Abstract
The Cloud environment had been the go-to for many users recently. Once request from users get submitted, cloud resources are put into action to fulfill the request. Scheduling is the primary task in cloud that needs to be up-to the mark for completing the requests swiftly. Multiple dynamic requests are submitted simultaneously by cloud users that necessitates precise and prompt scheduling in cloud. Scheduling in cloud may be hampered by various constraints, take for example the various QoS parameters that needs to be upheld. Though many researchers had proposed solutions for scheduling in cloud, improvisations can still be made by combining several QoS parameters that help attain optimized scheduling in cloud to boost the overall cloud performance. In this paper, we had proposed a Binary Grey Wolf Optimization (BGWO) algorithm to optimize the scheduling activity in cloud computing environment. The BGWO is a multi-heuristic algorithm where tasks are scheduled based on a fitness function, explicitly designed for achieving optimization goal. The fitness function that had been designed comprises of three prime parameters namely, the degree of imbalance (DoI), energy consumption and makespan. The performance efficiency of the proposed BGWO had been ascertained by comparing it with Oppositional based Grey Wolf Optimization algorithm (OGWO) and Mean Grey Wolf Optimization algorithm (Mean GWO) with respect to imbalance, energy and makespan parameters. The proposed algorithm had produced a cumulative improvement of 10.13% and 17.4% for makespan, 30.18% and 41.96% for DoI, 8.94% and 14.95% for energy consumption parameters. Detailed comparative results obtained had been described in the Results part of this research article.
Similar content being viewed by others
Data availability
The data can be shared on valid request made to corresponding author.
References
Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599–616. https://doi.org/10.1016/j.future.2008.12.001
Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: State-of-the-art and research challenges. J Internet Serv Appl 1(1):7–18. https://doi.org/10.1007/s13174-010-0007-6
Houssein EH, Gad AG, Wazery YM, Suganthan PN (2021) Task scheduling in cloud computing based on meta-heuristics: review, taxonomy, open challenges, and future trends. Swarm Evol Comput 62:100841. https://doi.org/10.1016/j.swevo.2021.100841
Ibrahim IM (2021) Task scheduling algorithms in cloud computing: a review. Turk J Comput Math Educ 12(4):1041–1053. https://doi.org/10.17762/turcomat.v12i4.612
Pradhan A, Bisoy SK, ADas, (2021) A Survey on PSO Based Meta-Heuristic Scheduling Mechanism in Cloud Computing Environment, J. King Saud Univ, Comput. Info. Scie. https://doi.org/10.1016/j.jksuci.2021.01.003
Jacob TP, Pradeep K (2019) A Multi-objective Optimal Task Scheduling in Cloud Environment Using Cuckoo Particle Swarm Optimization. Wirel Pers Commun 109(1):315–331. https://doi.org/10.1007/s11277-019-06566-w
Gobalakrishnan N, Arun C (2018) A new multi-objective optimal programming model for task scheduling using genetic gray wolf optimization in cloud computing. Comput J 61(10):1523–1536. https://doi.org/10.1093/comjnl/bxy009
Prasanna Kumar KR, Kousalya K (2020) Amelioration of task scheduling in cloud computing using crow search algorithm. Neural Comput Appl 32(10):5901–5907. https://doi.org/10.1007/s00521-019-04067-2
Natesan G, Chokkalingam A (2020) Multi-objective task scheduling using hybrid whale genetic optimization algorithm in heterogeneous computing environment. Wirel Pers Commun 110(4):1887–1913. https://doi.org/10.1007/s11277-019-06817-w
Pradeep K, Javid Ali L, Gobalakrishnan N, Raman CJ, Manikandan N (2021) CWOA: hybrid approach for task scheduling in cloud environment. Comput J. https://doi.org/10.1093/comjnl/bxab028
Golchi MM, Saraeian S, Heydari M (2019) A hybrid of firefly and improved particle swarm optimization algorithms for load balancing in cloud environments: performance evaluation. Comput Netw 162:106860. https://doi.org/10.1016/j.comnet.2019.106860
Li K (2019) Energy and time constrained scheduling for optimized quality of service. Sustain Comput Informatics Syst 22:134–138. https://doi.org/10.1016/j.suscom.2019.04.001
