本文選題：云計算 + 節(jié)能��； 參考：《武漢理工大學》2013年博士論文

【摘要】：作為一種新興的信息處理模式,云計算(Cloud Computing)技術已經成為信息領域備受關注的研究熱點。云計算以虛擬化(Virtualization)作為支撐技術,以按需方式向Internet用戶提供動態(tài)可擴展的服務。然而,由于云計算環(huán)境規(guī)模大,資源管理與分配動態(tài)可伸縮的特點,導致云數據中心的能耗問題及其資源提供效率成為影響云計算性能的關鍵因素。本文以新的計算基礎設施——云計算技術為背景,研究如何優(yōu)化云計算數據中心的能耗及其資源的優(yōu)化配置問題。到目前為止,云計算的能耗問題及其資源提供依然存在很多亟待解決的問題。本文重點從節(jié)能機制、負載均衡和市場經濟模型等方面研究云計算環(huán)境中的高效資源提供優(yōu)化方法,主要的研究工作包括以下幾點： 1)系統(tǒng)研究了云計算環(huán)境中的節(jié)能機制及其資源提供優(yōu)化方法。 首先,從云計算的基本概念入手,介紹了云計算的特點、服務類型及層次；其次,重點研究了云計算中的節(jié)能優(yōu)化策略,分析比較了策略的應用環(huán)境及優(yōu)缺點；然后,進一步研究了云計算中的資源提供技術,并對該領域目前的優(yōu)化策略進行了分類比較；最后,對云實驗環(huán)境CloudSim進行介紹并對其資源提供機制進行實驗分析。 2)提出了基于能量與SLA均衡的虛擬機資源提供策略。 針對云計算環(huán)境中應用需求的動態(tài)變化特性,提出了基于強局部加權回歸的虛擬機自適應部署算法RLWR, RLWR可以根據應用負載所體現(xiàn)的資源占用歷史信息動態(tài)決策主機的超載時機。檢測出超載主機后,提出了遷移周期最優(yōu)的虛擬機遷移選擇算法MPM和遷移量最小算法MNM進行遷移虛擬機的選擇,然后提出以基于功耗的降序最佳適應啟發(fā)式算法PBFDH對遷移虛擬機進行再次優(yōu)化部署。該自適應部署策略比較靜態(tài)閾值算法STH、MPA和DVFS,不僅可以動態(tài)地將虛擬機部署到更少物理主機上,從而關閉閑置主機,提高了能效,而且通過主機資源的負載預測實現(xiàn)了高可靠的QoS服務交付,避免了用戶與資源提供者之間過多的SLA違例。實驗結果表明,策略在保證能效的同時,在減少SLA違例確保QoS方面也具有明顯的效果。 3)提出了基于多數據中心的綠色高能效資源提供策略。 數據中心的能效通常被多個動態(tài)因素影響,包括：能源成本、碳排放率、負載類型、CPU能效及冷卻系統(tǒng)等,該策略將同時考慮以上因素研究跨越多個地理位置環(huán)境中的多數據中心的全局能效問題。首先建立了多數據中心的資源提供模型,將能耗制約的收益問題和碳排放(Carbon Footprint)問題形式化為QoS約束的收益函數和代價函數的多目標最優(yōu)化模型,證明了該模型是NP-hard問題。針對該問題提出了綠色云優(yōu)先的CMM、MCMP算法和收益優(yōu)先的PMM、 MPMC算法,算法綜合考慮了碳排放、能耗、收益和應用的QoS需求,目標是降低碳排放,增加收益,同時滿足用戶應用的QoS需求。執(zhí)行應用階段,在數據中心中利用提出的NDVS方法進一步優(yōu)化能耗,求解了給定負載情況下單個數據中心功耗最小時CPU頻率滿足的條件,并求解了CPU的最優(yōu)頻率,證明了該頻率下能耗達到局部極小。實驗結果表明,策略不僅可以降低能耗成本,優(yōu)化任務調度,而且還可以權衡碳足跡。 4)提出了基于遺傳算法的虛擬機資源提供負載均衡策略。 應用需求的多樣性和節(jié)點資源的異構性不可避免地會導致資源提供過程中云計算節(jié)點的負載失衡問題,這極大地降低了云計算的整體資源提供效率。如何通過高效的負載均衡機制協(xié)調主機負載以提高資源利用率和系統(tǒng)性能是目前丞待解決的問題。針對這一問題,提出了基于負載均衡的虛擬機資源提供遺傳算法VMPGALB, VMPGALB舍棄了傳統(tǒng)二進制編碼方法,采用了更適宜體現(xiàn)虛擬機提供特點的樹型編碼方案。制定選擇策略時,采用基于適應度的比例選擇策略和最優(yōu)保存策略,該方法使得具有較小適應度的個體也有被選擇的機會并直接保留最優(yōu)個體至后代中。設計雜交算子時,通過對兩個父代個體的交叉操作,并利用生成樹方法,使VMPGALB具有更好的雜交性能。同時,為避免求解過程陷入局部最優(yōu),VMPGALB還按一定比例對產生的個體進行了變異操作。實驗結果表明,比較傳統(tǒng)遺傳算法BGA、MOGA、啟發(fā)式算法BFH和WLC,VMPGALB不僅遺傳性能更優(yōu),虛擬機遷移次數更少,而且能以較快的收斂速度求解虛擬機提供的負載均衡方案。 5)提出了基于市場經濟學模型的資源提供博弈策略。 市場經濟學模型可以通過均衡理論實現(xiàn)資源的優(yōu)化配置,研究了以市場經濟模型為基礎的云計算資源提供機制,結合博弈論在資源管理領域的優(yōu)勢,首先,建立了非合作競爭市場的資源提供模型,提出了非合作博弈資源提供算法RPANCG,該算法以非合作博弈進行建模,RPANCG的目標是尋找使得各個資源提供者效用達到最優(yōu)的Nash均衡解,證明了RPANCG算法可以產生唯一的Nash均衡。然后,在RPANCG算法滿足效用相互最優(yōu)的基礎上,為了進一步增加集體收益,并滿足效率與公平的約束,在非合作競爭市場的基礎上提出了議價市場中的資源提供算法RPABG,該算法以議價博弈進行建模,RPABG的目標則是尋找Nash議價解。實驗結果表明,RPANCG算法可以收斂到唯一的Nash均衡解,資源提供者的效用達到相互最優(yōu),整個資源提供趨于合理。而RPABG則在RPANCG算法的基礎上進一步兼顧了資源分配的效率和公平性,并且能夠提高資源提供者的整體效用,實現(xiàn)了Pareto改進,從而達到云資源的公平、合理和均衡的優(yōu)化分配。 本文的研究得到了國家自然科學基金項目(批準號：60970064,61272116),新世紀優(yōu)秀人才支持計劃項目(批準號：NCET-08-0806),教育部博士點基金項目(批準號：20120143110014)及湖北省高端人才引領培養(yǎng)計劃項目的資助。
[Abstract]:As a new information processing model, Cloud Computing (Cloud Computing) technology has become a hot research focus in the information field. Cloud computing uses Virtualization as support technology to provide dynamic and scalable services to Internet users in a on-demand way. However, because of the large scale of cloud computing environment, resource management and division. With dynamic and scalable features, the energy consumption and resource availability of the cloud data center are the key factors affecting the performance of the cloud computing. In this paper, a new computing infrastructure, cloud computing technology, is used to study how to optimize the energy consumption and the optimal allocation of resources in cloud computing data centers. The energy consumption problem and its resources still exist a lot of problems to be solved. This paper focuses on the optimization methods of efficient resource provision in the cloud computing environment, including energy saving mechanism, load balance and market economy model. The main research work includes the following points:
