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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Cheng-Jen Tang Miau-Ru Dai |
| Copyright Year | 2011 |
| Description | Author affiliation: Institute of Communication Engineering at Tatung University, 104 Taipei, Taiwan (Miau-Ru Dai) || Department of Electrical Engineering, Tatung University, 104 Taipei, Taiwan (Cheng-Jen Tang) |
| Abstract | Cloud computing clusters distributed computers to provide applications as services and on-demand resources over Internet. From the perspective of average and total energy consumption, such consolidated resource enhances the energy efficiency on both clients and servers. However, cloud computing has a different power consumption pattern from the traditional storage oriented Internet services. The computation oriented implementation of cloud service broadens the gap between the peak power demand and base power demand of a data center. A higher peak demand implies the need of feeder capacity expansion, which requires a considerable investment. This study proposes a computation related approach to lessen the increasing power demand of cloud service data centers. Through appropriated designs, some frequently used computing algorithms can be performed by either clients or servers. As a model presented in this paper, such client-server balanced computation resource integration suggests an energy-efficient and cost-effective cloud service data center. |
| Starting Page | 1159 |
| Ending Page | 1164 |
| File Size | 1052942 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781457715235 |
| e-ISBN | 9781457715242 |
| DOI | 10.1109/SII.2011.6147613 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-12-20 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computers Cloud computing Power demand Computational modeling Central Processing Unit Servers Sorting |
| Content Type | Text |
| Resource Type | Article |
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