Server frequency control using markov decision processes

For a wide range of devices and servers, Dynamic Frequency Scaling (DFS) can reduce energy consumption to various degrees by appropriately trading-off system performance. Efficient DFS policies are able to adjust server frequencies by extrapolating the transition of the highly varying workload without incurring much of implementation overhead. This paper models DFS policies of a single server using Markov Decision Processes (MDP). To accommodate the highly varying nature of workload in the proposed MDP, we adopt fluid approximation based on continuous time Markov chain and discrete time Markov chain modeling for the fluid workload generator respectively. Accordingly, we design two frequency controllers (FC), namely C-FC and D-FC, corresponding to the continuous and discrete modeling of the workload generator. We evaluate the proposed policies on synthetic and web traces. The proposed C-FC and D-FC schemes ensure performance satisfaction with moderate energy saving as well as ease of implementation, in comparison with existing DFS policies.