本文選題：數(shù)據(jù)中心 + 資源監(jiān)控　； 參考：《電子科技大學(xué)》2017年碩士論文

【摘要】：近年,云計算技術(shù)的發(fā)展和成熟,使得云計算已經(jīng)被廣泛的應(yīng)用在各行各業(yè)(如軍事、教育、金融、電子政務(wù)、電商等)中,大量的傳統(tǒng)IT系統(tǒng)紛紛遷移到云數(shù)據(jù)中心,此外,大數(shù)據(jù)、移動互聯(lián)網(wǎng)、物聯(lián)網(wǎng)等的飛速發(fā)展和應(yīng)用,進(jìn)一步加速了云計算的基礎(chǔ)設(shè)施也就是云數(shù)據(jù)中心數(shù)量和規(guī)模的增長,這也造成了現(xiàn)代云數(shù)據(jù)中心規(guī)模極其龐大、結(jié)構(gòu)異常復(fù)雜等特點(diǎn)。除此之外,現(xiàn)代多樣的應(yīng)用需求背景下,資源管理者除了需要關(guān)心用戶對可靠性、性能等服務(wù)質(zhì)量之外,還要考慮節(jié)能減排等問題來降低的開支,實(shí)現(xiàn)利潤的增大,這給云數(shù)據(jù)中心的管理者帶來了不小的挑戰(zhàn)。為了實(shí)現(xiàn)云數(shù)據(jù)中心高效、高可靠和低能耗的資源管理,本文分別從云數(shù)據(jù)中心資源監(jiān)控、關(guān)聯(lián)建模和資源調(diào)度三個方面進(jìn)行了研究,核心的貢獻(xiàn)點(diǎn)如下:1)研發(fā)了基于仿生自主神經(jīng)系統(tǒng)(Bionic Autonomic Nervous System,BANS)的云資源監(jiān)控系統(tǒng)。借鑒BANS的設(shè)計思想,設(shè)計了一種資源監(jiān)控系統(tǒng),它充分的考慮現(xiàn)代云數(shù)據(jù)中心的虛擬化和超大規(guī)模等特點(diǎn)。其通過分層式的設(shè)計,使得系統(tǒng)的擴(kuò)展性良好;除此之外,該監(jiān)控系統(tǒng)還具有BANS的自主性,能夠?qū)崿F(xiàn)某種程度上的自組織、自診斷、自修復(fù)和自優(yōu)化,進(jìn)而減輕監(jiān)控系統(tǒng)主節(jié)點(diǎn)的負(fù)載,使得監(jiān)控系統(tǒng)可以更好的適用于現(xiàn)代的大規(guī)模云數(shù)據(jù)中心。2)提出了一種可靠性、性能和能耗的多維關(guān)聯(lián)模型。其首先在考慮硬件隨機(jī)失效的前提下,分別建立了可靠性-性能和可靠性-能耗兩個關(guān)聯(lián)子模型,實(shí)現(xiàn)了更加準(zhǔn)確合理的云服務(wù)的性能和能耗分析;然后通過利潤模型——利用時間功效函數(shù)評估性能的收益,電能消耗來評估支出——實(shí)現(xiàn)了可靠性、性能和能耗三者的關(guān)聯(lián)分析,為現(xiàn)代云數(shù)據(jù)中心可靠性、性能和能耗評估和調(diào)度管理建立了模型基礎(chǔ)。3)提出了一種自優(yōu)化的資源管理框架。充分考慮了VM對資源需求是動態(tài)變化的這一前提,本文借鑒BANS的思想,提出了一種基于BANS的云資源管理系統(tǒng)用于云資源動態(tài)管理,其將監(jiān)控系統(tǒng)獲取的監(jiān)控信息作為基礎(chǔ),通過系統(tǒng)各個模塊的配合實(shí)現(xiàn)了一種自優(yōu)化的動態(tài)云資源管理框架。其通過虛擬機(jī)的遷移來滿足虛擬機(jī)對資源的動態(tài)需求:當(dāng)物理機(jī)出現(xiàn)過載時,可以選擇遷出虛擬機(jī),減少不必要服務(wù)性能退化;而當(dāng)物理機(jī)欠載時,還可以通過遷移整合虛擬機(jī)來減少開啟的物理機(jī)總數(shù),進(jìn)而提高物理機(jī)資源的利用率。4)提出了可靠性感知的多數(shù)據(jù)中心能耗成本建模方法。在考慮可靠性的基礎(chǔ)上,建立分布式多數(shù)據(jù)中心的系統(tǒng)、任務(wù)、調(diào)度、功耗和約束模型,將多數(shù)據(jù)中心管理問題公式化為了滿足用戶對性能、可靠性約束的前提下,最小化多數(shù)據(jù)中心能耗成本的問題,并提出可靠性感知的分布式數(shù)據(jù)中心能耗成本優(yōu)化的調(diào)度算法,其在考慮可靠性的基礎(chǔ)上,充分利用了不同地域數(shù)據(jù)中心電價的差異性,實(shí)現(xiàn)跨域的多數(shù)據(jù)中心能耗成本優(yōu)化。
[Abstract]:In recent years, with the development and maturity of cloud computing technology, cloud computing has been widely used in all walks of life (such as military, education, finance, e-government, e-commerce and so on). A large number of traditional IT systems have migrated to cloud data centers. With the rapid development and application of big data, mobile Internet, Internet of things and so on, cloud computing infrastructure, that is, the number and scale of cloud data centers, has been further accelerated, which has resulted in the extremely large scale of modern cloud data centers. The structure is extremely complex and so on. In addition, in the context of modern and diverse application requirements, resource managers need to care about the reliability, performance and other service quality of the user, but also consider energy saving and emission reduction to reduce the expenditure and realize the increase of profits. This poses a great challenge to the management of cloud data centers. In order to realize high efficiency, high reliability and low energy consumption resource management of cloud data center, this paper studies three aspects of cloud data center resource monitoring, association modeling and resource