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Improving completion time and execution time using FSMPIA: A case study | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 13، شماره 1، خرداد 2022، صفحه 3707-3721 اصل مقاله (629.97 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.6148 | ||
| نویسندگان | ||
| Mehran Mokhtari* 1؛ Peyman Bayat2 | ||
| 1College of Skills and Entrepreneurship, Qaemshar Branch, Islamic Azad University, Qaemshar, Iran | ||
| 2Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran | ||
| تاریخ دریافت: 17 خرداد 1400، تاریخ بازنگری: 07 مهر 1400، تاریخ پذیرش: 25 مهر 1400 | ||
| چکیده | ||
| Cloud computing (CC) has facilitated the use, access, and storage of resources via sharing them with customers of different organizations. To shorten the completion time and execution time, we introduced the fuzzy k-means (FKM) clustering method, which is based on the new fuzzy entropy. This method was combined with Greedy\_SMPIA, Max-Min\_SMPIA, Min-Min\_SMPIA, GA\_SMPIA and PSO\_SMPIA to make virtual machines (VMs) smarter. The FKM clustering method was implemented for both Non-SMPIA and SMPIA. The simulation results in MATLAB showed that the improvement of completion time of tasks with GA\_SMPIA was up to 88.43\% more than other studied methods. Execution time was improved also further improved to 55.37\% compared to the other methods studied. The fuzzy smart MPI approach (FSMPIA) performs better than Non-FSMPIA. Also, a comparison of both methods shows that the FSMPIA performance is 32.49\% and 11.26\% higher than that of the SMPIA in terms of competition time and resource utilization (RU), respectively. | ||
| کلیدواژه‌ها | ||
| Cloud computing؛ completion time؛ execution time؛ resource utilization؛ SMPIA؛ fuzzy k-mean | ||
| مراجع | ||
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[1] M. Mokhtari, P. Bayat and H. Motameni, Multi-objective task scheduling using smart MPI-based cloud resources, Comput. Inf. 40(1) (2021). [2] M. Mokhtari, P. Bayat and H. Motameni, Solving the task starvation and resources problem using optimized SMPIA in cloud, Comput. Syst. Sci. Engin. 42(2) (2022) 659–675. [3] R.A. Al-Arasi and A. Saif, Task scheduling in cloud computing based on metaheuristic techniques: A review paper, EAI Endorsed Trans. Cloud Syst. 6(17) (2020). [4] R. Jain and A. Nayyar, A novel homomorphic RASD framework for secured data access and storage in cloud computing, Open Comput. Sci. 10(1) (2020) 431–443. [5] L. Esp´ınola, D. Franco and E. Luque, Mcm: A new mpi communication management for cloud environments, Procedia Comput. Sci. 108 (2017) 2303–2307. [6] H.M. Wei, J. Gao, P. Qing, K. Yu, Y.F. Fang and M.L. Li, A framework for MPI runtime communication deadlock detection, J. Comput. Sci. Technol. 35 (2020) 395–411. [7] H. Hassanpour and M. Mokhtari, Proposing a Dynamic Routing Approach to Improve Performance of Iran Data Network, 2009. [8] M.M.S. Maswood, C. Develder, E. Madeira and D. Medhi, A stable matching based elephant flow scheduling algorithm in data center networks, Comput. Networks 120 (2017) 186–197. [9] B. Liang, X. Dong, Y. Wang and X. Zhang, A low-power task scheduling algorithm for heterogeneous cloud computing, J. Supercomput. 