Name
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.
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Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICCAD.2014.7001379 | ICCAD |
Keywords | Field | DocType |
power aware computing,learning-on-cloud power management policy,android app,learning engines,learning (artificial intelligence),energy conservation,energy saving,loc agent,convergence rate,energy consumption,smart phones,cloud computing,mobile computing,smart devices,algorithms,placement,optimization,engines,convergence | Power management,Overhead (computing),Computer science,Exploit,Real-time computing,System time,Energy consumption,Timing closure,Distributed computing,Cloud computing,The Internet | Conference |
ISSN | ISBN | Citations |
1933-7760 | 978-1-4799-6277-8 | 2 |
PageRank | References | Authors |
0.45 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gung-Yu Pan | 1 | 10 | 2.31 |
| Bo-Cheng Charles Lai | 2 | 177 | 19.25 |
| Sheng-Yen Chen | 3 | 2 | 0.45 |
| Jing-Yang Jou | 4 | 681 | 88.55 |