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Energy Consumption Management using Fuzzy Logic in Distributed Systems Environment
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- 발행기관
- 보안공학연구지원센터(IJGDC) 바로가기
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- 간행물
- International Journal of Grid and Distributed Computing SCOPUS 바로가기
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- 통권
- Vol.9 No.8 (2016.08)바로가기
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- 페이지
- pp.319-334
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A284182
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- Cloud computing has based on on-demand access to potentially significant amounts of remote datacenter capabilities in distributed systems environment. By propagation data centers in clouds, computers should have a lower energy consumption. Hence, a good prediction about the amount of needed resources is very helpful for correct decision on energy consumption management. In this paper the Fuzzy Logic approach has been used for decision making about the performance of processors to obtain the estimated amounts of energy consumed by them and have been applied Auto Regressive and Neural Network approaches to predict the future workload. In prediction operation, three modes On, Off and Idle are considered for each server. At times of peak demand all servers must be turned on but when consumption is low, an estimated number of servers that must be turned on is obtained, 10% of remaining servers are idle and others are off. In this case a significant amount of energy is saved compared to the case that all servers are clear. The result show that Neural Network approach has more accurate prediction and better performance than Auto Regressive model.
목차
Abstract
1. Introduction
2. Related Works
3. Concepts
3.1. Fuzzy Logic
3.2. Auto Regressive Model
3.3. Neural Network
4. Problem Definition
4.1. First phase: Energy Estimation Algorithm using Fuzzy Logic technique (EEFL)
4.2. Second Phase: The Proposed Algorithm for Predicting Amount of Required Energy of Processors
5. Proposed Method
5.1. EEFL Configuration
5.2. EPAR Configuration
5.3. EPNN Configuration
6. Evaluation and Simulation
6.1. Grid5000 Platform
6.2. Estimation Phase
6.3. Prediction Phase
6.4. Prediction Results of both Auto Regressive and Neural Network Methods
6.5. Determine Server State using EPNN
7. Conclusion
References
키워드
cloud computing on-demand access energy consumption management Fuzzy Logic Auto Regressive Neural Network저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJGDC) [Science & Engineering Research Support Center, Republic of Korea(IJGDC)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Grid and Distributed Computing
- 간기
- 격월간
- pISSN
- 2005-4262
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Grid and Distributed Computing Vol.9 No.8
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