A Comparative Study of Task Scheduling Approaches in Cloud Computing
Keywords:
Cloud Computing, Task Scheduling, Heuristics Based Scheduling, Hybrid Scheduling
Abstract
The evolution of task scheduling algorithms in cloud computing environments has been a crucial aspect for efficient resource utilization and improved performance. This paper provides a comprehensive overview of the evolution of task scheduling algorithms in cloud computing, starting from the earliest algorithms to the latest ones. The paper starts by defining cloud computing and its basic characteristics, followed by a discussion on the importance of task scheduling in cloud computing. Then, the paper analyzes the various task scheduling algorithms that have been proposed over the years, including the First-Come-First-Served (FCFS) algorithm, Shortest Job First (SJF) algorithm, and Round Robin (RR) algorithm. The paper also covers more advanced algorithms such as the Load Balancing Algorithm, Priority Scheduling Algorithm, and Latest heuristics based hybrid Scheduling Algorithm. Finally, the paper concludes by highlighting the detailed merits and limitations of each latest approach viz. soft-computing techniques, Machine Learning techniques and other nature-inspired techniques.
References
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25. T. Bezdan, M. Zivkovic, N. Bacanin, I. Strumberger, E. Tuba, and M. Tuba, “Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm,” J. Intell. Fuzzy Syst., vol. 42, no. 1, pp. 411–423, Dec. 2021, doi: 10.3233/JIFS-219200.
2. F. Ebadifard and S. M. Babamir, “A PSO-based task scheduling algorithm improved using a load-balancing technique for the cloud computing environment,” Concurr. Comput. Pract. Exp., vol. 30, no. 12, p. e4368, Jun. 2018, doi: 10.1002/cpe.4368.
3. Y. Lu and N. Sun, “An effective task scheduling algorithm based on dynamic energy management and efficient resource utilization in green cloud computing environment,” Clust. Comput., vol. 22, no. S1, pp. 513–520, Jan. 2019, doi: 10.1007/s10586-017-1272-y.
4. S. P. Praveen, K. T. Rao, and B. Janakiramaiah, “Effective Allocation of Resources and Task Scheduling in Cloud Environment using Social Group Optimization,” Arab. J. Sci. Eng., vol. 43, no. 8, pp. 4265–4272, Aug. 2018, doi: 10.1007/s13369-017-2926-z.
5. K. Dubey, M. Kumar, and S. C. Sharma, “Modified HEFT Algorithm for Task Scheduling in Cloud Environment,” Procedia Comput. Sci., vol. 125, pp. 725–732, 2018, doi: 10.1016/j.procs.2017.12.093.
6. C. Wu and L. Wang, “A multi-model estimation of distribution algorithm for energy efficient scheduling under cloud computing system,” J. Parallel Distrib. Comput., vol. 117, pp. 63–72, Jul. 2018, doi: 10.1016/j.jpdc.2018.02.009.
7. M. Cheng, J. Li, and S. Nazarian, “DRL-cloud: Deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers,” in 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC), Jeju, , pp. 129–134, Jan. 2018. doi: 10.1109/ASPDAC.2018.8297294.
8. S. K. Mishra et al., “Energy-efficient VM-placement in cloud data center,” Sustain. Comput. Inform. Syst., vol. 20, pp. 48–55, Dec. 2018, doi: 10.1016/j.suscom.2018.01.002.
9. H. S. Yahia et al., “Comprehensive Survey for Cloud Computing Based Nature-Inspired Algorithms Optimization Scheduling,” Asian J. Res. Comput. Sci., pp. 1–16, May 2021, doi: 10.9734/ajrcos/2021/v8i230195.
10. A. Choudhary, I. Gupta, V. Singh, and P. K. Jana, “A GSA based hybrid algorithm for bi-objective workflow scheduling in cloud computing,” Future Gener. Comput. Syst., vol. 83, pp. 14–26, Jun. 2018, doi: 10.1016/j.future.2018.01.005.
11. D. Chaudhary and B. Kumar, “Cloudy GSA for load scheduling in cloud computing,” Appl. Soft Comput., vol. 71, pp. 861–871, Oct. 2018, doi: 10.1016/j.asoc.2018.07.046.
12. J. Wei and X. Zeng, “Optimal computing resource allocation algorithm in cloud computing based on hybrid differential parallel scheduling,” Clust. Comput., vol. 22, no. S3, pp. 7577–7583, May 2019, doi: 10.1007/s10586-018-2138-7.
