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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.9 No.1
- 발행연도
- 2020.2
- 수록면
- 49 - 57 (9page)
- DOI
- 10.5573/IEIESPC.2020.9.1.049
이용수
초록· 키워드
오류제보하기
Over the last decade, cloud computing has exponentially transformed the ways of computing. In spite of its various advantages, cloud computing suffers from several challenges that affect performance. Two of the fundamental challenges are power consumption and dynamic resource scaling. An efficient resource allocation strategy could help cloud computing to improve overall performance and operational costs. In this paper, we design a novel approach to available time slot prediction in a data node, based on an artificial neural network (ANN), which predicts the time at which the required resources will be available. We conducted experiments on several nodes, obtaining up to 98%, and outperforming state-of-the-art available time slot prediction approaches. We claim that available time–slot prediction for cloud computing based on an ANN will lead to optimum resource allocation and to minimizing energy consumed while maintaining the essential performance level.
목차
Abstract
1. Introduction
2. Literature Review
3. Artificial Neural Network for Available Time Slot Prediction
4. Experiment and Results
5. Conclusion and Future Work
References
참고문헌 (25)
참고문헌 신청
R. Buyya / 2009 / Cloud computing and emerging it platforms : Vision, hype, and reality for delivering computing as the 5th utility / Future Generation computer systems 25(6):599~616
N. Roy / 2011 / Efficient autoscaling in the cloud using predictive models for workload forecasting / 2011 IEEE 4th International Conference on Cloud Computing :500~507
K. Cetinski / 2015 / Ame-wpc : Advanced model for efficient workload prediction in the cloud / Journal of Network and Computer Applications 55:191~201
Alanazi Rayan / 2018 / Resource Prediction for Big Data Processing in a Cloud Data Center: A Machine Learning Approach / IEIE Transactions on Smart Processing & Computing 7(6):478~488
Zhang, Yuanyuan / 2008 / Predict task running time in grid environments based on CPU load predictions / Future Generation Computer Systems 24(6):489~497
N. Roy / 2011 / Efficient autoscaling in the cloud using predictive models for workload forecasting / 2011 IEEE 4th International Conference on Cloud Computing :500~507
K. Cetinski / 2015 / Ame-wpc : Advanced model for efficient workload prediction in the cloud / Journal of Network and Computer Applications 55:191~201
Alanazi Rayan / 2018 / Resource Prediction for Big Data Processing in a Cloud Data Center: A Machine Learning Approach / IEIE Transactions on Smart Processing & Computing 7(6):478~488
Zhang, Yuanyuan / 2008 / Predict task running time in grid environments based on CPU load predictions / Future Generation Computer Systems 24(6):489~497
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