Performance evaluation of a Green Scheduling Algorithm for energy savings in Cloud computing

With energy shortages and global climate change leading our concerns these days, the power consumption of datacenters has become a key issue. Obviously, a substantial reduction in energy consumption can be made by powering down servers when they are not in use. This paper aims at designing, implemen...

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Bibliographic Details
Main Authors: Truong Vinh Truong Duy, Sato, Yukinori, Inoguchi, Yasushi
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Cloud computing
Costs
datacenters
Design optimization
Energy consumption
energy savings
green scheduling
Information science
Network servers
Neural networks
neural predictor
Prediction algorithms
Scheduling algorithm
Online Access:Request full text
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Summary:With energy shortages and global climate change leading our concerns these days, the power consumption of datacenters has become a key issue. Obviously, a substantial reduction in energy consumption can be made by powering down servers when they are not in use. This paper aims at designing, implementing and evaluating a Green Scheduling Algorithm integrating a neural network predictor for optimizing server power consumption in Cloud computing. We employ the predictor to predict future load demand based on historical demand. According to the prediction, the algorithm turns off unused servers and restarts them to minimize the number of running servers, thus minimizing the energy use at the points of consumption to benefit all other levels. For evaluation, we perform simulations with two load traces. The results show that the PP20 mode can save up to 46.3% of power consumption with a drop rate of 0.03% on one load trace, and a drop rate of 0.12% with a power reduction rate of 46.7% on the other.
DOI:10.1109/IPDPSW.2010.5470908