EAEFA: An Efficient Energy-Aware Task Scheduling in Cloud Environment
DOI:
https://doi.org/10.4108/eetsis.3922Keywords:
Task scheduling, cloud computing, Electric fish optimization, HPC2NAbstract
The scheduling of tasks in the cloud is a major challenge for improving resource availability and decreasing the total execution time and energy consumption of operations. Due to its simplicity, efficiency, and effectiveness in identifying global optimums, electric fish optimisation (EFO) has recently garnered a lot of interest as a metaheuristic method for solving optimisation issues. In this study, we apply electric fish optimisation (EAEFA) to the problem of cloud task scheduling in an effort to cut down on power usage and turnaround time. The objective is to finish all tasks in the shortest possible time, or makespan, taking into account constraints like resource availability and task dependencies. In the EAEFA approach, a school of electric fish is used to solve a multi-objective optimization problem that represents the scheduling of tasks. Because electric fish are drawn to high-quality solutions and repelled by low-quality ones, the algorithm is able to converge to a global optimum. Experiments validate EAEFA's ability to solve the task scheduling issue in cloud computing. The suggested scheduling strategy was tested on HPC2N and other large-scale simulations of real-world workloads to measure its makespan time, energy efficiency and other performance metrics. Experimental results demonstrate that the proposed EAEFA method improves performance by more than 30% with respect to makespan time and more than 20% with respect to overall energy consumption compared to state-of-the-art methods.
References
Shukri, S. E., Al-Sayyed, R., Hudaib, A., & Mirjalili, S. (2021). Enhanced multi-verse optimizer for task scheduling in cloud computing environments. Expert Systems with Applications, 168, 114230.
Bezdan, T., Zivkovic, M., Bacanin, N., Strumberger, I., Tuba, E., & Tuba, M. (2022). Multi-objective task scheduling in a cloud computing environment by hybridized bat algorithm. Journal of Intelligent & Fuzzy Systems, 42(1), 411-423.
Amer, D. A., Attiya, G., Zeidan, I., & Nasr, A. A. (2022). Elite learning Harris hawks optimizer for multi-objective task scheduling in cloud computing. The Journal of Supercomputing, 78(2), 2793-2818.
Attiya, I., Abd Elaziz, M., Abualigah, L., Nguyen, T. N., & Abd El-Latif, A. A. (2022). Improved hybrid swarm intelligence for scheduling iot application tasks in the cloud. IEEE Transactions on Industrial Informatics.
Lim, J. (2022). Latency-Aware Task Scheduling for IoT Applications Based on Artificial Intelligence with Partitioning in Small-Scale Fog Computing Environments. Sensors, 22(19), 7326.
Hussain, S. M., & Begh, G. R. (2022). Hybrid heuristic algorithm for cost-efficient QoS aware task scheduling in fog–cloud environment. Journal of Computational Science, 64, 101828.
Najafizadeh, A., Salajegheh, A., Rahmani, A. M., & Sahafi, A. (2022). Multi-objective Task Scheduling in cloud-fog computing using goal programming approach. Cluster Computing, 25(1), 141-165.
Attiya, I., Abualigah, L., Elsadek, D., Chelloug, S. A., & Abd Elaziz, M. (2022). An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing. Mathematics, 10(7), 1100.
Kar, Arpan Kumar. "Bio inspired computing–a review of algorithms and scope of applications." Expert Systems with Applications 59 (2016): 20-32.
Ibrahim, Rehab Ali, et al. "An electric fish-based arithmetic optimization algorithm for feature selection." Entropy 23.9 (2021): 1189.
Thakur, Sanjeev, and Ankur Chaurasia. "Towards Green Cloud Computing: Impact of carbon footprint on environment." 2016 6th international conference-cloud system and big data engineering (Confluence). IEEE, 2016.
Chen, Xuan, et al. "A WOA-based optimization approach for task scheduling in cloud computing systems." IEEE Systems journal 14.3 (2020): 3117-3128.
Shu, Wanneng, Ken Cai, and Neal Naixue Xiong. "Research on strong agile response task scheduling optimization enhancement with optimal resource usage in green cloud computing." Future Generation Computer Systems 124 (2021): 12-20.
Li, Yibin, et al. "Energy optimization with dynamic task scheduling mobile cloud computing." IEEE Systems Journal 11.1 (2015): 96-105.
Kumar, M. Santhosh, and Ganesh Reddy Karri. "EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework." Sensors 23.5 (2023): 2445.