2020 (2020) 1–25. [10] M.Y. Wu, W. Shu and H. Zhang, Segmented min-min: A static mapping algorithm for meta-tasks on heterogeneous computing systems, Proc. 9th Heterog. Computing Workshop (HCW 2000)(Cat. No. PR00556). IEEE, 2000. [11] T.C. Hung, L.N. Hieu, P.T. Hy and N.X. Phi, MMSIA: improved max-min scheduling algorithm for load balancing on cloud computing, Proc. 3rd Int. Conf. Machine Learn. Soft Comput. 2019. [12] J. Zhang, J. Zhai, W. Chen and W. Zheng, Wavelet-Based Adaptive Solvers on Multi-Core Architectures for the Simulation of Complex Systems, Eur. Conf. Parall. Process. Springer, Berlin, Heidelberg, 2009. [13] J.K. Konjaang and X. Lina, Multi-objective workflow optimization strategy (MOWOS) for cloud computing, J. Cloud Comput. 10(1) (2021) 1–19. [14] P. Brucker, Scheduling Algorithms, 5th edition, Berlin, Springer, 2006. [15] S. Elmougy, S. Sarhan and M. Joundy, A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling technique, J. Cloud Comput. 6(1) (2017) 1–12. [16] V. Singh and N.K. Verma, An entropy-based variable feature weighted fuzzy k-means algorithm for high dimensional data, arXiv preprint arXiv:1912.11209 (2019). [17] Z. Xie, X. Shao and Y. Xin, A scheduling algorithm for cloud computing system based on the driver of dynamic essential path, PloS one 11(8) (2016) e0159932. [18] N. Almezeini and A. Hafez, An Enhanced Workflow Scheduling Algorithm in Cloud Computing, CLOSER 2 (2016) 67—73. [19] A. Manasrah ad H. Ba Ali, Workflow scheduling using hybrid GA-PSO algorithm in cloud computing, Wireless Commun. Mobile Comput. 2018 (2018). [20] P. Kumar and A. Verma, Scheduling using improved genetic algorithm in cloud computing for independent tasks, Proc. Int. Conf. Adv. Comput. Commun. Inf. ACM, 2012, pp. 137-142. [21] T.P. Jacob and K. Pradeep, A multi-objective optimal task scheduling in cloud environment using cuckoo particle swarm optimization, Wireless Person. Commun. 109(1) (2019) 315–331. [22] F. Luo, Y. Yuan, W. Ding and H. Lu, An improved particle swarm optimization algorithm based on adaptive weight for task scheduling in cloud computing, Proc. 2nd Int. Conf. Comput. Sci. Appl. Engin. ACM, vol. 142, 2018. [23] L. Guo, S. Zhao, S. Shen and C. Jiang, Task scheduling optimization in cloud computing based on heuristic algorithm, J. Networks 7(3) (2012) 547. [24] M.H. Hilman, M. A. Rodriguez and R. Buyya, Task runtime prediction in scientific workflows using an online incremental learning approach, Proc. IEEE/ACM 11th Int. Conf, Util. Cloud Comput., 2018, pp. 93-102. [25] B. Barzegar, H. Motameni and A. Movaghar, EATSDCD: A green energy-aware scheduling algorithm for parallel task-based application using clustering, duplication and DVFS technique in cloud datacenters, J. Intell. Fuzzy Syst. 36(6) (2019) 5135–5152. [26] J. Masoudi, B. Barzegar and H. Motameni, Energy-aware virtual machine allocation in DVFS-enabled cloud data centers, IEEE Access 10 (2021) 3617–3630. [27] S. Fatehi, H. Motameni, B. Barzegar and M. Golsorkhtabaramiri, Energy-aware multi-objective algorithm for task scheduling on DVFS-enabled cloud datacenters using fuzzy NSGA-II, Int. J. Nonlinear Anal. Appl. 12(2) (2021) 2303–2331. [28] Z. Peng, B. Barzegar, M. Yarahmadi, H. Motameni and P. Pirouzmand, Energy-aware scheduling of workflow using a heuristic method on green cloud, Sci. Programm. 2020 (2020). [29] M.H. Nejat, H. Motameni, H. Vahdat-Nejad and B. Barzegar, Efficient cloud service ranking based on uncertain user requirements, Cluster Comput. 25(1) (2022) 485–502. | ||
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