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A Comparative Analysis for Energy Efficiency in Cloud Computing using CSO

by Sabyasachi Narendrasingh, Ashis Kumar Mishra, Subasish Mohapatra
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 13
Year of Publication: 2023
Authors: Sabyasachi Narendrasingh, Ashis Kumar Mishra, Subasish Mohapatra
10.5120/ijca2023922811

Sabyasachi Narendrasingh, Ashis Kumar Mishra, Subasish Mohapatra . A Comparative Analysis for Energy Efficiency in Cloud Computing using CSO. International Journal of Computer Applications. 185, 13 ( Jun 2023), 30-37. DOI=10.5120/ijca2023922811

@article{ 10.5120/ijca2023922811,
author = { Sabyasachi Narendrasingh, Ashis Kumar Mishra, Subasish Mohapatra },
title = { A Comparative Analysis for Energy Efficiency in Cloud Computing using CSO },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2023 },
volume = { 185 },
number = { 13 },
month = { Jun },
year = { 2023 },
issn = { 0975-8887 },
pages = { 30-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number13/32758-2023922811/ },
doi = { 10.5120/ijca2023922811 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:25:59.495850+05:30
%A Sabyasachi Narendrasingh
%A Ashis Kumar Mishra
%A Subasish Mohapatra
%T A Comparative Analysis for Energy Efficiency in Cloud Computing using CSO
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 13
%P 30-37
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now a day, many organizations and independent consumers are heavily utilizing cloud services for their scalability, reliability, and low expenses. Developing management techniques used to minimize energy consumption, boost profitability, and decrease environmental impact is a key aspect of cloud data centers. Every consumer's usage of service can generate a huge amount of data. Therefore, it will be very expensive to transfer data between two dependent resources. This paper suggests a meta heuristic-based optimization process called the Cat swarm optimization algorithm to improve the performance and energy efficiency in cloud resource allocation. Also, its performance is compared with gray wolf optimization and Whale Optimization Algorithms with a variation in of population sizes and iterations. Also, energy efficiency and throughput are calculated.

References
  1. Moganarangan, N., Babukarthik, R. G., Bhuvaneswari, S., Basha, M. S., & Dhavachelvan, P. (2016). A novel algorithm for reducing energy-consumption in cloud computing environment: Web service computing approach. Journal of King Saud University-Computer and Information Sciences, 28(1), 55-67.
  2. Ficco, M., & Palmieri, F. (2015). Introducing fraudulent energy consumption in cloud infrastructures: A new generation of denial-of-service attacks. IEEE Systems Journal, 11(2), 460-470.
  3. Ahvar, E., Orgerie, A. C., & Lebre, A. (2019). Estimating Energy Consumption of Cloud, Fog, and Edge Computing Infrastructures. IEEE Transactions on Sustainable Computing, 7(2), 277-288.
  4. El Kafhali, S., & Salah, K. (2018). Modeling and analysis of performance and energy consumption in cloud data centers. Arabian Journal for Science and Engineering, 43(12), 7789-7802.
  5. Vishwanath, A., Jalali, F., Hinton, K., Alpcan, T., Ayre, R. W., & Tucker, R. S. (2015). Energy consumption comparison of interactive cloud-based and local applications. IEEE Journal on selected areas in communications, 33(4), 616-626.
  6. Prabhakaran, N., & Nedunchelian, R. (2023). Oppositional Cat Swarm Optimization-Based Feature Selection Approach for Credit Card Fraud Detection. Computational Intelligence and Neuroscience, 2023.
  7. Li, X., Guo, C., Li, C., Xu, T., & Wu, S. (2022). Power Grid Low Carbon Collaborative Planning Method Using Improved Cat Swarm Optimization Algorithm in Edge Cloud Computing Environment. Wireless Communications and Mobile Computing, 2022.
  8. Ji, X. F., Pan, J. S., Chu, S. C., Hu, P., Chai, Q. W., & Zhang, P. (2020). Adaptive cat swarm optimization algorithm and its applications in vehicle routing problems. Mathematical Problems in Engineering, 2020, 1-14.
  9. Yadav, R., Zhang, W., Kaiwartya, O., Singh, P. R., Elgendy, I. A., & Tian, Y. C. (2018). Adaptive energy-aware algorithms for minimizing energy consumption and SLA violation in cloud computing. IEEE Access, 6, 55923-55936.
  10. Xie, G., Zeng, G., Jiang, J., Fan, C., Li, R., & Li, K. (2020). Energy management for multiple real-time workflows on cyber–physical cloud systems. Future Generation Computer Systems, 105, 916-931.
  11. . Cai, Y., Gu, J., Pan, H., Zhang, H., & Zhao, T. (2021). Tabu Genetic Cat Swarm Algorithm Analysis of Optimization Arrangement on Mistuned Blades Based on CUDA. Shock and Vibration, 2021, 1-18.
  12. Karthikeyan, K., Sunder, R., Shankar, K., Lakshmanaprabu, S. K., Vijayakumar, V., Elhoseny, M., & Manogaran, G. (2020). Energy consumption analysis of Virtual Machine migration in cloud using hybrid swarm optimization (ABC–BA). The Journal of Supercomputing, 76, 3374-3390.
  13. Yang, J., Jiang, B., Lv, Z., & Choo, K. K. R. (2020). A task scheduling algorithm considering game theory designed for energy management in cloud computing. Future Generation computer systems, 105, 985-992.
  14. Seyyedabbasi, A., & Kiani, F. (2022). Sand Cat swarm optimization: A nature-inspired algorithm to solve global optimization problems. Engineering with Computers, 1-25.
  15. Li, G., Yan, J., Chen, L., Wu, J., Lin, Q., & Zhang, Y. (2019). Energy consumption optimization with a delay threshold in cloud-fog cooperation computing. IEEE access, 7, 159688-159697.
  16. Du, Y., Wang, J. L., & Lei, L. (2019). Multi-objective scheduling of cloud manufacturing resources through the integration of Cat swarm optimization and Firefly algorithm. Advances in Production Engineering & Management, 14(3).
  17. Soto, R., Crawford, B., Aste Toledo, A., Castro, C., Paredes, F., & Olivares, R. (2019). Solving the manufacturing cell design problem through binary cat swarm optimization with dynamic mixture ratios. Computational intelligence and neuroscience, 2019.
  18. Fernández-Cerero, D., Fernández-Montes, A., Jakóbik, A., Kołodziej, J., & Toro, M. (2018). SCORE: Simulator for cloud optimization of resources and energy consumption. Simulation Modelling Practice and Theory, 82, 160-173.
  19. Pappula, L., & Ghosh, D. (2018). Cat swarm optimization with normal mutation for fast convergence of multimodal functions. Applied soft computing, 66, 473-491.
  20. Nie, X., Wang, W., & Nie, H. (2017). Chaos quantum-behaved cat swarm optimization algorithm and its application in the PV MPPT. Computational Intelligence and Neuroscience, 2017.
  21. Gabi, D., Ismail, A. S., Zainal, A., Zakaria, Z., & Al-Khasawneh, A. (2017, May). Cloud scalable multi-objective task scheduling algorithm for cloud computing using cat swarm optimization and simulated annealing. In 2017 8th International Conference on Information Technology (ICIT) (pp. 599-604). IEEE.
  22. Kumar, Y., & Singh, P. K. (2018). Improved cat swarm optimization algorithm for solving global optimization problems and its application to clustering. Applied Intelligence, 48, 2681-2697.
  23. Naim, A. A., El Bakrawy, L. M., & Ghali, N. I. (2017). A hybrid Cat Optimization and K-median for Solving Community Detection Problem. Asian Journal of Applied Sciences, 5(5).
  24. Hadi, I., & Sabah, M. (2014). Enhanced hybrid cat swarm optimization based on fitness approximation method for efficient motion estimation. International Journal of Hybrid Information Technology, 7(6), 345-364.
  25. Bilgaiyan, S., Sagnika, S., & Das, M. (2014, February). Workflow scheduling in cloud computing environment using cat swarm optimization. In 2014 IEEE International Advance Computing Conference (IACC) (pp. 680-685). IEEE.
  26. . Peng, H., Wen, W. S., Tseng, M. L., & Li, L. L. (2019). Joint optimization method for task scheduling time and energy consumption in mobile cloud computing environment. Applied Soft Computing, 80, 534-545.
  27. Strumberger, I., Bacanin, N., Tuba, M., & Tuba, E. (2019). Resource scheduling in cloud computing based on a hybridized whale optimization algorithm. Applied Sciences, 9(22), 4893.
  28. Chang, Z., Zhou, Z., Ristaniemi, T., & Niu, Z. (2017, December). Energy efficient optimization for computation offloading in fog computing system. In GLOBECOM 2017-2017 IEEE Global Communications Conference (pp. 1-6). IEEE.
  29. Yan, D., Cao, H., Yu, Y., Wang, Y., & Yu, X. (2020). Single-objective/multiobjective cat swarm optimization clustering analysis for data partition. IEEE Transactions on Automation Science and Engineering, 17(3), 1633-1646.
  30. Zhong, W., Zhuang, Y., Sun, J., & Gu, J. (2018). A load prediction model for cloudcomputing using PSO-based weighted wavelet support vector machine. Applied Intelligence, 48, 4072-4083.
  31. Vasudevan, M., Tian, Y. C., Tang, M., Kozan, E., & Zhang, X. (2018). Energy-efficient application assignment in profile-based data center management through a repairing genetic algorithm. Applied Soft Computing, 67, 399-408.
  32. Zekić-Sušac, M., Mitrović, S., & Has, A. (2021). Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities. International journal of information management, 58, 102074.
  33. Askarizade Haghighi, M., Maeen, M., & Haghparast, M. (2019). An energy-efficient dynamic resource management approach based on clustering and meta-heuristic algorithms in cloud computing IaaS platforms: Energy efficient dynamic cloud resource management. Wireless Personal Communications, 104, 1367-1391.
  34. Prassanna, J., & Venkataraman, N. (2021). Adaptive regressive holt–winters workload prediction and firefly optimized lottery scheduling for load balancing in cloud. Wireless Networks, 27, 5597-5615.
  35. Arianyan, E., Taheri, H., & Khoshdel, V. (2017). Novel fuzzy multi objective DVFS-aware consolidation heuristics for energy and SLA efficient resource management in cloud data centers. Journal of Network and Computer Applications, 78, 43-61.
  36. Horri, A., & Dastghaibyfard, G. (2015). A novel cost based model for energy consumption in cloud computing. The Scientific World Journal, 2015.
  37. Hanini, M., Kafhali, S. E., & Salah, K. (2019). Dynamic VM allocation and traffic control to manage QoS and energy consumption in cloud computing environment. International Journal of Computer Applications in Technology, 60(4), 307-316.
Index Terms

Computer Science
Information Sciences

Keywords

Datacenters CSO GWO WOA Cloud Computing Energy Efficiency Throughput.

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