13. K. Dubey and S. C. Sharma, “A novel multi-objective CR-PSO task scheduling algorithm with deadline constraint in cloud computing,” Sustain. Comput. Inform. Syst., vol. 32, p. 100605, Dec. 2021, doi: 10.1016/j.suscom.2021.100605.
14. H. Singh, S. Tyagi, P. Kumar, S. S. Gill, and R. Buyya, “Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions,” Simul. Model. Pract. Theory, vol. 111, p. 102353, Sep. 2021, doi: 10.1016/j.simpat.2021.102353.
15. E. H. Houssein, A. G. Gad, Y. M. Wazery, and P. N. Suganthan, “Task Scheduling in Cloud Computing based on Meta-heuristics: Review, Taxonomy, Open Challenges, and Future Trends,” Swarm Evol. Comput., vol. 62, p. 100841, Apr. 2021, doi: 10.1016/j.swevo.2021.100841.
16. M. Hussain, L.-F. Wei, A. Lakhan, S. Wali, S. Ali, and A. Hussain, “Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing,” Sustain. Comput. Inform. Syst., vol. 30, p. 100517, Jun. 2021, doi: 10.1016/j.suscom.2021.100517.
17. L. Abualigah and M. Alkhrabsheh, “Amended hybrid multi-verse optimizer with genetic algorithm for solving task scheduling problem in cloud computing,” J. Supercomput., vol. 78, no. 1, pp. 740–765, Jan. 2022, doi: 10.1007/s11227-021-03915-0.
18. Q.-H. Zhu, H. Tang, J.-J. Huang, and Y. Hou, “Task Scheduling for Multi-Cloud Computing Subject to Security and Reliability Constraints,” IEEECAA J. Autom. Sin., vol. 8, no. 4, pp. 848–865, Apr. 2021, doi: 10.1109/JAS.2021.1003934.
19. K. Pradeep and T. P. Jacob, “CGSA scheduler: A multi-objective-based hybrid approach for task scheduling in cloud environment,” Inf. Secur. J. Glob. Perspect., vol. 27, no. 2, pp. 77–91, Mar. 2018, doi: 10.1080/19393555.2017.1407848.
20. Z. Peng, J. Lin, D. Cui, Q. Li, and J. He, “A multi-objective trade-off framework for cloud resource scheduling based on the Deep Q-network algorithm,” Clust. Comput., vol. 23, no. 4, pp. 2753–2767, Dec. 2020, doi: 10.1007/s10586-019-03042-9.
21. G. Natesan and A. Chokkalingam, “An Improved Grey Wolf Optimization Algorithm Based Task Scheduling in Cloud Computing Environment,” Int. Arab J. Inf. Technol., pp. 73–81, Jan. 2020, doi: 10.34028/iajit/17/1/9.
22. M. Zivkovic, N. Bacanin, E. Tuba, I. Strumberger, T. Bezdan, and M. Tuba, “Wireless Sensor Networks Life Time Optimization Based on the Improved Firefly Algorithm,” in 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus, , pp. 1176–1181, Jun. 2020. doi: 10.1109/IWCMC48107.2020.9148087.
23. N. Bacanin, T. Bezdan, E. Tuba, I. Strumberger, and M. Tuba, “Monarch Butterfly Optimization Based Convolutional Neural Network Design,” Mathematics, vol. 8, no. 6, pp. 936, Jun. 2020, doi: 10.3390/math8060936.
24. Ramamoorthy, G. Ravikumar, B. Saravana Balaji, S. Balakrishnan, and K. Venkatachalam, “RETRACTED ARTICLE: MCAMO: multi constraint aware multi-objective resource scheduling optimization technique for cloud infrastructure services,” J. Ambient Intell. Humaniz. Comput., vol. 12, no. 6, pp. 5909–5916, Jun. 2021, doi: 10.1007/s12652-020-02138-0.
25. T. Bezdan, M. Zivkovic, N. Bacanin, I. Strumberger, E. Tuba, and M. Tuba, “Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm,” J. Intell. Fuzzy Syst., vol. 42, no. 1, pp. 411–423, Dec. 2021, doi: 10.3233/JIFS-219200.
Published
2022-02-28
How to Cite
Chhabra, B. (2022). A Comparative Study of Task Scheduling Approaches in Cloud Computing. CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES, 3(2), 18-24. Retrieved from https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/167
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Articles
