本文選題：云計(jì)算 + 自適應(yīng)機(jī)制�。� 參考：《西北工業(yè)大學(xué)》2015年博士論文

【摘要】：云計(jì)算(Cloud Computing)能通過(guò)虛擬化的方式,將系統(tǒng)內(nèi)部多樣化的異構(gòu)資源封裝成統(tǒng)一的計(jì)算資源池,并按需為用戶(hù)提供所需的計(jì)算資源,是一種面向服務(wù)的計(jì)算模式。其中,基礎(chǔ)設(shè)施即服務(wù)(Infrastructure as a Service, IaaS)作為云計(jì)算服務(wù)中的基礎(chǔ)服務(wù),主要是為用戶(hù)提供按需的底層基礎(chǔ)設(shè)施資源(計(jì)算資源、存儲(chǔ)空間和網(wǎng)絡(luò)帶寬等)。而作為IaaS研究核心的資源管理和調(diào)度策略,對(duì)系統(tǒng)的穩(wěn)定性和服務(wù)的可靠性起著非常重要的作用。本文著重對(duì)IaaS中的虛擬資源管理機(jī)制,以及其在高性能應(yīng)用集群、Web服務(wù)集群中的自適應(yīng)資源分配及調(diào)度機(jī)制等方面進(jìn)行了深入研究。首先通過(guò)現(xiàn)有流行的集群、虛擬化技術(shù)、云計(jì)算平臺(tái)等相關(guān)研究,針對(duì)其資源的特點(diǎn)及現(xiàn)有資源管理策略中的不足,結(jié)合面向市場(chǎng)(Marketing-Oriented)的資源調(diào)度策略、資源協(xié)商(Negotiating)機(jī)制、資源預(yù)留(Advance Reservation)機(jī)制及自適應(yīng)機(jī)制,側(cè)重研究IaaS自適應(yīng)資源管理機(jī)制；其次,針對(duì)現(xiàn)有集群資源管理機(jī)制在網(wǎng)格應(yīng)用集群、高性能計(jì)算集群、電子商務(wù)應(yīng)用中大量使用的Web集群,對(duì)于自適應(yīng)資源協(xié)調(diào)機(jī)制所存在的不足,研究了基于云計(jì)算IaaS服務(wù)的資源分配及調(diào)度自適應(yīng)彈性管理機(jī)制。最后,由于系統(tǒng)識(shí)別部分是自適應(yīng)控制的關(guān)鍵,鑒于最小成分分析算法在自適應(yīng)系統(tǒng)識(shí)別領(lǐng)域有顯著的成效,因此本文結(jié)合目前最小成分分析算法在系統(tǒng)識(shí)別方面的特點(diǎn)及存在問(wèn)題,研究IaaS服務(wù)的資源自適應(yīng)識(shí)別算法。本文所論述的研究?jī)?nèi)容,都是本文作者在Platform公司參與網(wǎng)格和云計(jì)算方面的合作課題中所取得的研究成果,這些成果不僅納入市場(chǎng)上某些主流的商業(yè)項(xiàng)目,而且被成功、廣泛應(yīng)用到歐洲原子能(CERN)、美國(guó)國(guó)家航空航天局(NASA)等研究中心,可以保證研究具有高效性及高可用性。主要研究工作及創(chuàng)新點(diǎn)如下：1)為了有效地管理虛擬資源,使資源使用率最大化,并能保證用戶(hù)對(duì)資源使用的有效性,本文通過(guò)對(duì)虛擬資源的劃分、預(yù)留及調(diào)度策略等方面的研究,提出了一種面向虛擬資源的IaaS資源管理機(jī)制。該機(jī)制實(shí)現(xiàn)了資源的按需分配和調(diào)度,從而為用戶(hù)提供有效的IaaS服務(wù)。仿真實(shí)驗(yàn)結(jié)果表明,該方法能夠提高虛擬資源的使用率及保證用戶(hù)對(duì)資源使用的有效性。2)提出了一種面向網(wǎng)格化Web集群的按需IaaS資源管理機(jī)制及其分配調(diào)度策略。Web集群使用一組作為后端節(jié)點(diǎn)的系統(tǒng)資源,能夠有效地解決傳統(tǒng)Web服務(wù)器的負(fù)載均衡及故障恢復(fù)問(wèn)題,但是到目前為止,如何使Web集群更有效地利用系統(tǒng)資源尚未見(jiàn)到有效的解決方案。針對(duì)這一問(wèn)題,本章采取一種網(wǎng)格化Web集群的思想,并針對(duì)網(wǎng)格化的Web集群后端節(jié)點(diǎn)制定了一種按需IaaS資源管理機(jī)制及其分配調(diào)度策略,此技術(shù)路線(xiàn)能夠有效地提高Web集群對(duì)資源的使用效率3)提出了一種面向高性能應(yīng)用集群的自適應(yīng)IaaS資源彈性管理機(jī)制。高性能計(jì)算(High Performance Computing,HPC)集群通過(guò)整合并利用一組計(jì)算節(jié)點(diǎn)的計(jì)算能力來(lái)處理大量提交到高性能應(yīng)用系統(tǒng)中的計(jì)算作業(yè)。吞吐量是高性能計(jì)算機(jī)群的一個(gè)重要考察指標(biāo),然而不恰當(dāng)?shù)娜萘恳?guī)劃及其相關(guān)的資源管理機(jī)制,將會(huì)嚴(yán)重影響高性能計(jì)算集群的吞吐量,從而降低高性能應(yīng)用集群的效率,同時(shí)還會(huì)大大浪費(fèi)投入的成本。為了克服這些問(wèn)題,本文所提出的彈性管理機(jī)制通過(guò)動(dòng)態(tài)按需調(diào)節(jié)高性能應(yīng)用環(huán)境中異構(gòu)資源的特性,使得高性能應(yīng)用環(huán)境能夠針對(duì)等待作業(yè)進(jìn)行異構(gòu)資源的按需切換,從而減少了等待作業(yè)以提高系統(tǒng)的吞吐量、提高性能。4)電子商務(wù)應(yīng)用中,Web集群能夠作為一個(gè)強(qiáng)大的Web服務(wù)器來(lái)處理巨大的并發(fā)請(qǐng)求。然而,傳統(tǒng)的Web集群在對(duì)固有的基礎(chǔ)設(shè)施進(jìn)行預(yù)部署和預(yù)配置時(shí),由于不適當(dāng)?shù)念A(yù)先計(jì)劃,可能會(huì)導(dǎo)致過(guò)多的成本投入或資源冗余、浪費(fèi),尤其是面對(duì)無(wú)法預(yù)計(jì)的峰值問(wèn)題。因此本文首先在云計(jì)算系統(tǒng)上通過(guò)管理虛擬機(jī)來(lái)部署Web集群,以替代在固定的基礎(chǔ)設(shè)施上部署Web集群的方法,然后進(jìn)一步通過(guò)基于借入-借出(Lend-Brow)策略的資源預(yù)留機(jī)制,提出一種自適應(yīng)IaaS資源管理機(jī)制和高低水位線(xiàn)(low-high Water Line) I閾值檢測(cè)負(fù)載均衡算法,使Web集群能夠按需對(duì)集群負(fù)載進(jìn)行計(jì)算節(jié)點(diǎn)的自適應(yīng)調(diào)節(jié),從而提高了資源的利用率,且有效地減少了投入成本和能耗。5)提出了兩種基于遞歸最小成分分析的IaaS自適應(yīng)系統(tǒng)識(shí)別算法。由于云計(jì)算系統(tǒng)資源的動(dòng)態(tài)性,及自適應(yīng)控制研究對(duì)象的不確定性,常規(guī)反饋調(diào)節(jié)機(jī)制的效果往往難以令人滿(mǎn)意。針對(duì)自適應(yīng)調(diào)節(jié)的不確定性所造成的系統(tǒng)波動(dòng),需要一個(gè)高效、精確的系統(tǒng)識(shí)別算法,從而為動(dòng)態(tài)變化的系統(tǒng)負(fù)載提供預(yù)測(cè)的計(jì)算資源。近來(lái)最小成分分析法被廣泛用于系統(tǒng)識(shí)別方面,因此本章對(duì)其進(jìn)行分析并優(yōu)化,進(jìn)而在此基礎(chǔ)上構(gòu)建了a-RMCA和f-RMCA兩種算法。其中a-RMCA算法具有高精度的優(yōu)點(diǎn),但是計(jì)算速度有待改進(jìn)；而f-RMCA算法運(yùn)算速度較快,但精度稍低。最后本文從數(shù)學(xué)的角度證明了算法的收斂性,并通過(guò)數(shù)學(xué)建模仿真和真實(shí)環(huán)境測(cè)試,證明了算法的有效性。