scheduling, respectively. The core contribution is as follows: 1) A cloud resource monitoring system based on Bionic Autonomic Nervous system is developed. Based on the design idea of Bans, a resource monitoring system is designed, which fully considers the virtualization and large scale of modern cloud data center. In addition, the monitoring system has the autonomy of Bans, which can realize some degree of self-organization, self-diagnosis, self-repair and self-optimization. Then the load of the master node of the monitoring system is reduced, which makes the monitoring system more suitable for the modern large-scale cloud data center. 2) A multi-dimensional correlation model of reliability, performance and energy consumption is proposed. Firstly, two correlation sub-models of reliability performance and reliability energy consumption are established on the premise of hardware random failure, which realizes more accurate and reasonable analysis of cloud service performance and energy consumption. Then through the profit model-using the time efficiency function to evaluate the performance income, the electric energy consumption to evaluate the expenditure-to realize the reliability, the performance and the energy consumption correlation analysis, for the modern cloud data center reliability, The performance and energy consumption evaluation and scheduling management are modeled on the basis of. 3) A self-optimizing resource management framework is proposed. Considering the premise that VM is a dynamic change of resource requirement, this paper proposes a cloud resource management system based on banks, which takes the monitoring information obtained by monitoring system as the basis, and proposes a new cloud resource management system based on Bans. A self-optimizing dynamic cloud resource management framework is implemented through the cooperation of various modules of the system. The migration of virtual machine can satisfy the dynamic resource demand of virtual machine: when the physical machine is overloaded, it can choose to move out of the virtual machine to reduce the unnecessary service performance degradation, and when the physical machine is under load, It is also possible to reduce the total number of open physical machines by migrating and integrating virtual machines, and then improve the utilization ratio of physical computers. 4) A reliability aware multi-data center energy cost modeling method is proposed. On the basis of considering reliability, the system, task, scheduling, power consumption and constraint model of distributed multi-data center are established, and the multi-data center management problem is formulated to satisfy the performance and reliability constraints of users. The problem of minimizing the cost of multi-data center energy consumption is minimized, and a distributed data center cost optimization scheduling algorithm based on reliability perception is proposed. On the basis of considering reliability, it makes full use of the difference of electricity price between different regional data centers. The energy cost optimization of multi-data center across domains is realized.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP308

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本文編號：2048055