本文選題：網(wǎng)絡(luò)虛擬化 + 虛擬網(wǎng)絡(luò)�。� 參考：《東北大學(xué)》2015年博士論文

【摘要】：云計(jì)算最大的特點(diǎn)是使得IT資源可以像水、電、天然氣一樣按需租賃并計(jì)費(fèi)。租賃的實(shí)現(xiàn)離不開虛擬化技術(shù)的支持,主要手段是通過(guò)虛擬化技術(shù)將物理資源集中在一起形成共享虛擬資源池,實(shí)現(xiàn)虛擬資源動(dòng)態(tài)分配的多租賃特性。云環(huán)境下的市場(chǎng)被分為兩部分：基礎(chǔ)設(shè)施提供商和服務(wù)提供商,前者擁有并維護(hù)硬件資源,對(duì)其進(jìn)行抽象形成虛擬資源；后者根據(jù)實(shí)際需求按需租賃前者的虛擬資源來(lái)構(gòu)建定制的虛擬網(wǎng)絡(luò)以便于向用戶提供服務(wù)。云環(huán)境下,這種典型的云環(huán)境下多租賃市場(chǎng)運(yùn)營(yíng)機(jī)制對(duì)于企業(yè)節(jié)省成本及提高資源利用率具有重要意義。因此,設(shè)計(jì)合理的虛擬資源租賃機(jī)制,提高物理資源利用率,是云計(jì)算虛擬資源分配研究的關(guān)鍵問題。現(xiàn)有的虛擬資源分配研究?jī)?nèi)容主要集中在租賃交易方式和虛擬網(wǎng)絡(luò)部署兩個(gè)方面。前者更加宏觀,研究云市場(chǎng)競(jìng)爭(zhēng)的多租賃環(huán)境下基礎(chǔ)設(shè)施提供商和服務(wù)提供商之間的資源供求關(guān)系,以最大化社會(huì)整體利益為目標(biāo),并保證公平、高效的競(jìng)爭(zhēng)環(huán)境。后者以硬件資源利用率為目標(biāo),研究如何在有限的硬件資源上盡可能滿足更多用戶的需求,為用戶直接分配資源并創(chuàng)建虛擬網(wǎng)絡(luò),屬于虛擬網(wǎng)絡(luò)映射問題(Virtual Network Embedding, VNE)。雖然目前己經(jīng)有多種虛擬資源分配算法被提出,但現(xiàn)有研究成果仍然存在以下幾方面的問題：(1)云環(huán)境下資源分配定價(jià)機(jī)制不夠靈活；(2)底層網(wǎng)絡(luò)的資源利用效率差；(3)虛擬網(wǎng)絡(luò)映射成功率低；(4)物理網(wǎng)絡(luò)易出現(xiàn)資源占用不平衡問題；(5)僅考慮虛擬網(wǎng)絡(luò)請(qǐng)求固定不變的情況；(6)虛擬網(wǎng)絡(luò)服務(wù)可靠性差。因此,針對(duì)上述問題,本文對(duì)虛擬資源分配問題進(jìn)行了深入研究,并取得如下成果：(1)考慮到云市場(chǎng)多租賃環(huán)境下服務(wù)提供商之間的競(jìng)爭(zhēng)關(guān)系,他們不可能完全共享信息。因此利用隱馬爾可夫理論,根據(jù)服務(wù)提供商的歷史資源需求情況預(yù)測(cè)其當(dāng)前出價(jià),進(jìn)而構(gòu)建動(dòng)態(tài)博弈定價(jià)模型以激勵(lì)服務(wù)提供商選擇最優(yōu)出價(jià)策略,從而實(shí)現(xiàn)利益最大化。在資源分配階段,設(shè)計(jì)了以多種類資源單位價(jià)格為基準(zhǔn)的資源分配模型,該模型支持多服務(wù)提供商、多種資源同時(shí)分配,增加了基礎(chǔ)設(shè)施提供商的收益并能夠提高競(jìng)爭(zhēng)公平性。(2)針對(duì)多租賃模式下的虛擬網(wǎng)絡(luò)映射問題,以降低底層鏈路負(fù)載,加快映射效率,提高物理資源利用率為目標(biāo),將離散粒子群算法與虛擬節(jié)點(diǎn)映射規(guī)則相結(jié)合,提出了物理節(jié)點(diǎn)可復(fù)用、負(fù)載可控制的虛擬網(wǎng)絡(luò)映射算法。算法能夠節(jié)約物理鏈路的帶寬資源。在保證網(wǎng)絡(luò)負(fù)載的前提下,獲得了較好的物理節(jié)點(diǎn)利用率和收益成本比。為了進(jìn)一步提高算法在大規(guī)模網(wǎng)絡(luò)下的求解效率,引入了交叉算子,設(shè)計(jì)了混合智能群算法,解決了粒子群算法容易陷入局部最優(yōu)解,無(wú)法達(dá)到全局最優(yōu)而出現(xiàn)早熟收斂的問題,能夠使得物理網(wǎng)絡(luò)獲得較高的收益成本比。(3)考慮到網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)對(duì)虛擬網(wǎng)絡(luò)映射成功率的影響,重新定義了節(jié)點(diǎn)的綜合能力,提出一種基于拓?fù)涓兄奶摂M網(wǎng)絡(luò)映射算法。在映射過(guò)程中加入了拓?fù)涓兄攘糠椒ㄒ暂o助選擇映射方案,同時(shí)引入了滑動(dòng)窗口技術(shù)對(duì)虛擬網(wǎng)絡(luò)請(qǐng)求進(jìn)行預(yù)處理,使得算法能夠獲得較好的接受率和收益成本比。(4)以提高虛擬網(wǎng)絡(luò)接受率和底層網(wǎng)絡(luò)利用率,避免物理網(wǎng)絡(luò)節(jié)點(diǎn)和鏈路出現(xiàn)瓶頸為目標(biāo),建立了虛擬網(wǎng)絡(luò)重配置問題的多目標(biāo)優(yōu)化數(shù)學(xué)模型,并采用元啟發(fā)算法提出了一種虛擬網(wǎng)絡(luò)重配置算法。算法可以顯著地降低物理節(jié)點(diǎn)和鏈路的最大負(fù)載,并能夠保證虛擬網(wǎng)絡(luò)請(qǐng)求獲得較高的接受率。(5)針對(duì)虛擬網(wǎng)絡(luò)請(qǐng)求資源動(dòng)態(tài)變化的實(shí)際情況,提出了面向動(dòng)態(tài)虛擬網(wǎng)絡(luò)請(qǐng)求的虛擬網(wǎng)絡(luò)映射算法。以混合線性規(guī)劃理論為基礎(chǔ),建立了以最小映射和遷移代價(jià)為優(yōu)化目標(biāo)的映射模型,該算法采用多隊(duì)列存儲(chǔ)不同類型的虛擬網(wǎng)絡(luò)請(qǐng)求,優(yōu)先映射需要釋放資源的請(qǐng)求以獲得更多的資源,從而降低了鏈路映射成本和遷移成本。(6)針對(duì)物理網(wǎng)絡(luò)節(jié)點(diǎn)和鏈路失效問題,從容錯(cuò)角度出發(fā),為虛擬網(wǎng)絡(luò)增加備份的冗余虛擬節(jié)點(diǎn)和虛擬鏈路,以最小化映射成本為目標(biāo)建立整數(shù)線性規(guī)劃模型,設(shè)計(jì)了面向物理網(wǎng)絡(luò)節(jié)點(diǎn)與鏈路失效的可靠虛擬網(wǎng)絡(luò)映射算法。算法通過(guò)評(píng)估虛擬網(wǎng)絡(luò)節(jié)點(diǎn)的重要性來(lái)定位需要備份的節(jié)點(diǎn)和鏈路,然后建立附加備份資源的虛擬網(wǎng)絡(luò)增廣圖并對(duì)其進(jìn)行映射,使得用戶的虛擬網(wǎng)絡(luò)獲得了更好的可靠性支持�？傊�,文本從云環(huán)境下虛擬資源分配和虛擬網(wǎng)絡(luò)映射兩個(gè)角度出發(fā),著重研究了基于非完全信息博弈的虛擬資源分配算法和網(wǎng)絡(luò)虛擬化環(huán)境中適用于不同場(chǎng)景的虛擬網(wǎng)絡(luò)映射算法。理論分析和大量的實(shí)驗(yàn)結(jié)果證明了這些方法的有效性和高效性。我們希望基于這些方法和技術(shù)進(jìn)一步開發(fā)云資源管理、調(diào)度系統(tǒng)。