Haghnegahdar, L., Chen, Y., & Wang, Y. (2022). Enhancing dynamic energy network management using a multiagent cloud-fog structure. Renewable and Sustainable Energy Reviews, 162, 112439.
Momeni, H., & Mabhoot, N. (2021). An Energy-aware Real-time Task Scheduling Approach in a Cloud Computing Environment. Journal of AI and Data Mining, 9(2), 213-226.
Abd Elaziz, M., Abualigah, L., & Attiya, I. (2021). Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments. Future Generation Computer Systems, 124, 142-154.
Najafizadeh, A., Salajegheh, A., Rahmani, A. M., & Sahafi, A. (2022). Multi-objective Task Scheduling in cloud-fog computing using goal programming approach. Cluster Computing, 25(1), 141-165.
Abohamama, A. S., El-Ghamry, A., & Hamouda, E. (2022). Real-Time Task Scheduling Algorithm for IoT-Based Applications in the Cloud–Fog Environment. Journal of Network and Systems Management, 30(4), 1-35.
Fatehi, S., Motameni, H., Barzegar, B., & Golsorkhtabaramiri, M. (2021). Energy aware multi objective algorithm for task scheduling on DVFS-enabled cloud datacenters using fuzzy NSGA-II. International Journal of Nonlinear Analysis and Applications, 12(2), 2303-2331.
Zandvakili, A., Mansouri, N., & Javidi, M. M. (2021). Energy-aware task scheduling in cloud computing based on discrete pathfinder algorithm. International Journal of Engineering, 34(9), 2124-2136.
Y. Chen, F. Zhao, Y. Lu and X. Chen, "Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply," in Tsinghua Science and Technology, vol. 28, no. 3, pp. 421-432, June 2023, doi: 10.26599/TST.2021.9010050.
Shukri, S. E., Al-Sayyed, R., Hudaib, A., & Mirjalili, S. (2021). Enhanced multi-verse optimizer for task scheduling in cloud computing environments. Expert Systems with Applications, 168, 114230.
Amer, D. A., Attiya, G., Zeidan, I., & Nasr, A. A. (2022). Elite learning Harris hawks optimizer for multi-objective task scheduling in cloud computing. The Journal of Supercomputing, 78(2), 2793-2818.
Attiya, I., Abd Elaziz, M., Abualigah, L., Nguyen, T. N., & Abd El-Latif, A. A. (2022). Improved hybrid swarm intelligence for scheduling iot application tasks in the cloud. IEEE Transactions on Industrial Informatics.
Hussain, S. M., & Begh, G. R. (2022). Hybrid heuristic algorithm for cost-efficient QoS aware task scheduling in fog–cloud environment. Journal of Computational Science, 64, 101828.
Attiya, I., Abualigah, L., Elsadek, D., Chelloug, S. A., & Abd Elaziz, M. (2022). An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing. Mathematics, 10(7), 1100.
Yin, Z., Xu, F., Li, Y., Fan, C., Zhang, F., Han, G., & Bi, Y. (2022). A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing. Sensors, 22(4), 1555.
Jing, W., Zhao, C., Miao, Q., Song, H., & Chen, G. (2021). QoS-DPSO: QoS-aware task scheduling for the cloud computing system. Journal of Network and Systems Management, 29(1), 1-29.
Cheng, F., Huang, Y., Tanpure, B., Sawalani, P., Cheng, L., & Liu, C. (2022). Cost-aware job scheduling for cloud instances using deep reinforcement learning. Cluster Computing, 25(1), 619-631.
Abd Elaziz, M., Abualigah, L., Ibrahim, R. A., & Attiya, I. (2021). IoT workflow scheduling using intelligent arithmetic optimization algorithm in fog computing. Computational intelligence and neuroscience, 2021.
Hussain, M., Wei, L. F., Rehman, A., Abbas, F., Hussain, A., & Ali, M. (2022). Deadline-constrained energy-aware workflow scheduling in geographically distributed cloud data centers. Future Generation Computer Systems, 132, 211-222.
Medara, R., & Singh, R. S. (2021). Energy-aware workflow task scheduling in clouds with virtual machine consolidation using discrete water wave optimization. Simulation Modelling Practice and Theory, 110, 102323.
Mohammadzadeh, A., Masdari, M., & Gharehchopogh, F. S. (2021). Energy and cost-aware workflow scheduling in cloud computing data centers using a multi-objective optimization algorithm. Journal of Network and Systems Management, 29(3), 1-34.
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Copyright (c) 2023 M. Santhosh Kumar, Ganesh Reddy Kumar
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