scheduling and improved power management in cloud computing. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01794-6
Rajput S, Kushwah V (2016) A genetic based improved load balanced min-min task scheduling algorithm for load balancing in cloud computing. Proc IEEE Int Conf Comput Intell Commun Netw. https://doi.org/10.1109/CICN.2016.139
Selvarani S, Sadhasivam GS (2010) Improved cost-based algorithm for task scheduling in cloud computing. Proc IEEE Int Conf Comput Intell Comput Res. https://doi.org/10.1109/ICCIC.2010.5705847
Sutha K, Kadhar Nawaz GM (2016) Research perspective of job scheduling in cloud computing. Proc IEEE Int Conf Adv Comput. https://doi.org/10.1109/ICoAC.2017.7951746
Tawfeek M, El-Sisi A, Keshk A, Torkey F (2013) Cloud task scheduling based on ant colony optimization. J Arab Inf Technol 12(2):64–69. https://doi.org/10.1109/ICCES.2013.6707172
Tsai C, Huang W, Chiang M, Chiang M, Yang Chu-Sing (2014) A hyper-heuristic scheduling algorithm for cloud. IEEE Trans Cloud Comput 2(2):236–250. https://doi.org/10.1109/TCC.2014.2315797
Yang J, Chen Z (2010) Cloud computing research and security issues. Proc IEEE Int Conf Comput Intell Softw Eng. https://doi.org/10.1109/CISE.2010.5677076
Zhan Z, Zhang G, Ying L, Gong Y, Zhang J (2014) Load balance aware genetic algorithm for task scheduling in cloud computing. Proc Spring Int Conf Simul Evolut Learn. https://doi.org/10.1007/978-3-319-13563-2_54
Zhang H (2016) Research on job security scheduling strategy in cloud computing model. Proc IEEE Int Conf Intell Transport Big Data Smart City. https://doi.org/10.1109/ICITBS.2015.165
Zhang Y, Yang R (2017) Cloud computing task scheduling based on an improved particle swarm optimization algorithm. Proc IEEE Annu Conf Ind Electron Soc. https://doi.org/10.1109/IECON.2017.8217541
Zhang H, Li P, Zhou Z, Yu X (2013) A PSO-based hierarchical resource scheduling strategy on cloud computing. Proc Spring Int Conf Trustworthy Comput Serv. https://doi.org/10.1007/978-3-642-35795-4_41
Zuo L, Shu L, Dong S, Zhu C, Hara T (2015) A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. Proc IEEE Conf Access Big Data Serv Comput Intell Ind Syst. https://doi.org/10.1109/ACCESS.2015.2508940
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04238-5
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Gupta, A., Bhadauria, H.S. & Singh, A. RETRACTED ARTICLE: Load balancing based hyper heuristic algorithm for cloud task scheduling. J Ambient Intell Human Comput 12, 5845–5852 (2021). https://doi.org/10.1007/s12652-020-02127-3
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DOI: https://doi.org/10.1007/s12652-020-02127-3