[Abstract]:The biggest feature of cloud computing is that IT resources can be leased and charged on demand like water, electricity and natural gas. The implementation of the lease can not be separated from the support of virtualization technology. The main means is to concentrate the physical resources together by virtualization technology to form a shared virtual resource pool and realize the multi lease characteristics of the dynamic allocation of virtual resources. The market is divided into two parts: the infrastructure provider and the service provider, the former owns and maintains the hardware resources, and abstracts it into virtual resources; the latter leases the former virtual resource according to the actual demand to build a customized virtual network to facilitate the service to the user. In the cloud environment, this typical cloud environment The operation mechanism of the next multi lease market is of great significance for the enterprise to save cost and improve the utilization of resources. Therefore, it is the key problem to design a reasonable leasing mechanism of virtual resources and improve the utilization rate of physical resources. There are two aspects of virtual network deployment. The former is more macro. It studies the supply and demand relationship between infrastructure providers and service providers in the multi rental environment of the cloud market competition, aims at maximizing the overall social benefits, and ensures a fair and efficient competitive environment. The latter aims at the use of hardware resources and studies how it is limited. The hardware resources are as much as possible to meet the needs of more users, directly allocate resources for users and create virtual networks, which belong to the Virtual Network Embedding (VNE). Although many kinds of virtual resource allocation algorithms have been proposed at present, the existing research results still have the following problems: (1) cloud ring The resource allocation pricing mechanism under the environment is not flexible enough; (2) the low utilization efficiency of the underlying network is poor; (3) the success rate of the virtual network mapping is low; (4) the physical network is prone to the imbalance of resource occupancy; (5) only the virtual network request is fixed and fixed; (6) the virtual network service is poor in reliability. Therefore, this paper is aimed at the above problem. The problem of virtual resource allocation is studied in depth, and the following results are obtained: (1) considering the competitive relationship between service providers in the multi rental environment of the cloud market, they can not fully share information. Therefore, the hidden Markov theory is used to predict the current bids according to the resource requirements of the service providers and then build up. The dynamic game pricing model is used to motivate service providers to select the best bid strategy and maximize the benefit. In the resource allocation stage, a resource allocation model based on multi resource unit price is designed. The model supports multi service providers, multiple resources are allocated at the same time, and the revenue of infrastructure providers can be increased and can be increased. In order to improve the fairness of competition. (2) aiming at the problem of virtual network mapping under the multi lease mode, in order to reduce the load of the underlying link, accelerate the mapping efficiency and improve the utilization of physical resources, the discrete particle swarm optimization (PSO) and the virtual node mapping rule are combined to propose a virtual network mapping algorithm