云計算數(shù)據(jù)中心服務(wù)器數(shù)量動態(tài)配置策略

衛(wèi)星; 張建軍; 石雷; 翟琰

doi:10.11999/JEIT141286

云計算數(shù)據(jù)中心服務(wù)器數(shù)量動態(tài)配置策略

doi: 10.11999/JEIT141286

2.
(合肥工業(yè)大學(xué)計算機與信息學(xué)院合肥 230009) ②(安全關(guān)鍵工業(yè)測控技術(shù)教育部工程研究中心合肥 230009)

基金項目:

國家自然科學(xué)基金(61370088)，國家國際科技合作專項項目(2014DFB10060)和中央高?；究蒲袠I(yè)務(wù)費專項資金(2011HGBZ1321, 2012HGQC0012)

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- 文章訪問數(shù): 1587
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2014-10-09
- 修回日期: 2015-04-16
- 刊出日期: 2015-08-19

Dynamic Active Servers Allocating Policy for Cloud Computing Data Centers

2.
(School of Computer and Information, Hefei University of Technology, Hefei 230009, China)

摘要

摘要: 云計算數(shù)據(jù)中心由通過高速網(wǎng)絡(luò)連接的大量服務(wù)器構(gòu)成，一種有效的節(jié)能措施是維持與系統(tǒng)負載成比例的活躍服務(wù)器數(shù)量同時切換剩余服務(wù)器到空閑模式，由此分別產(chǎn)生操作能耗和切換能耗。該文研究如何動態(tài)配置活躍服務(wù)器數(shù)量以最小化數(shù)據(jù)中心能耗(操作與切換能耗之和)的問題。首先，建立了問題的NP數(shù)學(xué)模型，并分析了無切換能耗情況下最優(yōu)解的特性；其次，通過消除整數(shù)動態(tài)規(guī)劃的遞推過程，推導(dǎo)具有多項式復(fù)雜度的最優(yōu)靜態(tài)算法；最后，采用對未來負載的最壞預(yù)測結(jié)果作為約束制定了優(yōu)化在線策略。仿真結(jié)果表明，所提出的靜態(tài)最優(yōu)和動態(tài)優(yōu)化策略能夠適應(yīng)外界負載的劇烈變化趨勢始終謹慎調(diào)整活躍服務(wù)器和休眠服務(wù)器的比例，以接近最優(yōu)的能耗代價維持數(shù)據(jù)中心的平穩(wěn)運行。
- 云計算 /
- 數(shù)據(jù)中心 /
- 活躍服務(wù)器 /
- 離線最優(yōu)算法 /
- 動態(tài)規(guī)劃 /
- 在線算法
Abstract: Cloud computing data centers generally consist of a large number of servers connected via high speed network. One promising approach to saving energy is to maintain enough active severs in proportion to system load, while switch left servers to idle mode whenever possible. Then operating cost and switching cost is brought about respectively. The problem of right-sizing active severs to minimize energy consumption (total cost of operating and switching) in data centers is discussed. Firstly, the NP-hard model is established, and the characteristics of the optimal solution when omitting the switching cost are analyzed. Then by revising the solution procedure carefully, the recursive procedure is successfully eliminated. The optimal static algorithm with polynomial complexity is achieved. Finally, the online strategy is developed using the worst predicting load as the constraints. Simulation results show that the proposed offline and online algorithm can adapt the dramatic trend of external load and always carefully adjust the proportion of active servers, to guarantee minimum power consumption with a smooth computing process.
- Cloud computing /
- Data center /
- Active servers /
- Offline optimal algorithm /
- Dynamic programming /
- Online algorithm

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參考文獻(17)

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