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Published in: The Journal of Supercomputing 8/2021

18-01-2021

Multi-objective heuristics algorithm for dynamic resource scheduling in the cloud computing environment

Authors: K. Lalitha Devi, S. Valli

Published in: The Journal of Supercomputing | Issue 8/2021

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Abstract

Cloud infrastructure provides resources needed for tasks for resource scheduling. This work uses a genetic algorithm based on encoded chromosome (GEC-DRP) to manage dynamic resource scheduling. However, the existing scheduling algorithm estimates the number of required physical machines (PM) needed for the client in the future. This developed scheduling algorithm schedules the tasks on cloud by calculating the number of virtual machines needed in the near future along with their predicted CPU and memory requirements, which is the main contribution of the work. K-means algorithm clusters the tasks based on CPU and memory usage as parameters. The future arrival of tasks for every cluster is predicted and accordingly, the required number of VMs is created. The incoming requests known as tasks are scheduled on the appropriate VM using the genetic algorithm (GA). Based on the workload prediction results, a cost-optimized resource scheduling strategy in cloud computing environment is proposed aiming at minimizing the total cost of rental virtual machines from the central cloud. Finally, a genetic algorithm is used to solve the resource scheduling strategy. The developed algorithms are evaluated by the workload prediction accuracy, the total cost of the cluster and the algorithm’s consuming time for solving the resource scheduling problems through the experiments. Finally, the effective of workload prediction algorithm based on SES and cost-optimized resource scheduling strategy is verified by simulation.

