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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.13 No.5
- 발행연도
- 2024.10
- 수록면
- 435 - 442 (8page)
- DOI
- 10.5573/IEIESPC.2024.13.5.435
이용수
초록· 키워드
오류제보하기
A decentralized computing system distributed among data-generating hardware and the cloud is called fog computing (FC). The ability to place resources to improve performance is given to users by such a flexible structure. On the other hand, low-delay services and limited resources make it difficult to use new virtualization technologies for task scheduling with resource management in fog computing. Several studies have examined scheduling and load balancing (LB) in cloud computing (CC). Nevertheless, countless LB initiatives have been proposed in fog environments. Task scheduling using a Dual interactive Wasserstein generative adversarial network (DIWGAN) optimized with an Improved Dwarf Mongoose Optimization algorithm is proposed to classify the suitable and not suitable server for the process (ESA-WF-FE-DIWGAN). Initially, the "Cloud-Fog Computing Dataset is used. Afterward, the dataset is fed to the Fog Resource Monitor (FRM). Here, the statistical features like storage, computing, and RAM are extracted. Subsequently, the extracted features are given to DIWGAN to classify the suitable server and the unsuitable server for the process. The scheduling process was done using the Improved Dwarf Mongoose Optimization algorithm. The proposed approach achieves 3.101%, which was a 7.12% higher make span: 24.13% and 13.04% lower total cost; 2.292% and 5.365% higher ARU compared to the existing methods.
목차
Abstract
1. Introduction
2. Proposed Methodology
3. Result and Discussion
4. Conclusion
Reference
참고문헌 (13)
참고문헌 신청
S. Iftikhar, M.M.M. Ahmad, S. Tuli, D. Chowdhury, M. Xu, S.S. Gill, and S. Uhlig, “HunterPlus: AI based energy-efficient task scheduling for cloud–fog computing environments,” Internet of Things, vol. 21, p. 100667. Apr. 2023. Article (CrossRef Link)
G. Shruthi, M.R. Mundada,, B.J Sowmya, and S. Supreeth, “Mayfly tayloroptimisation-based scheduling algorithm with deep reinforcement learning for dynamic scheduling in fog-cloud computing,” Applied Computational Intelligence and Soft Computing, vol. 198, no. 1, pp. 117273, Aug. 2022. Article (CrossRef Link)
A. Pradhan, S.K. Bisoy, S Kautish, M.B. Jasser, and A.W. Mohamed, “Intelligent Decision-Making of Load Balancing Using Deep Reinforcement Learning and Parallel PSO in Cloud Environment,” IEEE Access, vol. 10, no. 1, pp. 76939-76952. Jul. 2022. Article (CrossRef Link).
Z. Movahedi, and B. Defude, “An efficient population-based multi-objective task scheduling approach in fog computing systems,” Journal of Cloud Computing, vol. 10, no. 1, pp. 1-31. Dec. 2021. Article (CrossRef Link)
M. Zahra, and D Bruno, A mohammad Hosseininia, “An efficient population-based multi-objective task scheduling approach in fog computing systems,” Journal of Cloud Computing, vol. 10, no. 1, pp. 1-1, Dec. 2021. Article (CrossRef Link)
G. Shruthi, M.R. Mundada,, B.J Sowmya, and S. Supreeth, “Mayfly tayloroptimisation-based scheduling algorithm with deep reinforcement learning for dynamic scheduling in fog-cloud computing,” Applied Computational Intelligence and Soft Computing, vol. 198, no. 1, pp. 117273, Aug. 2022. Article (CrossRef Link)
A. Pradhan, S.K. Bisoy, S Kautish, M.B. Jasser, and A.W. Mohamed, “Intelligent Decision-Making of Load Balancing Using Deep Reinforcement Learning and Parallel PSO in Cloud Environment,” IEEE Access, vol. 10, no. 1, pp. 76939-76952. Jul. 2022. Article (CrossRef Link).
Z. Movahedi, and B. Defude, “An efficient population-based multi-objective task scheduling approach in fog computing systems,” Journal of Cloud Computing, vol. 10, no. 1, pp. 1-31. Dec. 2021. Article (CrossRef Link)
M. Zahra, and D Bruno, A mohammad Hosseininia, “An efficient population-based multi-objective task scheduling approach in fog computing systems,” Journal of Cloud Computing, vol. 10, no. 1, pp. 1-1, Dec. 2021. Article (CrossRef Link)
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