1) the energy saving mechanism and resource optimization methods in cloud computing environment are studied systematically.
First, from the basic concept of cloud computing, it introduces the characteristics of cloud computing, the type of service and the level of service. Secondly, it focuses on the energy saving optimization strategy in cloud computing, analyzes and compares the application environment and advantages and disadvantages of the strategy. Then, it further studies the source supply technology in the cloud computing, and advances the current optimization strategy in this field. The classification and comparison are carried out. Finally, the cloud experiment environment CloudSim is introduced, and its resource providing mechanism is experimentally analyzed.
2) a virtual machine resource providing strategy based on energy and SLA equilibrium is proposed.
In view of the dynamic change characteristics of application requirements in the cloud computing environment, a virtual machine adaptive deployment algorithm RLWR based on strong local weighted regression is proposed. RLWR can dynamically decide the overloading time of the host based on the resource occupying historical information embodied in the applied load. After the overloaded main machine is detected, the virtual machine migration with the best migration cycle is proposed. The migration selection algorithm MPM and the least migration algorithm MNM are used to select the migration virtual machine, and then the optimal deployment of the migrated virtual machine is redeployed with the descending optimal adaptive heuristic algorithm PBFDH based on the power consumption. The adaptive deployment strategy compares the static threshold algorithm STH, MPA and DVFS, which can not only dynamically deploy the virtual machine to less. On the physical host, it closes the idle host, improves the energy efficiency, and realizes high reliable QoS service delivery through the load prediction of the host resource, avoiding excessive SLA violation between the user and the resource provider. The experimental results show that the strategy also has obvious effect in reducing the SLA violation to ensure the QoS while guaranteeing the energy efficiency.
3) a green energy efficient resource delivery strategy based on multi data center is proposed.
The energy efficiency of the data center is usually influenced by several dynamic factors, including energy cost, carbon emission rate, load type, CPU energy efficiency and cooling system. The strategy will consider the above factors at the same time to study the global energy efficiency problems of multi data centers across multiple geographic environments. The problem of energy consumption and carbon emission (Carbon Footprint) is formalized into a multi-objective optimization model of revenue function and cost function of QoS constraints. It is proved that the model is a NP-hard problem. The green cloud priority CMM, MCMP algorithm and revenue priority PMM, MPMC algorithm are proposed for the problem, and the carbon scheduling algorithm is taken into consideration. The QoS demand for energy consumption, revenue and application is designed to reduce carbon emissions, increase revenue and meet the QoS requirements of user applications. In the application phase, the proposed NDVS method is used to further optimize energy consumption in the data center, and the conditions for the minimum power consumption of a single data center in the case of a given load are solved, and the C is solved, and the C is solved. The optimal frequency of PU shows that the energy consumption at this frequency reaches a local minimum. The experimental results show that the strategy not only reduces the cost of energy consumption, optimizes the task scheduling, but also can balance the carbon footprint.