[Abstract]:Cloud Computing (Cloud Computing) can encapsulate the heterogeneous heterogeneous resources in the system into a unified pool of computing resources by virtualization, and provide users with the required computing resources on demand. It is a service oriented computing model. Among them, the infrastructure (Infrastructure as a Service, IaaS) is used as a cloud computing service. The basic service is to provide users with the underlying infrastructure resources (computing resources, storage space and network bandwidth). As the core of the IaaS research, the resource management and scheduling strategy plays a very important role in the stability of the system and the reliability of the service. This paper focuses on the virtual resource management mechanism in IaaS. And it has studied the adaptive resource allocation and scheduling mechanism in Web service cluster. Firstly, through the popular cluster, virtualization technology, cloud computing platform and other related research, the characteristics of its resources and the shortage of existing resource management strategies are combined with the market (Marketing-Orient). ED) resource scheduling strategy, resource negotiation (Negotiating) mechanism, resource reservation (Advance Reservation) mechanism and adaptive mechanism, focusing on the study of IaaS adaptive resource management mechanism. Secondly, the existing cluster resource management mechanism in the grid application cluster, high performance computing cluster, a large number of Web clusters used in e-commerce applications, In the shortcomings of adaptive resource coordination mechanism, this paper studies the adaptive resilient management mechanism of resource allocation and scheduling based on cloud computing IaaS services. Finally, because the system recognition part is the key to adaptive control, the minimum component analysis algorithm has remarkable achievements in the field of adaptive system recognition, so this paper combines the present situation. The characteristics and existing problems of the minimum component analysis algorithm in system identification and its existing problems are studied. The research content of this paper is the research results obtained by the author in Platform company's cooperation in grid and cloud computing. These results are not only included in some market owners of IaaS. The flow of commercial projects, and has been successfully applied to the European Atomic Energy Energy (CERN), the National Aeronautics and Space Administration (NASA) and other research centers, can ensure the efficiency and high availability of the research. The main research work and innovation are as follows: 1) in order to effectively manage virtual resources, maximize the utilization of resources, and ensure the user's capital The effectiveness of source use is based on the research of virtual resource division, reservation and scheduling strategy. A virtual resource oriented IaaS resource management mechanism is proposed. This mechanism realizes the allocation and scheduling of resources on demand and provides effective IaaS services for users. The simulation experiment results show that this method can improve the virtual resource. The utilization rate of quasi resource and the effectiveness of ensuring the user's use of resources.2) proposed a IaaS resource management mechanism for grid oriented Web cluster and its allocation and scheduling policy.Web cluster to use a set of system resources as backend nodes, which can effectively solve the load balancing and failure recovery of the traditional Web