Ismayilov G, Topcuoglu HR (2020) Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing. Future Gener Comput Syst 102:307–322. https://doi.org/10.1016/j.future.2019.08.012
Mansouri N, Zade BMH, Javidi MM (2019) Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory. Comput Ind Eng 130:597–633. https://doi.org/10.1016/j.cie.2019.03.006
Pietri I, Sakellariou R (2019) A Pareto-based approach for CPU provisioning of scientific workflows on clouds. Future Gener Comput Syst 94:479–487. https://doi.org/10.1016/j.future.2018.12.004
Priya V, Kumar CS, Kannan R (2019) Resource scheduling algorithm with load balancing for cloud service provisioning. Appl Soft Comput 76:416–424. https://doi.org/10.1016/j.asoc.2018.12.021
Stavrinides GL, Karatza HD (2019) An energy-efficient, QoS-aware and cost-effective scheduling approach for real-time workflow applications in cloud computing systems utilizing DVFS and approximate computations. Future Gener Comput Syst 96:216–226. https://doi.org/10.1016/j.future.2019.02.019
Zhang Y, Zhou J, Sun J (2019) Scheduling bag-of-tasks applications on hybrid clouds under due date constraints. J Syst Archit 101:101654. https://doi.org/10.1016/j.sysarc.2019.101654
Zhou X, Zhang G, Sun J, Zhou J, Wei T, Hu S (2019) Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based HEFT, Future Gener. Comput Syst 93:278–289. https://doi.org/10.1016/j.future.2018.10.046
Kashikolaei SMG, Hosseinabadi AAR, Saemi B, Shareh MB, Sangaiah AK, Bian GB (2020) An enhancement of task scheduling in cloud computing based on imperialist competitive algorithm and firefly algorithm. J Supercomput 76(8):6302–6329. https://doi.org/10.1007/s11227-019-02816-7
Zhou Z, Li F, Zhu H, Xie H, Abawajy JH, Chowdhury MU (2020) An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments. Neural Comput Appl 32(6):1531–1541. https://doi.org/10.1007/s00521-019-04119-7
Ababneh J (2021) A hybrid approach based on grey wolf and whale optimization algorithms for solving cloud task scheduling problem. Math Prob Eng 2021:3517145. https://doi.org/10.1155/2021/3517145
Medara R, Singh RS (2021) Energy efficient and reliability aware workflow task scheduling in cloud environment. Wirel Pers Commun 119(2):1301–1320. https://doi.org/10.1007/s11277-021-08263-z
Abualigah L, Alkhrabsheh M (2022) Amended Hybrid multi-verse optimizer with genetic algorithm for solving task scheduling problem in cloud computing. J Super Comput 78(1):740–765. https://doi.org/10.1007/s11227-021-03915-0
Amer DA, Attiya G, Zeidan I, Nasr AA (2022) Elite learning Harris hawks optimizer for multi-objective task scheduling in cloud computing. J Super Comput 78(2):2793–2818. https://doi.org/10.1007/s11227-021-03977-0
Jain R, Sharma N (2022) A quantum inspired hybrid SSA–GWO algorithm for SLA based task scheduling to improve QoS parameter in cloud computing. Cluster Comput. https://doi.org/10.1007/s10586-022-03740-x
Li J, Zhang X, Wei J, Ji Z, Wei Z (2022) GARLSched: generative adversarial deep reinforcement learning task scheduling optimization for large-scale high performance computing systems. Future Gener Comput Syst 135:259–269. https://doi.org/10.1016/j.future.2022.04.032
Zade BMH, Mansouri N (2022) Improved red fox optimizer with fuzzy theory and game theory for task scheduling in cloud environment. J Comput Sci 63:101805. https://doi.org/10.1016/j.jocs.2022.101805
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
Hu P, Pan JS, Chu SC (2020) Improved binary grey wolf optimizer and its application for feature selection. Knowl Based Syst 195:105746. https://doi.org/10.1016/j.knosys.2020.105746
Abdullahi M, Ngadi MA (2016) Symbiotic Organism Search optimization based task scheduling in cloud computing environment. Future Gener Comput Syst 56:640–650. https://doi.org/10.1016/j.future.2015.08.006
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
All authors declare that they have no conflicts of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Natesan, G., Manikandan, N., Pradeep, K. et al. Task scheduling based on minimization of makespan and energy consumption using binary GWO algorithm in cloud environment. Peer-to-Peer Netw. Appl. 16, 2560–2573 (2023). https://doi.org/10.1007/s12083-023-01536-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12083-023-01536-3