4) proposes a load balancing strategy based on genetic algorithm for virtual machine resources.
The diversity of application requirements and the heterogeneity of node resources inevitably lead to the problem of load imbalance of cloud computing nodes in the process of resource provision, which greatly reduces the overall resource efficiency of cloud computing. How to coordinate host load through efficient load balancing mechanism to improve resource utilization and system performance is now the prime minister To solve this problem, a genetic algorithm VMPGALB is provided for virtual machine resources based on load balancing, and VMPGALB abandoning the traditional binary coding method and adopting a tree coding scheme which is more suitable to provide the features of virtual machines. In this method, the method makes the individuals with smaller fitness have the opportunity to be selected and retain the optimal individual directly to the offspring. When designing the crossover operator, the cross operation of two parent individuals and the spanning tree method make VMPGALB have better hybrid performance. At the same time, in order to avoid the solution process falling into local optimal, VMPGA LB also performs the mutation operation on a certain proportion. The experimental results show that compared with traditional genetic algorithms BGA, MOGA, heuristic algorithm BFH and WLC, VMPGALB not only has better genetic performance and less migration times, but also can solve the load balancing scheme provided by virtual machine at a faster rate of convergence.
5) put forward a game strategy of resource supply based on market economics model.
The market economy model can optimize the allocation of resources by equilibrium theory, study the mechanism of providing cloud computing resources based on the market economy model, combine the advantages of game theory in the field of resource management. First, it establishes a resource supply model for non cooperative competition market, and proposes an algorithm RPANCG for non cooperative game resource provision. The algorithm is modeled by non cooperative game. The goal of RPANCG is to find the Nash equilibrium solution that makes the utility of various resource providers reach the best. It is proved that the RPANCG algorithm can produce the unique Nash equilibrium. Then, on the basis of the RPANCG algorithm satisfying the mutual optimal utility, the algorithm can increase the collective income and meet the efficiency and fairness. Constraint, based on the non cooperative competitive market, the resource provision algorithm RPABG in the bargaining market is proposed. The algorithm is modeled by the bargaining game, and the goal of RPABG is to find the Nash bargaining solution. The experimental results show that the RPANCG algorithm converges to the only Nash equilibrium solution, the utility of the source provider reaches each other optimal, and the whole resource is proposed. The supply tends to be reasonable, and RPABG is based on the efficiency and fairness of resource allocation on the basis of RPANCG algorithm, and can improve the overall utility of resource providers and realize the improvement of Pareto, thus achieving fair, reasonable and balanced allocation of cloud resources.
The research obtained from the National Natural Science Foundation Project (approval number: 6097006461272116), the new century excellent talent support program (approval number: NCET-08-0806), the PhD fund project of the Ministry of Education (approval number: 20120143110014) and the financing plan project of the high-end talents in Hubei province.
【學位授予單位】：武漢理工大學
【學位級別】：博士
【學位授予年份】：2013
【分類號】：TP3;F205