server. So far, how to make Web clusters more efficient use of system resources has not yet seen effective solutions. In this chapter, a grid based Web cluster is adopted in this chapter, and a kind of IaaS resource management mechanism and allocation scheduling strategy for the grid based Web cluster back end nodes are formulated. Effectively improving the efficiency of Web cluster for resource use 3) an adaptive IaaS resource flexible management mechanism for high performance application clusters is proposed. High Performance Computing (High Performance Computing, HPC) clusters deal with a large number of computations submitted to high performance application systems by integrating and utilizing a set of computing nodes' computing power. Throughput is an important indicator of high performance computer groups. However, inappropriate capacity planning and related resource management mechanisms will seriously affect the throughput of high performance computing clusters, thus reducing the efficiency of high performance application clusters and the cost of big wave costs. In order to overcome these problems, this paper The proposed flexible management mechanism regulates the characteristics of heterogeneous resources in the high performance application environment by dynamic demand, making the high performance application environment switching on the demand for waiting jobs for heterogeneous resources, thus reducing the waiting job to improve the throughput of the system and improving the performance.4). In the e-commerce application, the Web cluster can be used as a one. A powerful Web server handles huge concurrent requests. However, when the traditional Web cluster is predeployed and preconfigured for inherent infrastructure, due to improper pre planning, it may lead to excessive cost input or resource redundancy and waste, especially in the face of unanticipated peak problems. The Web cluster is deployed by managing virtual machines to replace the method of deploying Web clusters on a fixed infrastructure, and then an adaptive IaaS resource management mechanism and a low-high Water Line (low-high Water Line) I threshold detection load are proposed in order to replace the method of deploying a cluster on a fixed infrastructure. The algorithm makes the Web cluster adaptively adjust the computing nodes of the cluster load on demand, thus improving the utilization of resources, reducing the input cost and energy consumption.5 effectively. Two kinds of IaaS adaptive system recognition algorithm based on recursive least component analysis are proposed. In order to control the uncertainty of the research object, the effect of the conventional feedback regulation mechanism is often unsatisfactory. For the system fluctuation caused by the uncertainty of adaptive adjustment, a high efficient and accurate system recognition algorithm is needed to provide the pre measured computing resources for the dynamic system load. It is widely used for system recognition, so this chapter analyses and optimizes it. On this basis, two algorithms of a-RMCA and f-RMCA are constructed. Among them, the a-RMCA algorithm has the advantages of high precision, but the computing speed needs to be improved, while the f-RMCA algorithm has a fast operation speed, but the precision is a little low. Finally, this paper proves the algorithm from the mathematical point of view. The validity of the algorithm is proved by mathematical modeling and simulation and real environment testing.

【學(xué)位授予單位】：西北工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TP393.09

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