Characterization of a next generation step-and-scan system
Timothy J. Wiltshire, Joseph P. Kirk, et al.
SPIE Advanced Lithography 1998
Grid computing platforms dissipate massive amounts of energy. Energy efficiency, therefore, is an essential requirement that directly affects its sustainability. Resource management systems deploy rule-based approaches to mitigate this cost. However, these strategies do not consider the patterns of the workloads being executed. In this context, we demonstrate how a solution based on Deep Reinforcement Learning is used to formulate an adaptive power-efficient policy. Specifically, we implement an off-reservation approach to overcome the disadvantages of an aggressive shutdown policy and minimise the frequency of shutdown events. Through simulation, we train the algorithm and evaluate it against commonly used shutdown policies using real traces from GRID’5000. Based on the experiments, we observed a reduction of 46% on the averaged energy waste with an equivalent frequency of shutdown events compared to a soft shutdown policy.
Timothy J. Wiltshire, Joseph P. Kirk, et al.
SPIE Advanced Lithography 1998
Kafai Lai, Alan E. Rosenbluth, et al.
SPIE Advanced Lithography 2007
David Cash, Dennis Hofheinz, et al.
Journal of Cryptology
J.P. Locquet, J. Perret, et al.
SPIE Optical Science, Engineering, and Instrumentation 1998