| Publication Title: |
Managing the 802.11 Energy/Performance Tradeoff with Machine Learning |
| Publication Author: |
Monteleoni, Claire |
| Additional Authors: |
Hari Balakrishnan, Nick Feamster, Tommi Jaakkola |
| LCS Document Number: |
MIT-LCS-TR-971 |
| Publication Date: |
10-27-2004 |
| LCS Group: |
Networks and Mobile Systems |
| Additional URL: |
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| Abstract: |
| This paper addresses the problem of managing the tradeoff between
energy consumption and performance in wireless devices implementing
the IEEE 802.11 standard. 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, because network
activity is bursty and time-varying. We apply a new online machine
learning algorithm to this problem and show, using ns 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
simulations, LPSM saved 7%-20% more energy than 802.11 in power-saving
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. |
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