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[ subject:"Computer engineering." ]
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Managing server energy and reducing ...
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Zheng, Xinying.
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Managing server energy and reducing operational cost for online service providers.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Managing server energy and reducing operational cost for online service providers./
作者:
Zheng, Xinying.
面頁冊數:
181 p.
附註:
Source: Dissertation Abstracts International, Volume: 74-03(E), Section: B.
Contained By:
Dissertation Abstracts International74-03B(E).
標題:
Computer engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3542555
ISBN:
9781267704399
Managing server energy and reducing operational cost for online service providers.
Zheng, Xinying.
Managing server energy and reducing operational cost for online service providers.
- 181 p.
Source: Dissertation Abstracts International, Volume: 74-03(E), Section: B.
Thesis (Ph.D.)--Michigan Technological University, 2012.
The past decade has seen the energy consumption in servers and Internet Data Centers (IDCs) skyrocket. A recent survey estimated that the worldwide spending on servers and cooling have risen to above $30 billion and is likely to exceed spending on the new server hardware. The rapid rise in energy consumption has posted a serious threat to both energy resources and the environment, which makes green computing not only worthwhile but also necessary. This dissertation intends to tackle the challenges of both reducing the energy consumption of server systems and by reducing the cost for Online Service Providers (OSPs).
ISBN: 9781267704399Subjects--Topical Terms:
621879
Computer engineering.
Managing server energy and reducing operational cost for online service providers.
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Source: Dissertation Abstracts International, Volume: 74-03(E), Section: B.
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Advisers: Yu Cai; Jindong Tan.
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Thesis (Ph.D.)--Michigan Technological University, 2012.
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The past decade has seen the energy consumption in servers and Internet Data Centers (IDCs) skyrocket. A recent survey estimated that the worldwide spending on servers and cooling have risen to above $30 billion and is likely to exceed spending on the new server hardware. The rapid rise in energy consumption has posted a serious threat to both energy resources and the environment, which makes green computing not only worthwhile but also necessary. This dissertation intends to tackle the challenges of both reducing the energy consumption of server systems and by reducing the cost for Online Service Providers (OSPs).
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Two distinct subsystems account for most of IDC's power: the server system, which accounts for 56% of the total power consumption of an IDC, and the cooling and humidification systems, which accounts for about 30% of the total power consumption. The server system dominates the energy consumption of an IDC, and its power draw can vary drastically with data center utilization. In this dissertation, we propose three models to achieve energy efficiency in web server clusters: an energy proportional model, an optimal server allocation and frequency adjustment strategy, and a constrained Markov model. The proposed models have combined Dynamic Voltage/Frequency Scaling (DV/FS) and Vary-On, Vary-off (VOVF) mechanisms that work together for more energy savings. Meanwhile, corresponding strategies are proposed to deal with the transition overheads. We further extend server energy management to the IDC's costs management, helping the OSPs to conserve, manage their own electricity cost, and lower the carbon emissions. We have developed an optimal energy-aware load dispatching strategy that periodically maps more requests to the locations with lower electricity prices. A carbon emission limit is placed, and the volatility of the carbon offset market is also considered. Two energy efficient strategies are applied to the server system and the cooling system respectively.
520
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With the rapid development of cloud services, we also carry out research to reduce the server energy in cloud computing environments. In this work, we propose a new live virtual machine (VM) placement scheme that can effectively map VMs to Physical Machines (PMs) with substantial energy savings in a heterogeneous server cluster. A VM/PM mapping probability matrix is constructed, in which each VM request is assigned with a probability running on PMs. The VM/PM mapping probability matrix takes into account resource limitations, VM operation overheads, server reliability as well as energy efficiency.
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The evolution of Internet Data Centers and the increasing demands of web services raise great challenges to improve the energy efficiency of IDCs. We also express several potential areas for future research in each chapter.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3542555
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