【參考文獻】

相關期刊論文 前10條

1 劉漳輝;王曉莉;;云計算虛擬機群中帶遺傳算法的負載均衡算法[J];福州大學學報(自然科學版);2012年04期

2 吳松;王飛;石宣化;金海;王志武;;HPC Cloud環(huán)境中基于網絡Ⅰ/O負載的虛擬機放置算法[J];中國科學:信息科學;2012年03期

3 何大勇,查建中,姜義東;遺傳算法求解復雜集裝箱裝載問題方法研究[J];軟件學報;2001年09期

4 吳琦,熊光澤;非平穩(wěn)自相似業(yè)務下自適應動態(tài)功耗管理[J];軟件學報;2005年08期

5 李志潔;程春田;黃飛雪;李欣;;一種基于序貫博弈的網格資源分配策略[J];軟件學報;2006年11期

6 宋杰;李甜甜;閆振興;那俊;朱志良;;一種云計算環(huán)境下的能效模型和度量方法[J];軟件學報;2012年02期

7 譚一鳴;曾國蓀;王偉;;隨機任務在云計算平臺中能耗的優(yōu)化管理方法[J];軟件學報;2012年02期

8 袁文成;朱怡安;陸偉;;面向虛擬資源的云計算資源管理機制[J];西北工業(yè)大學學報;2010年05期

9 楊峰;田高成;;基于碳足跡的虛擬化實驗室虛擬機資源分析與優(yōu)化[J];系統(tǒng)工程理論與實踐;2011年S2期

10 曾智斌;許力;;云計算中高能效的虛擬資源分配策略[J];計算機系統(tǒng)應用;2011年12期

相關博士學位論文 前3條

1 劉航;WMSNs在云計算中心節(jié)能減排中的關鍵技術研究[D];大連理工大學;2011年

2 陳海波;云計算平臺可信性增強技術的研究[D];復旦大學;2008年

3 孫鑫;面向云環(huán)境數據中心的高效資源調度機制研究[D];北京郵電大學;2012年

相關碩士學位論文 前3條

1 施楊斌;云計算環(huán)境下一種基于虛擬機動態(tài)遷移的負載均衡算法[D];復旦大學;2011年

2 趙春燕;云環(huán)境下作業(yè)調度算法研究與實現(xiàn)[D];北京交通大學;2009年

3 劉鵬程;云計算中虛擬機動態(tài)遷移的研究[D];復旦大學;2009年

，

本文編號：2110264