本文選題：云計算　切入點：任務分類　出處：《遼寧大學》2012年碩士論文　論文類型：學位論文

【摘要】：提高資源利用率是云計算的一個重要研究方向。目前，主要是通過人工智能方法對任務的資源需求做出預測，并根據(jù)服務器資源的使用情況選擇最優(yōu)的任務分配方案，來提高資源的利用率。這些方法不僅和平臺密切相關(guān)，而且在任務數(shù)量巨大、以虛擬機為資源調(diào)度單位的云計算環(huán)境下，這些方法需要耗費大量的資源來采集、存儲、加工數(shù)據(jù)，整合和遷移虛擬機。 本文提出的云計算環(huán)境下面向任務分類的個性虛擬化策略，通過綜合分析云計算中的任務，并結(jié)合已有的通信協(xié)議、程序度量、資源預測等分析方法估測任務對處理器、網(wǎng)絡帶寬、磁盤等資源的需求特性，然后根據(jù)任務的資源需求特性將云計算中的任務分為計算型、通信型、磁盤型、計算通信型、計算磁盤型、通信磁盤型、強標準型、弱標準型等多種類型；對任務分類以后，再結(jié)合現(xiàn)有的虛擬化技術(shù)，按照特定的資源配置比例給虛擬機分配處理器、網(wǎng)絡帶寬、磁盤等資源，從而虛擬出和任務類型相對應的個性化虛擬機；然后，提出了針對任務分類和個性化虛擬機的個性虛擬化策略，該策略按照一定的約束條件，根據(jù)需要構(gòu)建新的虛擬機或者回收已有的虛擬機資源，并在需要啟動物理或者關(guān)閉物理機時啟動和關(guān)閉物理機。和傳統(tǒng)的基于資源預測的負載均衡方法相比，本文提出的策略可以避免一些資源不能滿足需求而其它資源卻處于空閑狀態(tài)的情況出現(xiàn)，并提高任務的執(zhí)行效率和資源的整體利用率。 最后，在不同的硬件配置環(huán)境下對任務分類系統(tǒng)的平臺無關(guān)性進行驗證，，并在模擬云計算的環(huán)境下，對比了在采用個性虛擬化策略和沒有采用該策略的條件下資源的使用情況。結(jié)果證明，本文提出的策略在云計算環(huán)境下，能夠有效性的提高資源利用率。
[Abstract]:Improving resource utilization is an important research direction of cloud computing. At present, it mainly uses artificial intelligence method to forecast the resource demand of the task, and selects the optimal task allocation scheme according to the use of server resources. These methods are not only closely related to the platform, but also need a large amount of resources to collect and store in the cloud computing environment where the virtual machine is the resource scheduling unit. Process data, integrate and migrate virtual machines. This paper proposes a personalized virtualization strategy for task classification in cloud computing environment. By synthetically analyzing the tasks in cloud computing, and combining the existing communication protocols, program metrics, resource prediction and other analysis methods to estimate the task to processor. Network bandwidth, disk and other resource requirements, and then according to the resource requirements of the task, the tasks in cloud computing are divided into computational type, communication type, disk type, computational communication type, computing disk type, communication disk type, strong standard type. After classifying tasks and combining existing virtualization technologies, the virtual machine is allocated resources such as processor, network bandwidth, disk and so on according to specific resource allocation ratio. Then, a personalized virtual machine corresponding to the task type is proposed. Then, a personalized virtualization strategy for task classification and personalized virtual machine is proposed, which is based on certain constraints. Build new virtual machines as needed or recycle existing virtual machine resources, and start and shut down physical machines when they need to be started or shut down. The strategy proposed in this paper can avoid the situation that some resources can not meet the requirements while others are idle and improve the efficiency of task execution and the overall utilization of resources. Finally, the platform independence of task classification system is verified in different hardware configuration environment, and in the environment of simulating cloud computing, The results show that the proposed strategy can effectively improve resource utilization in cloud computing environment.
【學位授予單位】：遼寧大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TP3

【引證文獻】

相關(guān)碩士學位論文 前1條

1 程萌;基于混合優(yōu)化算法的云計算資源分配研究[D];南京大學;2013年

本文編號：1581618