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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Gung-Yu Pan Lai, B.-C.C. Sheng-Yen Chen Jing-Yang Jou |
| Copyright Year | 2014 |
| Description | Author affiliation: Dept. of Electron. Eng. & Inst. of Electron., Nat. Chiao Tung Univ., Hsinchu, Taiwan (Gung-Yu Pan; Lai, B.-C.C.; Sheng-Yen Chen) || Dept. of Electr. Eng., Nat. Central Univ., Jhongli, Taiwan (Jing-Yang Jou) |
| Abstract | Energy consumption poses severe limitations for smart devices, urging the development of effective and efficient power management policies. State-of-the-art learning-based policies are autonomous and adaptive to the environment, but they are subject to costly computational overhead and lengthy convergence time. As smart devices are connected to Internet, this paper proposes the Learning-on-Cloud (LoC) policy to exploit cloud computing for power management. Sophisticated learning engines are offloaded from local devices to the cloud with minimal communication data, thus the runtime overhead is reduced. The learning data are shared between many devices with the same model, hence the convergence rate is raised. With one thousand devices connecting to the cloud, the LoC agent is able to converge within a few iterations; the energy saving is better than both of the greedy and the learning-based policies with less latency penalty. By implementing the LoC policy as an Android App, the measured overhead is only 0.01% of the system time. |
| Starting Page | 376 |
| Ending Page | 381 |
| File Size | 1207217 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479962785 |
| DOI | 10.1109/ICCAD.2014.7001379 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-11-02 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Context Runtime Convergence IEEE 802.11 Standards Performance evaluation Cloud computing Engines |
| Content Type | Text |
| Resource Type | Article |
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