Autonomic power and performance management for computing systems
Cluster Computing
Achieving autonomous power management using reinforcement learning
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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This paper presents a method to combine memory resizing and disk shutdown to achieve better energy savings than can be achieved individually. The method periodically adjusts the size of physical memory and the timeout value to shut down a hard disk to reduce the average energy consumption. Pareto distributions are used to model the disk idle time. The parameters of the distributions are estimated at runtime and used to calculate the appropriate timeout value. The memory size is changed based on the predicted number of disk accesses at different memory sizes. The method also considers the delay caused by power management and limits the performance degradation. The method is simulated and compared with other power management methods. Simulation results show that the method consistently achieves better energy savings and less performance degradation across different workloads