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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.3
- 발행연도
- 2024.6
- 수록면
- 243 - 253 (11page)
- DOI
- 10.5573/IEIESPC.2024.13.3.243
이용수
초록· 키워드
오류제보하기
With the continuous development of internet information technology, cloud-computing task-scheduling platform technology is gradually maturing. Cloud computing is profoundly changing every aspect of people’s lives and providing many conveniences. With the application of cloud computing in more fields, more extensive applications and efficient task scheduling algorithms have become increasingly important. This research focuses on the problem of taskscheduling methods for cloud computing platforms in customer-oriented online training systems. Based on the optimization of the ant colony algorithm, an ant colony optimization (ACO) cloudcomputing task-scheduling algorithm is proposed. The research results indicate that when the number of tasks is 300, the makespan value of the optimized ant colony cloud scheduling algorithm (OACC) is 340, that of the discrete firefly algorithm (DFA) is 350, that of multi-objective differential evolution (MODE) is 380, and that of improved group search optimization (IGSO) is 409. The overall performance of OACC was 20.3% higher than that of IGSO. OACC maintained a low and stable degree of imbalance (DI) in different task count tests. At a task volume of 300, the overall utility evaluation of the ACO cloud-computing task-scheduling algorithm was 146, which is 31.5% higher than ACO, 18.7% higher than TACO, and 8.1% higher than LB-AACO. The experimental results meet expectations and indicate that the OACC cloud-computing taskscheduling algorithm proposed in the study has high task-processing ability and efficiency and is capable of scheduling tasks on cloud computing platforms for customer-oriented online training systems.
목차
Abstract
1. Introduction
2. Related Work
3. Task Scheduling Method based on Ant Colony Algorithm
4. Experiment and Analysis
5. Conclusion
References
참고문헌 (19)
참고문헌 신청
G. Rjoub, J. Bentahar, O. A. Wahab, “Big Trust Scheduling: trust-aware big data task scheduling approach in cloud computing environments”. Future generation computer systems, vol. 110, pp. 1079-1097, 2019.
P. Pirozmand, A. A. R. Hosseinabadi, M. Farrokhzad, et al. “Multi - objective hybrid genetic algorithm for task scheduling problem in cloud computing”. Neural computing & applications, 2022, vol. 34, pp. 2497-2497, 2022.
K. Li, L. Jia, X. Shi, “A Study into Cloud Computing Task Scheduling Based on BIAS Algorithm.” Journal of Internet Technology, vol. pp. 1375-1383, 2021.
M. Zeedan, G. Attiya, N. El-Fishawy, “A Hybrid Approach for Task Scheduling Based Particle Swarm and Chaotic Strategies in Cloud Computing Environment”. Parallel Processing Letters, vol. 32, pp. 2250001-2250001, 2022.
S. A. Alsaidy, A. D. Abbood, M. A. Sahib, “Heuristic initialization of PSO task scheduling algorithm in cloud computing”. Journal of King Saud University - Computer and Information Sciences, vol. 34, pp. 2370-2382, 2020.
P. Pirozmand, A. A. R. Hosseinabadi, M. Farrokhzad, et al. “Multi - objective hybrid genetic algorithm for task scheduling problem in cloud computing”. Neural computing & applications, 2022, vol. 34, pp. 2497-2497, 2022.
K. Li, L. Jia, X. Shi, “A Study into Cloud Computing Task Scheduling Based on BIAS Algorithm.” Journal of Internet Technology, vol. pp. 1375-1383, 2021.
M. Zeedan, G. Attiya, N. El-Fishawy, “A Hybrid Approach for Task Scheduling Based Particle Swarm and Chaotic Strategies in Cloud Computing Environment”. Parallel Processing Letters, vol. 32, pp. 2250001-2250001, 2022.
S. A. Alsaidy, A. D. Abbood, M. A. Sahib, “Heuristic initialization of PSO task scheduling algorithm in cloud computing”. Journal of King Saud University - Computer and Information Sciences, vol. 34, pp. 2370-2382, 2020.
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