which can be reused and controlled by the physical node. The algorithm can save the bandwidth resources of physical links. Under the premise of guaranteeing the network load, the better utilization ratio of physical nodes and the ratio of profit and cost are obtained. In order to further improve the efficiency of the algorithm in the large-scale network, the cross operator is introduced, and the hybrid intelligent group algorithm is designed to solve the particle swarm algorithm easily falling into the local area. The optimal solution, which can not reach the global optimal, has the problem of premature convergence, which can make the physical network obtain higher profit and cost ratio. (3) considering the influence of the network topology on the success rate of the virtual network mapping, the comprehensive ability of the node is redefined, and a virtual network mapping algorithm based on topology perception is proposed. In the process, the topology perception measure is added to assist the selection mapping scheme, and the sliding window technology is introduced to preprocess the virtual network request, which makes the algorithm get better acceptance rate and benefit cost ratio. (4) to improve the acceptance rate of virtual network and the utilization rate of the underlying network, avoid the occurrence of physical network nodes and links. The multi-objective optimization mathematical model of virtual network reconfiguration is established, and a virtual network reconfiguration algorithm is proposed by using the meta heuristic algorithm. The algorithm can significantly reduce the maximum load of physical nodes and links, and can guarantee the higher acceptance rate of virtual network requests. (5) for virtual network request capital. A virtual network mapping algorithm for dynamic virtual network requests is proposed. Based on the theory of mixed linear programming, a mapping model with minimum mapping and migration costs is established. The algorithm uses multiple queues to store different types of virtual network requests, and priority mapping needs to release resources. The request is to obtain more resources, thus reducing the link mapping cost and transfer cost. (6) aiming at the physical network node and link failure problem, from the fault tolerance point of view, the redundant virtual nodes and virtual links are added to the virtual network, and the integer linear programming model is established to minimize the mapping cost. A reliable virtual network mapping algorithm for physical network nodes and link failures. By evaluating the importance of virtual network nodes to locate the nodes and links that need to be backed up, a virtual network augmented map with additional backup resources is established and mapped to make the user's virtual network better support for reliability. In a word, From the two angles of the virtual resource allocation and virtual network mapping in the cloud environment, the text focuses on the research of the virtual resource allocation algorithm based on the incomplete information game and the virtual network mapping algorithm for different scenarios in the network virtualization environment. The theoretical analysis and a large number of experimental results prove the effectiveness and high of these methods. Effectiveness. We hope to further develop cloud resource management and scheduling system based on these methods and technologies.

【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP393.07

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