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Literature
2.
go back to reference AKMMR Mazumder, KMA Uddin, N Arbe, L Jahan and M Whaiduzzaman, (2019) Dynamic task scheduling algorithms in cloud computing. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, pp. 1280-1286. https://doi.org/10.1109/ICECA.2019.8822020 AKMMR Mazumder, KMA Uddin, N Arbe, L Jahan and M Whaiduzzaman, (2019) Dynamic task scheduling algorithms in cloud computing. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, pp. 1280-1286. https://​doi.​org/​10.​1109/​ICECA.​2019.​8822020
4.
go back to reference Tsai JT, Fang JC, Chou JH (2013) Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Comput Oper Res 40(12):3045–3055CrossRef Tsai JT, Fang JC, Chou JH (2013) Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Comput Oper Res 40(12):3045–3055CrossRef
5.
go back to reference Dabbagh M, Hamdaoui B, Guizani M, Rayes A (2015) Energy-efficient resource allocation and provisioning framework for cloud data centers. IEEE Trans Netw Serv Manage 12(3):377–391CrossRef Dabbagh M, Hamdaoui B, Guizani M, Rayes A (2015) Energy-efficient resource allocation and provisioning framework for cloud data centers. IEEE Trans Netw Serv Manage 12(3):377–391CrossRef
6.
go back to reference Keshanchi B, Souri A, Navimipour NJ (2017) An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J Syst Softw 124:1–21CrossRef Keshanchi B, Souri A, Navimipour NJ (2017) An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J Syst Softw 124:1–21CrossRef
7.
go back to reference Mehdi NA, Mamat A, Ibrahim H, Subramaniam SK (2011) Impatient task mapping in elastic cloud using genetic algorithm. J Comput Sci 7(6):877CrossRef Mehdi NA, Mamat A, Ibrahim H, Subramaniam SK (2011) Impatient task mapping in elastic cloud using genetic algorithm. J Comput Sci 7(6):877CrossRef
8.
go back to reference Arianyan E, Maleki D, Yari A, Arianyan I (2012) November. Efficient resource allocation in cloud data centers through genetic algorithm. IEEE Sixth Int Symp Telecommun (IST) 2012:566–570 Arianyan E, Maleki D, Yari A, Arianyan I (2012) November. Efficient resource allocation in cloud data centers through genetic algorithm. IEEE Sixth Int Symp Telecommun (IST) 2012:566–570
9.
go back to reference Tao F, Feng Y, Zhang L, Liao TW (2014) CLPS-GA: a case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling. Appl Soft Comput 19(2014):264–279CrossRef Tao F, Feng Y, Zhang L, Liao TW (2014) CLPS-GA: a case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling. Appl Soft Comput 19(2014):264–279CrossRef
11.
go back to reference Zhang Q, Zhani MF, Boutaba R, Hellerstein JL (2014) Dynamic heterogeneity-aware resource provisioning in the cloud. IEEE Trans Cloud Comput 2(1):14–28CrossRef Zhang Q, Zhani MF, Boutaba R, Hellerstein JL (2014) Dynamic heterogeneity-aware resource provisioning in the cloud. IEEE Trans Cloud Comput 2(1):14–28CrossRef
12.
go back to reference Kumar M, Sharma SC, Goel A, Singh SP (2019) A comprehensive survey for scheduling techniques in cloud computing. J Netw Comput Appl 143:1–33CrossRef Kumar M, Sharma SC, Goel A, Singh SP (2019) A comprehensive survey for scheduling techniques in cloud computing. J Netw Comput Appl 143:1–33CrossRef
16.
go back to reference Xiao Z, Song W, Chen Qi (2013) Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans Parallel Distrib Syst 24(6):1107–1117CrossRef Xiao Z, Song W, Chen Qi (2013) Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans Parallel Distrib Syst 24(6):1107–1117CrossRef
17.
go back to reference Duan H, Chen C, Min G, Wu Y (2017) Energy-aware scheduling of virtual machines in heterogeneous cloud computing systems. Future Gener Comput Syst 74:142–150CrossRef Duan H, Chen C, Min G, Wu Y (2017) Energy-aware scheduling of virtual machines in heterogeneous cloud computing systems. Future Gener Comput Syst 74:142–150CrossRef
19.
go back to reference Xu Y, Li K, Hu J, Li K (2014) A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf Sci 270:255–287MathSciNetCrossRef Xu Y, Li K, Hu J, Li K (2014) A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf Sci 270:255–287MathSciNetCrossRef
20.
go back to reference Singh, S. and Kalra, M., (2014) Scheduling of independent tasks in cloud computing using modified genetic algorithm. International conference on computational intelligence and communication networks (CICN) pp. 565–569 Singh, S. and Kalra, M., (2014) Scheduling of independent tasks in cloud computing using modified genetic algorithm. International conference on computational intelligence and communication networks (CICN) pp. 565–569
21.
go back to reference Hu H, Li Z, Hu H, Chen J, Ge J, Li C, Chang V (2018) Multi-objective scheduling for scientific workflow in multicloud environment. J Netw Comput Appl 114:108–122CrossRef Hu H, Li Z, Hu H, Chen J, Ge J, Li C, Chang V (2018) Multi-objective scheduling for scientific workflow in multicloud environment. J Netw Comput Appl 114:108–122CrossRef
22.
go back to reference Kar, I., Parida, R.R. and Das, H., (2016) Energy aware scheduling using genetic algorithm in cloud data centers. International conference on electrical, electronics, and optimization techniques (ICEEOT), pp. 3545–3550 Kar, I., Parida, R.R. and Das, H., (2016) Energy aware scheduling using genetic algorithm in cloud data centers. International conference on electrical, electronics, and optimization techniques (ICEEOT), pp. 3545–3550
