An Improved Dingo Optimization for Resource Aware Scheduling in Cloud Fog Computing Environment

Majlesi Journal of Electrical Engineering

مقاله 5، دوره 17، شماره 3، آذر 2023، صفحه 31-41   

نوع مقاله: Reseach Article

شناسه دیجیتال (DOI): 10.30486/mjee.2023.1989335.1165

نویسندگان

Santhosh Kumar Medishetti^{* 1}؛ Ganesh Reddy Karri²

¹VIT-AP University, Amaravathi, A.P., India, 522 237.

²VIT-AP University, Inavolu, Beside AP Secretariat, Amaravati AP, India

چکیده

Task scheduling in Cloud-Fog computing environments is a critical aspect of optimizing resource allocation and enhancing performance. This study presents an improved version of the Dingo Optimization Algorithm (IDOA) specifically designed for task scheduling in Cloud-Fog computing. The enhanced IDOA incorporates novel modifications to address the limitations of the original algorithm and improve the efficiency and effectiveness of task allocation. The algorithm incorporates modifications to the fitness evaluation function, a dynamic update mechanism, and a neighborhood search technique to enhance task allocation efficiency. Extensive simulations and comparisons with existing algorithms are conducted to evaluate the performance of the IDOA. The results demonstrate its superiority in terms of task makespan time, VM failure rate, and degree of imbalance. Overall, the improved Dingo Optimization Algorithm offers a promising solution for efficient task scheduling in Cloud-Fog computing environments. The algorithm effectively balances exploration and exploitation, facilitating efficient task scheduling in Cloud-Fog computing environments and optimizing cloud-based applications and services.

کلیدواژه‌ها

Task scheduling؛ Cloud-Fog Computing؛ Makespan؛ Degree of Imbalance؛ Dingo Optimization Algorithm

مراجع

• Arunarani, A. R., Dhanabalachandran Manjula, and Vijayan Sugumaran. "Task scheduling techniques in cloud computing: A literature survey." Future Generation Computer Systems 91 (2019): 407-415.
• Houssein, Essam H., et al. "Task scheduling in cloud computing based on meta-heuristics: review, taxonomy, open challenges, and future trends." Swarm and Evolutionary Computation 62 (2021): 100841.
• Soltani, Nasim, Behzad Soleimani, and Behrang Barekatain. "Heuristic algorithms for task scheduling in cloud computing: a survey." International Journal of Computer Network and Information Security8 (2017): 16.
• Singh, Sukhpal, and Inderveer Chana. "A survey on resource scheduling in cloud computing: Issues and challenges." Journal of grid computing 14 (2016): 217-264.
• Menaka, M., and KS Sendhil Kumar. "Workflow scheduling in cloud environment–Challenges, tools, limitations & methodologies: A review." Measurement: Sensors (2022): 100436.
• Jamil, Bushra, et al. "Resource allocation and task scheduling in fog computing and internet of everything environments: A taxonomy, review, and future directions." ACM Computing Surveys (CSUR)11s (2022): 1-38.
• Aravind, Kalavagunta, and Praveen Kumar Reddy Maddikunta. "Dingo Optimization Based Cluster Based Routing in Internet of Things." Sensors20 (2022): 8064.
• Pari, Deepanramkumar, and Jaisankar Natarajan. "Secure Spectrum Access, Routing, and Hybrid Beamforming in an Edge-Enabled mmWave Massive MIMO CRN-Based Internet of Connected Vehicle (IoCV) Environments." Sensors15 (2022): 5647.
• Yin, Zhenyu, et al. "A multi-objective task scheduling strategy for intelligent production line based on cloud-fog computing." Sensors4 (2022): 1555.
• Ahmed, Omed Hassan, et al. "Using differential evolution and Moth–Flame optimization for scientific workflow scheduling in fog computing." Applied Soft Computing 112 (2021): 107744.
• Badri, Sahar, et al. "An Efficient and Secure Model Using Adaptive Optimal Deep Learning for Task Scheduling in Cloud Computing." Electronics6 (2023): 1441.
• Harika, Sonti, and B. Chaitanya Krishna. "Multi-Objective Optimization-Oriented Resource Allocation in the Fog Environment: A New Hybrid Approach." International Journal of Information Technology and Web Engineering (IJITWE)1 (2022): 1-25.
• Kumar, M. Santhosh, and Ganesh Reddy Karri. "Eeoa: cost and energy efficient task scheduling in a cloud-fog framework." Sensors5 (2023): 2445.
• Gomathi, B., et al. "Monarch Butterfly Optimization for Reliable Scheduling in Cloud." Computers, Materials & Continua3 (2021).
• Najafizadeh, Abbas, et al. "Multi-objective Task Scheduling in cloud-fog computing using goal programming approach." Cluster Computing1 (2022): 141-165.
• Nguyen, Binh Minh, et al. "Evolutionary algorithms to optimize task scheduling problem for the IoT based bag-of-tasks application in cloud–fog computing environment." Applied Sciences9 (2019): 1730.
• Huang, Xingwang, et al. "Task scheduling in cloud computing using particle swarm optimization with time varying inertia weight strategies." Cluster Computing 23 (2020): 1137-1147.
• Movahedi, Zahra, and Bruno Defude. "An efficient population-based multi-objective task scheduling approach in fog computing systems." Journal of Cloud Computing1 (2021): 1-31.
• Singh, Gyan, and Amit K. Chaturvedi. "Hybrid modified particle swarm optimization with genetic algorithm (GA) based workflow scheduling in cloud-fog environment for multi-objective optimization." Cluster Computing (2023): 1-18.
• Saravanan, T., and S. Saravanakumar. "Enhancing investigations in data migration and security using sequence cover cat and cover particle swarm optimization in the fog paradigm." International Journal of Intelligent Networks 3 (2022): 204-212.
• Jakwa, Ali Garba, et al. "Performance Evaluation of Hybrid Meta-Heuristics-Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing." International Journal of Cloud Applications and Computing (IJCAC)1 (2023): 1-16.
• Liu, Weimin, et al. "Fog Computing Resource-Scheduling Strategy in IoT Based on Artificial Bee Colony Algorithm." Electronics7 (2023): 1511.
• Abd Elaziz, Mohamed, et al. "Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution." Knowledge-Based Systems 169 (2019): 39-52.
• Mangalampalli, Sudheer, Ganesh Reddy Karri, and Ahmed A. Elngar. "An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization." Sensors3 (2023): 1384.
• Awad, A. I., N. A. El-Hefnawy, and H. M. Abdel_kader. "Enhanced particle swarm optimization for task scheduling in cloud computing environments." Procedia Computer Science 65 (2015): 920-929.
• Moon, YoungJu, et al. "A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments." Human-centric Computing and Information Sciences1 (2017): 1-10.
• Almezeini, Nora, and Alaaeldin Hafez. "Task scheduling in cloud computing using lion optimization algorithm." International Journal of Advanced Computer Science and Applications11 (2017).
• Singhal, Shweta, et al. "Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization." Computers, Materials & Continua3 (2023).
• Supreeth, S., et al. "An efficient policy-based scheduling and allocation of virtual machines in cloud computing environment." Journal of Electrical and Computer Engineering 2022 (2022).
• Gollapalli, Mohammed, et al. "Modeling Algorithms for Task Scheduling in Cloud Computing Using CloudSim." Mathematical Modelling of Engineering Problems5 (2022).