云計(jì)算數(shù)據(jù)中心服務(wù)器數(shù)量動(dòng)態(tài)配置策略
doi: 10.11999/JEIT141286 cstr: 32379.14.JEIT141286
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2.
(合肥工業(yè)大學(xué)計(jì)算機(jī)與信息學(xué)院 合肥 230009) ②(安全關(guān)鍵工業(yè)測(cè)控技術(shù)教育部工程研究中心 合肥 230009)
基金項(xiàng)目:
國(guó)家自然科學(xué)基金(61370088)，國(guó)家國(guó)際科技合作專(zhuān)項(xiàng)項(xiàng)目(2014DFB10060)和中央高?；究蒲袠I(yè)務(wù)費(fèi)專(zhuān)項(xiàng)資金(2011HGBZ1321, 2012HGQC0012)
Dynamic Active Servers Allocating Policy for Cloud Computing Data Centers
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2.
(School of Computer and Information, Hefei University of Technology, Hefei 230009, China)
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摘要: 云計(jì)算數(shù)據(jù)中心由通過(guò)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(fù)載成比例的活躍服務(wù)器數(shù)量同時(shí)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(dòng)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(wèn)題。首先，建立了問(wèn)題的NP數(shù)學(xué)模型，并分析了無(wú)切換能耗情況下最優(yōu)解的特性；其次，通過(guò)消除整數(shù)動(dòng)態(tài)規(guī)劃的遞推過(guò)程，推導(dǎo)具有多項(xiàng)式復(fù)雜度的最優(yōu)靜態(tài)算法；最后，采用對(duì)未來(lái)負(fù)載的最壞預(yù)測(cè)結(jié)果作為約束制定了優(yōu)化在線(xiàn)策略。仿真結(jié)果表明，所提出的靜態(tài)最優(yōu)和動(dòng)態(tài)優(yōu)化策略能夠適應(yīng)外界負(fù)載的劇烈變化趨勢(shì)始終謹(jǐn)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(jià)維持?jǐn)?shù)據(jù)中心的平穩(wěn)運(yùn)行。
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關(guān)鍵詞:
- 云計(jì)算 /
- 數(shù)據(jù)中心 /
- 活躍服務(wù)器 /
- 離線(xiàn)最優(yōu)算法 /
- 動(dòng)態(tài)規(guī)劃 /
- 在線(xiàn)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.-
Key words:
- Cloud computing /
- Data center /
- Active servers /
- Offline optimal algorithm /
- Dynamic programming /
- Online algorithm
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Chong F T, Heck M J R, Ranganathan P, et al.. Data center energy efficiency: improving energy efficiency in data centers beyond technology scaling[J]. IEEE Design Test, 2014, 31(1): 93-104. Li Jian, Shuang Kai, Su Sen, et al.. Reducing operational costs through consolidation with resource prediction in the cloud[C]. 12th IEEE/ACM International Symposium on Cloud and Grid Computing (CCGrid), Ottawa, Canada, 2012: 793-798. Wang Lin, Zhang Fa, Arjona Aroca J, et al.. GreenDCN: a general framework for achieving energy efficiency in data center networks[J]. IEEE Journal on Selected Areas in Communications, 2014, 32(1): 4-15. Urgaonkar R, Kozat U C, Igarashi K, et al.. Dynamic resource allocation and power management in virtualized data centers[C]. IEEE/IFIP Network Operations and Management Symposium (NOMS), Osaka, Japan, 2010: 479-486. Guenter B, Jain N, and Williams C. Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning[C]. 2011 Proceedings of IEEE International Conference on Computer Communications (INFOCOM), Shanghai, China, 2011: 1332-1340. Qureshi A, Weber R, Balakrishnan H, et al.. Cutting the electric bill for internet-scale systems[J]. ACM SIGCOMM Computer Communication Review, 2009, 39(4): 123-134. Guo Yuan-xiong and Fang Yu-guang. Electricity cost saving strategy in data centers by using energy storage[J]. IEEE Transactions on Parallel and Distributed Systems, 2013, 24(6): 1149-1160. Rao Lei, Liu Xue, Xie Le, et al.. Minimizing electricity cost: Optimization of distributed internet data centers in a multi-electricity market environment[C]. 2010 Proceedings of IEEE International Conference on Computer Communications (INFOCOM), San Diego, CA, USA, 2010: 1-9. Cao Jun-wei, Li Ke-qin and Stojmenovic I. Optimal power allocation and load distribution for multiple heterogeneous multi-core server processors across clouds and data centers[J]. IEEE Transactions on Computers, 2014, 63(1): 45-58. Beloglazov A, Buyya R, Lee Y C, et al.. A taxonomy and survey of energy-efficient data centers and cloud computing systems[J]. Advances in Computers, 2011, 82(2): 47-111. Wang Kai, Lin Ming-hong, Ciucu F, et al.. Characterizing the impact of the workload on the value of dynamic resizing in data centers[C]. ACM SIGMETRICS/Performance, London, United Kingdom, 2012: 405-406. Rabbani M G, Zhani M F, and Boutaba R. On achieving high survivability in virtualized data centers[J]. IEICE Transactions on Communications, 2014, E97B(1): 10-18. Liu Zhen-hua, Lin Ming-hong, Adam W, et al.. Greening geographical load balancing[C]. Proceedings ACM SIGMETRICS, San Jose, CA, USA, 2011: 233-244. Mathew V, Sitaraman R K, and Shenoy P. Energy-aware load balancing in content delivery networks[C]. Proceedings of the ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, Orlando, FL, USA, 2012: 954-962. Gandhi A, Gupta V, Harchol Balter M, et al.. Optimality analysis of energy-performance trade-off for server farm management[J]. Performance Evaluation, 2010, 67(11): 1155-1171. Lin Ming-hong, Wierman A, Andrew L L H, et al.. Dynamic right-sizing for power-proportional data centers[J]. IEEE/ACM Transactions on Networking, 2013, 21(5): 1378-1391. Michael R G and Johnson D S. Computers and Intractability: A Guide to the Theory of NP-completeness[M]. San Francisco: WH Freeman Co., 1979: 206-218. -
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