23.
go back to reference Zhu Z, Zhang G, Li M, Liu X (2015) Evolutionary multi-objective workflow scheduling in cloud. IEEE Trans Parallel Distrib Syst 27(5):1344–1357CrossRef Zhu Z, Zhang G, Li M, Liu X (2015) Evolutionary multi-objective workflow scheduling in cloud. IEEE Trans Parallel Distrib Syst 27(5):1344–1357CrossRef
24.
go back to reference Zheng Z, Wang R, Zhong H and Zhang X, (2011) An approach for cloud resource scheduling based on Parallel Genetic Algorithm. IEEE. In 2011 3rd International Conference on Computer Research and Development (Vol. 2, pp. 444–447) Zheng Z, Wang R, Zhong H and Zhang X, (2011) An approach for cloud resource scheduling based on Parallel Genetic Algorithm. IEEE. In 2011 3rd International Conference on Computer Research and Development (Vol. 2, pp. 444–447)
25.
go back to reference Ren, X., Lin, R. and Zou, H., (2011) A dynamic load balancing strategy for cloud computing platform based on exponential smoothing forecast. In IEEE international conference on cloud computing and intelligence systems (CCIS), pp. 220–224 Ren, X., Lin, R. and Zou, H., (2011) A dynamic load balancing strategy for cloud computing platform based on exponential smoothing forecast. In IEEE international conference on cloud computing and intelligence systems (CCIS), pp. 220–224
26.
go back to reference Hartigan JA, Wong MA (1979) Algorithm AS 136: a k-means clustering algorithm. J R Stat Soc Ser C (Applied Statistics) 28(1):100–108MATH Hartigan JA, Wong MA (1979) Algorithm AS 136: a k-means clustering algorithm. J R Stat Soc Ser C (Applied Statistics) 28(1):100–108MATH
29.
go back to reference Reiss, C., Tumanov, A., Ganger, G.R., Katz, R.H. and Kozuch, M.A., (2012) Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In Proceedings of the third ACM symposium on cloud computing, p. 7 Reiss, C., Tumanov, A., Ganger, G.R., Katz, R.H. and Kozuch, M.A., (2012) Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In Proceedings of the third ACM symposium on cloud computing, p. 7
30.
go back to reference Calheiros R, Ranjan R, De Rose C, Rajkumar B (2009) CloudSim: a novel framework for modeling and simulation of cloud computing infrastructures and services Calheiros R, Ranjan R, De Rose C, Rajkumar B (2009) CloudSim: a novel framework for modeling and simulation of cloud computing infrastructures and services
33.
go back to reference Madni SHH, Latiff MSA, Abdullahi M, Usman MJ (2017) Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment. PLoS ONE 12(5):e0176321CrossRef Madni SHH, Latiff MSA, Abdullahi M, Usman MJ (2017) Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment. PLoS ONE 12(5):e0176321CrossRef
34.
go back to reference Pakhira MK, (2014) A linear time-complexity k-means algorithm using cluster shifting. IEEE, In 2014 international conference on computational intelligence and communication networks (pp. 1047–1051) Pakhira MK, (2014) A linear time-complexity k-means algorithm using cluster shifting. IEEE, In 2014 international conference on computational intelligence and communication networks (pp. 1047–1051)
36.
go back to reference Liu, C.Y., Zou, C.M. and Wu, P., (2014) A Task scheduling algorithm based on genetic algorithm and ant colony optimization in cloud computing. Proceedings of the 13th international symposium on distributed computing and applications to business, engineering and science, pp. 68–72 Liu, C.Y., Zou, C.M. and Wu, P., (2014) A Task scheduling algorithm based on genetic algorithm and ant colony optimization in cloud computing. Proceedings of the 13th international symposium on distributed computing and applications to business, engineering and science, pp. 68–72
37.
go back to reference Jiang Y, Perng CS, Li T, Chang RN (2013) Cloud analytics for capacity planning and instant vm provisioning. IEEE Trans Netw Serv Manage 10(3):312–325CrossRef Jiang Y, Perng CS, Li T, Chang RN (2013) Cloud analytics for capacity planning and instant vm provisioning. IEEE Trans Netw Serv Manage 10(3):312–325CrossRef
38.
go back to reference Ghorbani, M., Wang, Y., Xue, Y., Pedram, M. and Bogdan, P., (2014). Prediction and control of bursty cloud workloads: a fractal framework. Proceedings of the 2014 ACM international conference on hardware/software codesign and system synthesis p. 12 Ghorbani, M., Wang, Y., Xue, Y., Pedram, M. and Bogdan, P., (2014). Prediction and control of bursty cloud workloads: a fractal framework. Proceedings of the 2014 ACM international conference on hardware/software codesign and system synthesis p. 12
41.
go back to reference Zhang P, Zhou M (2017) Dynamic cloud task scheduling based on a two-stage strategy. IEEE Trans Autom Sci Eng 15(2):772–783CrossRef Zhang P, Zhou M (2017) Dynamic cloud task scheduling based on a two-stage strategy. IEEE Trans Autom Sci Eng 15(2):772–783CrossRef
Metadata
Title
Multi-objective heuristics algorithm for dynamic resource scheduling in the cloud computing environment
Authors
K. Lalitha Devi
S. Valli
Publication date
18-01-2021
Publisher
Springer US
Published in
The Journal of Supercomputing / Issue 8/2021
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-020-03606-2

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