Abstract
The success of Hadoop, an open-source framework for massively parallel and distributed computing, is expected to drive energy consumption of cloud data centers to new highs as service providers continue to add new infrastructure, services and capabilities to meet the market demands. While current research on data center airflow management, HVAC (Heating, Ventilation and Air Conditioning) system design, workload distribution and optimization, and energy efficient computing hardware and software are all contributing to improved energy efficiency, energy forecast in cloud computing remains a challenge. This paper reports an evolutionary computation based modeling and forecasting approach to this problem. In particular, an evolutionary neural network is developed and structurally optimized to forecast the energy load of a cloud data center. The results, both in terms of forecasting speed and accuracy, suggest that the evolutionary neural network approach to energy consumption forecasting for cloud computing is highly promising.
| Language | English |
|---|---|
| Title of host publication | Proceedings - 2015 International Conference on Cloud Computing Research and Innovation, ICCCRI 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 53-64 |
| Number of pages | 12 |
| ISBN (Electronic) | 9781509001446 |
| DOIs | |
| Publication status | Published - 26 Feb 2016 |
| Event | 3rd International Conference on Cloud Computing Research and Innovation, ICCCRI 2015 - Singapore, Singapore Duration: 26 Oct 2015 → 27 Oct 2015 |
Conference
| Conference | 3rd International Conference on Cloud Computing Research and Innovation, ICCCRI 2015 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 26/10/15 → 27/10/15 |
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Keywords
- cloud computing
- energy efficiency
- evolutionary computing
- genetic algorithm
- hadoop
- neural networks
Cite this
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Evolutionary neural network based energy consumption forecast for cloud computing. / Foo, Yong Wee; Goh, Cindy; Lim, Hong Chee; Zhan, Zhi Hui; Li, Yun.
Proceedings - 2015 International Conference on Cloud Computing Research and Innovation, ICCCRI 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 53-64 7421894.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
TY - GEN
T1 - Evolutionary neural network based energy consumption forecast for cloud computing
AU - Foo, Yong Wee
AU - Goh, Cindy
AU - Lim, Hong Chee
AU - Zhan, Zhi Hui
AU - Li, Yun
PY - 2016/2/26
Y1 - 2016/2/26
N2 - The success of Hadoop, an open-source framework for massively parallel and distributed computing, is expected to drive energy consumption of cloud data centers to new highs as service providers continue to add new infrastructure, services and capabilities to meet the market demands. While current research on data center airflow management, HVAC (Heating, Ventilation and Air Conditioning) system design, workload distribution and optimization, and energy efficient computing hardware and software are all contributing to improved energy efficiency, energy forecast in cloud computing remains a challenge. This paper reports an evolutionary computation based modeling and forecasting approach to this problem. In particular, an evolutionary neural network is developed and structurally optimized to forecast the energy load of a cloud data center. The results, both in terms of forecasting speed and accuracy, suggest that the evolutionary neural network approach to energy consumption forecasting for cloud computing is highly promising.
AB - The success of Hadoop, an open-source framework for massively parallel and distributed computing, is expected to drive energy consumption of cloud data centers to new highs as service providers continue to add new infrastructure, services and capabilities to meet the market demands. While current research on data center airflow management, HVAC (Heating, Ventilation and Air Conditioning) system design, workload distribution and optimization, and energy efficient computing hardware and software are all contributing to improved energy efficiency, energy forecast in cloud computing remains a challenge. This paper reports an evolutionary computation based modeling and forecasting approach to this problem. In particular, an evolutionary neural network is developed and structurally optimized to forecast the energy load of a cloud data center. The results, both in terms of forecasting speed and accuracy, suggest that the evolutionary neural network approach to energy consumption forecasting for cloud computing is highly promising.
KW - cloud computing
KW - energy efficiency
KW - evolutionary computing
KW - genetic algorithm
KW - hadoop
KW - neural networks
UR - http://www.scopus.com/inward/record.url?scp=84964864006&partnerID=8YFLogxK
U2 - 10.1109/ICCCRI.2015.17
DO - 10.1109/ICCCRI.2015.17
M3 - Conference contribution book
SP - 53
EP - 64
BT - Proceedings - 2015 International Conference on Cloud Computing Research and Innovation, ICCCRI 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -