电子与信息学报
電子與信息學報
전자여신식학보
JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY
2015年
8期
2007-2013
,共7页
卫星%张建军%石雷%翟琰
衛星%張建軍%石雷%翟琰
위성%장건군%석뢰%적염
云计算%数据中心%活跃服务器%离线最优算法%动态规划%在线算法
雲計算%數據中心%活躍服務器%離線最優算法%動態規劃%在線算法
운계산%수거중심%활약복무기%리선최우산법%동태규화%재선산법
Cloud computing%Data center%Active servers%Offline optimal algorithm%Dynamic programming%Online algorithm
云计算数据中心由通过高速网络连接的大量服务器构成,一种有效的节能措施是维持与系统负载成比例的活跃服务器数量同时切换剩余服务器到空闲模式,由此分别产生操作能耗和切换能耗。该文研究如何动态配置活跃服务器数量以最小化数据中心能耗(操作与切换能耗之和)的问题。首先,建立了问题的NP数学模型,并分析了无切换能耗情况下最优解的特性;其次,通过消除整数动态规划的递推过程,推导具有多项式复杂度的最优静态算法;最后,采用对未来负载的最坏预测结果作为约束制定了优化在线策略。仿真结果表明,所提出的静态最优和动态优化策略能够适应外界负载的剧烈变化趋势始终谨慎调整活跃服务器和休眠服务器的比例,以接近最优的能耗代价维持数据中心的平稳运行。
雲計算數據中心由通過高速網絡連接的大量服務器構成,一種有效的節能措施是維持與繫統負載成比例的活躍服務器數量同時切換剩餘服務器到空閒模式,由此分彆產生操作能耗和切換能耗。該文研究如何動態配置活躍服務器數量以最小化數據中心能耗(操作與切換能耗之和)的問題。首先,建立瞭問題的NP數學模型,併分析瞭無切換能耗情況下最優解的特性;其次,通過消除整數動態規劃的遞推過程,推導具有多項式複雜度的最優靜態算法;最後,採用對未來負載的最壞預測結果作為約束製定瞭優化在線策略。倣真結果錶明,所提齣的靜態最優和動態優化策略能夠適應外界負載的劇烈變化趨勢始終謹慎調整活躍服務器和休眠服務器的比例,以接近最優的能耗代價維持數據中心的平穩運行。
운계산수거중심유통과고속망락련접적대량복무기구성,일충유효적절능조시시유지여계통부재성비례적활약복무기수량동시절환잉여복무기도공한모식,유차분별산생조작능모화절환능모。해문연구여하동태배치활약복무기수량이최소화수거중심능모(조작여절환능모지화)적문제。수선,건립료문제적NP수학모형,병분석료무절환능모정황하최우해적특성;기차,통과소제정수동태규화적체추과정,추도구유다항식복잡도적최우정태산법;최후,채용대미래부재적최배예측결과작위약속제정료우화재선책략。방진결과표명,소제출적정태최우화동태우화책략능구괄응외계부재적극렬변화추세시종근신조정활약복무기화휴면복무기적비례,이접근최우적능모대개유지수거중심적평은운행。
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.