發(fā)布時間：2018-04-23 08:39

  本文選題：異構(gòu)云環(huán)境 + 能耗感知　； 參考：《哈爾濱工業(yè)大學》2017年碩士論文

【摘要】：云計算技術(shù)環(huán)境下的數(shù)據(jù)中心大多逐步采用虛擬化技術(shù)向云服務模式遷移,從而使得運行在異構(gòu)云環(huán)境上的虛擬機網(wǎng)絡日益復雜。為了滿足用戶不斷增長的云計算需求,作為重要載體的云數(shù)據(jù)中心數(shù)量和規(guī)模也日益的龐大,云數(shù)據(jù)中心運營商面臨著高能耗、高成本等嚴峻問題;而在異構(gòu)云環(huán)境下,虛擬機到物理服務器映射關(guān)系的差異會導致數(shù)據(jù)中心能源消耗的不同,使得能耗感知的虛擬機網(wǎng)絡優(yōu)化調(diào)度問題成為云數(shù)據(jù)中心資源管理的重要問題。云數(shù)據(jù)中心管理者面對著如何度量好整個數(shù)據(jù)中心的能源消耗,感知云數(shù)據(jù)中心的能耗情況,從而對異構(gòu)云數(shù)據(jù)中心的能耗進行高效的管理;如何實現(xiàn)用最少的能源消耗來滿足數(shù)據(jù)中心虛擬機網(wǎng)絡的放置與運行;如何根據(jù)虛擬機在運行時的動態(tài)變化及時調(diào)整整個數(shù)據(jù)中心的運行情況,以降低數(shù)據(jù)中心的能耗,從而減少服務成本等相關(guān)問題。為此,本文對異構(gòu)云環(huán)境下能耗感知的虛擬機網(wǎng)絡優(yōu)化調(diào)度進行了研究。首先,針對異構(gòu)云數(shù)據(jù)中心的能耗感知問題,本文將采用適用于異構(gòu)云環(huán)境下能源消耗的度量方法;建立異構(gòu)云數(shù)據(jù)中心能源消耗模型,其中包括虛擬機能耗模型,物理機能耗模型,網(wǎng)絡設備能耗模型等;測量與預測異構(gòu)云環(huán)境下的基礎設施能耗,為研究能耗感知的虛擬機網(wǎng)絡優(yōu)化放置和調(diào)度提供支持。其次,針對能耗感知的虛擬機網(wǎng)絡優(yōu)化放置問題,本文將對虛擬機網(wǎng)絡優(yōu)化放置問題建立問題模型;并分別采用免疫遺傳算法和基于最小割與最佳適應的改進算法對虛擬機網(wǎng)絡放置問題進行求解;通過與現(xiàn)有數(shù)據(jù)中心中常見的放置算法以及一些啟發(fā)式算法進行對比,分析免疫遺傳算法和基于最小割與最佳適應的改進算法對虛擬機網(wǎng)絡放置問題的求解質(zhì)量。再次,針對能耗感知的虛擬機網(wǎng)絡動態(tài)遷移問題,本文將對通過統(tǒng)計用戶的歷史訪問習慣,分析用戶行為特征;進一步給出用戶行為特征模型并通過虛擬機負載預測建立特征模型;給出基于用戶行為分析的虛擬機網(wǎng)絡動態(tài)遷移算法的問題描述、問題模型和算法步驟,并通過對比實驗進行分析。最后,在虛擬機網(wǎng)絡優(yōu)化放置和動態(tài)遷移的理論研究基礎上,對能耗感知的云數(shù)據(jù)中心資源管理平臺進行設計和開發(fā)。設計能耗感知的云數(shù)據(jù)中心資源管理平臺的架構(gòu);對各功能模塊、數(shù)據(jù)庫、關(guān)鍵算法部分進行設計;完成平臺兩大中心模塊的實現(xiàn)。
[Abstract]:Most data centers in cloud computing environment gradually adopt virtualization technology to migrate to cloud service mode, which makes virtual machine network running in heterogeneous cloud environment more and more complex. In order to meet the increasing demand of cloud computing, the number and scale of cloud data centers, which are important carriers, are increasingly huge. Cloud data center operators are faced with severe problems such as high energy consumption and high cost. The difference of mapping relationship between virtual machine and physical server will lead to different energy consumption of data center, which makes the optimal scheduling of virtual machine network aware of energy consumption become an important problem in resource management of cloud data center. Cloud data center managers are faced with how to measure the energy consumption of the whole data center and perceive the energy consumption of the cloud data center so as to efficiently manage the energy consumption of heterogeneous cloud data center. How to achieve the minimum energy consumption to meet the data center virtual machine network placement and operation, according to the dynamic changes of the virtual machine in the run time adjust the entire data center operation, in order to reduce the energy consumption of the data center, To reduce service costs and other related issues. Therefore, this paper studies the optimal scheduling of energy-aware virtual machine networks in heterogeneous cloud environments. First of all, aiming at the problem of energy consumption perception of heterogeneous cloud data center, this paper will adopt the energy consumption measurement method suitable for heterogeneous cloud environment, and establish the energy consumption model of heterogeneous cloud data center, including virtual machine energy consumption model. Physical computer energy consumption model, network equipment energy consumption model and so on; measure and predict the energy consumption of infrastructure in heterogeneous cloud environment, which provides support for the research of virtual machine network optimization placement and scheduling. Secondly, aiming at the problem of optimal placement of virtual machine network based on energy consumption awareness, this paper establishes a problem model for optimal placement of virtual machine network. Immune genetic algorithm (IGA) and improved algorithm based on minimum cut and optimal adaptation are used to solve the problem of virtual machine network placement, which is compared with common placement algorithms and some heuristic algorithms in existing data centers. The quality of solving virtual machine network placement problem based on immune genetic algorithm and improved algorithm based on minimum cut and optimal adaptation is analyzed. Thirdly, aiming at the problem of dynamic migration of virtual machine network with energy consumption awareness, this paper will analyze the characteristics of user behavior through statistics of users' historical access habits. Furthermore, the user behavior feature model is given and the feature model is established through the virtual machine load prediction, and the problem description, problem model and algorithm steps of the virtual machine network dynamic migration algorithm based on user behavior analysis are given. And through the contrast experiment carries on the analysis. Finally, based on the theoretical research of virtual machine network optimization and dynamic migration, the energy-aware cloud data center resource management platform is designed and developed. Design the structure of energy consumption aware cloud data center resource management platform; design each function module, database, key algorithm part; complete the implementation of two central modules of the platform.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP302;TP393.09

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