Managing the 802.11 Energy/Performance Tradeoff with Machine Learning
This paper addresses the problem of managing the tradeoff between energy consumption and performance in wireless devices implementing the IEEE 802.11 standard [1]. To save energy, the 802.11 specification proposes a power-saving mode (PSM), where a device can sleep to save energy, periodically waking up to receive packets from a neighbor (e.g., an access point) that may have buffered packets for the sleeping device. Previous work has shown that a fixed polling time for waking up degrades the performance of Web transfers [2], because network activity is bursty and time-varying. We apply a new online machine learning algorithm to this problem and show, using ns-2 simulation and trace analysis, that it is able to adapt well to network activity. The learning process makes no assumptions about the underlying network activity being stationary or even Markov. Our learning power-saving algorithm, LPSM, guides the learning using a “loss function” that combines the increased latency from potentially sleeping too long and the wasted use of energy in waking up too soon. In our ns-2 simulations, LPSM saved 7%-20% more energy than 802.11 in powersaving mode, with an associated increase in average latency by a factor of 1.02, and not more than 1.2. LPSM is straightforward to implement within the 802.11 PSM framework.