發(fā)布時(shí)間：2018-11-02 09:11

【摘要】：隨著云計(jì)算越來(lái)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(lái)了新的挑戰(zhàn)。成本因素成為云計(jì)算管理需要考慮的主要問(wèn)題。 云計(jì)算數(shù)據(jù)中心規(guī)模不斷擴(kuò)大,能耗成為了數(shù)據(jù)中心的主要運(yùn)營(yíng)成本。為了滿足峰值負(fù)載的需求,云計(jì)算預(yù)備了大量計(jì)算資源。大部分的物理服務(wù)器在大部分時(shí)間并沒(méi)有被完全利用。雖然如此,這些服務(wù)器的運(yùn)行依然會(huì)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(guò)動(dòng)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(shí)降低能耗對(duì)云計(jì)算有重要意義。 云計(jì)算依賴(lài)于底層計(jì)算機(jī)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(lái)越高。傳統(tǒng)的基于被動(dòng)告警事件關(guān)聯(lián)方式的故障診斷在云計(jì)算網(wǎng)絡(luò)中并不適用。主動(dòng)探測(cè)的方法由于其靈活性,可能成為云計(jì)算網(wǎng)絡(luò)故障診斷的解決方案。由于主動(dòng)探測(cè)會(huì)對(duì)網(wǎng)絡(luò)性能造成影響,需要限制探測(cè)的成本。在成本約束下高效地挑選探測(cè)的探針是本文的另一個(gè)研究點(diǎn)。 本文的貢獻(xiàn)包括： (1)為了解決虛擬化云計(jì)算數(shù)據(jù)中心的能耗問(wèn)題以及資源利用率低的問(wèn)題,本文設(shè)計(jì)了一種基于能耗成本的動(dòng)態(tài)資源分配方法DRAMDT�？紤]到一臺(tái)物理服務(wù)器上的虛擬機(jī)實(shí)例間運(yùn)行截止時(shí)間差異較大可能造成服務(wù)器長(zhǎng)期處于未完全利用狀態(tài),造成能耗的浪費(fèi),DRAMDT算法利用隨時(shí)間輪轉(zhuǎn)的虛擬機(jī)資源子池對(duì)虛擬機(jī)實(shí)例進(jìn)行分組。分組內(nèi)的虛擬機(jī)實(shí)例才能被放置在相同的物理服務(wù)器上。仿真實(shí)驗(yàn)驗(yàn)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(gè)關(guān)于探針信息熵增益的公式�；谶@個(gè)公式,本文提出了一種計(jì)算探針信息熵增益的近似計(jì)算方法。有了這種近似計(jì)算方法,本文進(jìn)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(shí)驗(yàn)中得到驗(yàn)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(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP3

【共引文獻(xiàn)】

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