Effective Task Scheduling in Cloud Computing using Improved BAT Algorithm

Effective Task Scheduling in Cloud Computing using Improved BAT Algorithm
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-10
Year of Publication : 2022
Authors : Shyam Sunder Pabboju, Adilakshmi Thondepu
DOI : 10.14445/22315381/IJETT-V70I10P226

How to Cite?

Shyam Sunder Pabboju, Adilakshmi Thondepu, "Effective Task Scheduling in Cloud Computing using Improved BAT Algorithm," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 271-276, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P226

Abstract
The rapid adoption of cloud computing can be attributed to its high-performance distributed computing. Internet users can use its services and access shared resources through their own service providers. The scheduling of tasks is the primary challenge in cloud computing, which drags down the overall performance of the system. There is a requirement for an effective task-scheduling algorithm to increase the system's performance. The primary goal of the scheduling is to reduce the amount of time lost and the amount of work done while simultaneously increasing throughput. Therefore, the work of scheduling is necessary if one is to attain accuracy and correctness in the process of completing tasks in the cloud. Several different meta-heuristic task scheduling algorithms for the cloud, such as those based on Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), have been investigated, and all of them have demonstrated great performance in a reasonable amount of time (PSO). In this research piece, we used a metaheuristic strategy known as the bat algorithm. The Bat algorithm was developed expressly to optimise difficult issues. The job scheduling method that has been suggested is evaluated alongside scheduling algorithms that are based on genetic algorithms, ant colony optimization, and particle swarm optimization, respectively. In light of the findings, it is clear that the proposed algorithm has a superior performance to that of the other algorithms.

Keywords
Cloud, Scheduling, Genetic Algorithm, Improved BAT, ACO, PSO.

Reference
[1] P. Zhang and M. Zhou, "Dynamic Cloud Task Scheduling Based on a Two-Stage Strategy," In IEEE Transactions on Automation Science and Engineering, vol 15, no. 2, pp. 772-783, 2018, Doi: 10.1109/Tase.2017.2693688.
[2] Gareymr, Johnson Ds , “Computers and Intractability,” A Guide to the Theory of Np-Completeness, W.H. Freeman & Co., New York.
[3] Mala Kalra, Sarbjeet Singh, "A Review of Metaheuristic Scheduling Techniques in Cloud Computing,” Egyptian Informatics Journal, 2015.
[4] A. K. Jayswal, "Efficient Task Allocation for Cloud Using Bat Algorithm," 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 186-190, 2020. Doi: 10.1109/Pdgc50313.2020.9315845.
[5] Y. Song, F. Wang and X. Chen, “An Improved Genetic Algorithm For Numerical Function Optimization”, Applied Intelligence, vol. 49, no. 5, pp. 1880–1902, 2019.
[6] Zhao Junpu, Ban Jinyong, Jin Tongbiao, "Genetic Ant Colony Algorithm in Cloud Computing Resources Application in Scheduling", Computer Engineering and Design, vol. 38, no. 3, pp. 693-697, 2017.
[7] S. Sakthivel Padaiyatchi, S. Jaya, “Hybrid Bat Optimization Algorithm Applied to Optimal Reactive Power Dispatch Problems,” SSRG International Journal of Electrical and Electronics Engineering, vol 9, no. 1, pp. 1-5, 2022. Crossref, https://doi.org/10.14445/23488379/IJEEE-V9I1P101.
[8] Yang Xs, “A New Metaheuristic Bat-Inspired Algorithm,” Nat Inspired Coop Strateg Optim Comput Intell , vol. 284, pp. 65–74, 2010. http://Dx.Doi.Org/10.1007/978-3-642-12538-6_6.
[9] Jacob L, “Bat Algorithm For Resource Scheduling in Cloud Computing,” International Journal for Research in Applied Science and Engineering Technology, vol. 2, pp. 53–7, 2014.
[10] Suresh Kumar V, Aramudhan, “Hybrid Optimized List Scheduling and Trust Based Resource Selection In Cloud Computing,” Journal of Theoretical and Applied Information Technology, vol. 69, pp. 434–42, 2014.
[11] Raghavan S, Marimuthu, C, Sarwesh, P, & Chandrasekaran K, “Bat Algorithm For Scheduling Workflow Applications in Cloud,” 2015 International Conference on Electronic Design, Computer Networks & Automated Verification (EDCAV),IEEE, pp. 139–44, 2015.
[12] Wang T, Liu Z, Chen Y, Xu Y, Dai X, “Load Balancing Task Scheduling Based on Genetic Algorithm in Cloud Computing,” In: IEEE 12th International Conference on Dependable, Autonomic and Secure Computing, pp. 146–52, 2014. http://Dx.Doi.Org/10.1109/Dasc.2014.35.
[13] Zhu K, Song H, Liu L, Gao J, Cheng G, “Hybrid Genetic Algorithm For Cloud Computing Applications,” 2011 IEEE Asia-Pacific Services Computing Conference, pp. 182–7, 2011. http://Dx.Doi.Org/10. 1109/Apscc.2011.66.
[14] Chen W-N, Zhang J, Yu Y, “Workflow Scheduling In Grids: An Ant Colony Optimization Approach,” IEEE Congress on Evolutionary Computation, pp. 3308–15, 2007.
[15] Tawfeek Ma, El-Sisi A, Keshk Ae, Torkey Fa, “Cloud Task Scheduling Based on Ant Colony Optimization,” In: 8th International Conference on Computer Engneering System, pp. 64–9, 2013. http://Dx.Doi.Org/10.1109/ ICCEs.2013.6707172.
[16] Yassa S, Chelouah R, Kadima H, Granado B, “Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments,” Sci World Journal, 2013. http://Dx.Doi.Org/ 10.1155/2013/350934.
[17] Liu Z, Wang X, “A Pso-Based Algorithm for Load Balancing In Virtual Machines of Cloud Computing Environment,” Lect notes Comput Science (Including Subser Lect notes Artif Intell Lect notes Bioinformatics) 7331 Lncs:142–7, 2012. Http://Dx.Doi.Org/ 10.1007/978-3-642-30976-2_17.
[18] George S, “Hybrid Pso-Moba for Profit Maximization in Cloud Computing,” International Journal of Advanced Computer Science and Applications , vol. 6, pp. 159–63, 2015.
[19] Min Cao, Sean J. Bennett, Quanfei Shen, Ruqi Xu, "A Bat-Inspired Approach to Define Transition Rules For A Cellular Automaton Model Used to Simulate Urban Expansion", International Journal of Geographical Information Science, 2016.
[20] Prashant B. Jawade, D Sai Kumar, S. Ramachandram , “A Compact Analytical Survey on Task Scheduling in Cloud Computing Environment,” International Journal of Engineering Trends and Technology , vol. 69, no. 2, pp. 178-187, 2021.
[21] Wei, Chen Yuanyuan, "Cloud Computing Task Scheduling Model Based on Improved Ant Colony Algorithm", Computer Engineering, vol. 41, no. 2, pp.12-16, 2015.
[22] Agrawal, A.; Tripathi, S., “Particle Swarm Optimization With Adaptive Inertia Weight Based on Cumulative Binomial Probability”, Evolutionary Intelligence, vol. 14, pp.305–313, 2021.
[23] Yuan Zhengwu, Li Junqi, "Cloud Resource Scheduling Based on Improved Particle Swarm Algorithm", Journal of Computer Engineering and Design, vol. 37, no. 2, pp.401-412, 2016.
[24] Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., Buyya, R., “Cloudsim: A Toolkit For Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms,” Software: Practice and Experience, vol. 41, no. 1, pp. 23-50, 2011.