本文選題：云計(jì)算　切入點(diǎn)：任務(wù)分類(lèi)　出處：《遼寧大學(xué)》2012年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：提高資源利用率是云計(jì)算的一個(gè)重要研究方向。目前，主要是通過(guò)人工智能方法對(duì)任務(wù)的資源需求做出預(yù)測(cè)，并根據(jù)服務(wù)器資源的使用情況選擇最優(yōu)的任務(wù)分配方案，來(lái)提高資源的利用率。這些方法不僅和平臺(tái)密切相關(guān)，而且在任務(wù)數(shù)量巨大、以虛擬機(jī)為資源調(diào)度單位的云計(jì)算環(huán)境下，這些方法需要耗費(fèi)大量的資源來(lái)采集、存儲(chǔ)、加工數(shù)據(jù)，整合和遷移虛擬機(jī)。 本文提出的云計(jì)算環(huán)境下面向任務(wù)分類(lèi)的個(gè)性虛擬化策略，通過(guò)綜合分析云計(jì)算中的任務(wù)，并結(jié)合已有的通信協(xié)議、程序度量、資源預(yù)測(cè)等分析方法估測(cè)任務(wù)對(duì)處理器、網(wǎng)絡(luò)帶寬、磁盤(pán)等資源的需求特性，然后根據(jù)任務(wù)的資源需求特性將云計(jì)算中的任務(wù)分為計(jì)算型、通信型、磁盤(pán)型、計(jì)算通信型、計(jì)算磁盤(pán)型、通信磁盤(pán)型、強(qiáng)標(biāo)準(zhǔn)型、弱標(biāo)準(zhǔn)型等多種類(lèi)型；對(duì)任務(wù)分類(lèi)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(jī)分配處理器、網(wǎng)絡(luò)帶寬、磁盤(pán)等資源，從而虛擬出和任務(wù)類(lèi)型相對(duì)應(yīng)的個(gè)性化虛擬機(jī)；然后，提出了針對(duì)任務(wù)分類(lèi)和個(gè)性化虛擬機(jī)的個(gè)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(jī)或者回收已有的虛擬機(jī)資源，并在需要啟動(dòng)物理或者關(guān)閉物理機(jī)時(shí)啟動(dòng)和關(guān)閉物理機(jī)。和傳統(tǒng)的基于資源預(yù)測(cè)的負(fù)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(mǎn)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(wù)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(duì)任務(wù)分類(lèi)系統(tǒng)的平臺(tái)無(wú)關(guān)性進(jìn)行驗(yàn)證，，并在模擬云計(jì)算的環(huán)境下，對(duì)比了在采用個(gè)性虛擬化策略和沒(méi)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(jì)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(xué)位授予單位】：遼寧大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TP3

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

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

本文編號(hào)：1581618