【摘要】：隨著云計算越來越受到人們的歡迎,其規(guī)模和復(fù)雜度日益增大,也給管理帶來了新的挑戰(zhàn)。成本因素成為云計算管理需要考慮的主要問題。 云計算數(shù)據(jù)中心規(guī)模不斷擴大,能耗成為了數(shù)據(jù)中心的主要運營成本。為了滿足峰值負(fù)載的需求,云計算預(yù)備了大量計算資源。大部分的物理服務(wù)器在大部分時間并沒有被完全利用。雖然如此,這些服務(wù)器的運行依然會消耗大量的能耗。根據(jù)數(shù)據(jù)中心負(fù)載量的變化,通過動態(tài)資源分配的方式提高數(shù)據(jù)中心的資源利用率同時降低能耗對云計算有重要意義。 云計算依賴于底層計算機網(wǎng)絡(luò)的支持,而逐漸龐大和復(fù)雜的網(wǎng)絡(luò)讓網(wǎng)絡(luò)管理的成本越來越高。傳統(tǒng)的基于被動告警事件關(guān)聯(lián)方式的故障診斷在云計算網(wǎng)絡(luò)中并不適用。主動探測的方法由于其靈活性,可能成為云計算網(wǎng)絡(luò)故障診斷的解決方案。由于主動探測會對網(wǎng)絡(luò)性能造成影響,需要限制探測的成本。在成本約束下高效地挑選探測的探針是本文的另一個研究點。 本文的貢獻(xiàn)包括： (1)為了解決虛擬化云計算數(shù)據(jù)中心的能耗問題以及資源利用率低的問題,本文設(shè)計了一種基于能耗成本的動態(tài)資源分配方法DRAMDT�？紤]到一臺物理服務(wù)器上的虛擬機實例間運行截止時間差異較大可能造成服務(wù)器長期處于未完全利用狀態(tài),造成能耗的浪費,DRAMDT算法利用隨時間輪轉(zhuǎn)的虛擬機資源子池對虛擬機實例進行分組。分組內(nèi)的虛擬機實例才能被放置在相同的物理服務(wù)器上。仿真實驗驗證了DRAMDT算法的有效性和可行性。 (2)基于信息熵的性質(zhì),本文證明了一個關(guān)于探針信息熵增益的公式�；谶@個公式,本文提出了一種計算探針信息熵增益的近似計算方法。有了這種近似計算方法,本文進而提出了一種高效的故障探針選擇方法MEAP。MEAP算法的有效性和可行性在本文的仿真實驗中得到驗證。
[Abstract]:With the increasing popularity of cloud computing, the scale and complexity of cloud computing is increasing, which brings new challenges to management. Cost factor becomes the main problem that cloud computing management needs to consider. Cloud computing data center scale continues to expand, energy consumption has become the main operating cost of the data center. In order to meet the peak load requirements, cloud computing has prepared a large number of computing resources. Most physical servers are not fully utilized most of the time. However, these servers still consume a lot of energy. According to the change of data center load, it is important for cloud computing to improve the resource utilization of data center and reduce energy consumption by dynamic resource allocation. Cloud computing relies on the support of the underlying computer network, and the increasingly large and complex network makes the cost of network management higher and higher. Traditional fault diagnosis based on passive alarm event association is not applicable in cloud computing networks. Because of its flexibility, active detection may become a solution for cloud computing network fault diagnosis. Since active detection has an impact on network performance, it is necessary to limit the cost of detection. Another research point in this paper is to select probes efficiently under cost constraints. The contributions of this paper are as follows: (1) in order to solve the problem of energy consumption and low resource utilization in virtualized cloud computing data centers, a dynamic resource allocation method, DRAMDT. based on energy consumption cost, is designed in this paper. Considering that there is a large cut-off time difference between instances of virtual machines on a physical server, which may cause the server to remain in a state of incomplete utilization for a long time, resulting in a waste of energy consumption, In DRAMDT algorithm, virtual machine instances are grouped by the virtual machine resource subpool with time rotation. A virtual machine instance within a packet can be placed on the same physical server. Simulation results show that DRAMDT algorithm is effective and feasible. (2) based on the properties of information entropy, this paper proves a formula about the information entropy gain of probe. Based on this formula, an approximate method for calculating the information entropy gain of the probe is proposed. With this approximate calculation method, an efficient fault probe selection method, MEAP.MEAP algorithm, is proposed in this paper. The validity and feasibility of the algorithm are verified by the simulation experiments in this paper.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP3

【共引文獻(xiàn)】

相關(guān)期刊論文 前10條

1 趙海;劉怡文;艾均;王進法;;Internet動態(tài)節(jié)點特性的層級相關(guān)性研究[J];東北大學(xué)學(xué)報(自然科學(xué)版);2014年02期

2 薛健;李東;張宇;;IP級網(wǎng)絡(luò)拓?fù)錅y量技術(shù)的研究與實現(xiàn)[J];智能計算機與應(yīng)用;2014年01期

3 何靜;郭進利;徐雪娟;;微博關(guān)系網(wǎng)絡(luò)模型研究[J];計算機工程;2013年11期

4 顧亦然;戴曉罡;;基于虛擬力牽引的社團劃分算法[J];南京郵電大學(xué)學(xué)報(自然科學(xué)版);2013年06期

5 羅明偉;姚宏亮;李俊照;王浩;;一種基于節(jié)點相異度的社團層次劃分算法[J];計算機工程;2014年01期

6 范琪琳;尹浩;林闖;董加卿;宋偉;;互聯(lián)網(wǎng)自治域商業(yè)關(guān)系推測算法[J];計算機學(xué)報;2014年04期

7 焦璨;張楠楠;張敏強;馬紹奇;;基于社會網(wǎng)絡(luò)分析的心理學(xué)科研人員合作網(wǎng)絡(luò)研究[J];吉林大學(xué)社會科學(xué)學(xué)報;2014年04期

8 張寶軍;翁建廣;葉福軍;潘奕靜;;基于EMA的網(wǎng)絡(luò)拓?fù)渥詣踊J];計算機時代;2014年05期

9 張照文;范通讓;;帶有節(jié)點異質(zhì)性和免疫策略流行度的研究方法[J];河北省科學(xué)院學(xué)報;2014年02期

10 曾鳳琳;溫羅生;;二部無標(biāo)度網(wǎng)絡(luò)上病毒傳播模型和免疫策略研究[J];計算機工程;2014年08期

相關(guān)博士學(xué)位論文 前10條

1 楊雅君;動態(tài)圖數(shù)據(jù)挖掘與查詢算法的研究[D];哈爾濱工業(yè)大學(xué);2013年

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