本文關(guān)鍵詞： 移動(dòng)云 協(xié)作微云 計(jì)算資源分配 通信資源分配　出處：《東南大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：智能移動(dòng)設(shè)備逐漸成為人們生活中不可或缺的一部分。但是,它始終無法克服便攜性與自身資源有限之間的矛盾,直到移動(dòng)云計(jì)算被提出。在移動(dòng)云計(jì)算模型中,移動(dòng)應(yīng)用被轉(zhuǎn)移至云資源處理,從而達(dá)到節(jié)約移動(dòng)終端自身資源的目的。目前移動(dòng)云計(jì)算中可利用云資源包括：遠(yuǎn)程公共云、臨近微云。相較于遠(yuǎn)程云計(jì)算中心的資源,臨近微云具有通信時(shí)延短的優(yōu)點(diǎn),但是也具有單點(diǎn)計(jì)算資源有限的缺點(diǎn)。針對(duì)臨近微云單點(diǎn)計(jì)算資源有限這一缺陷,本文提出移動(dòng)協(xié)作微云計(jì)算(M3C, Mobile Cooperative Cloudlet Computing)系統(tǒng),在該系統(tǒng)中,來自移動(dòng)用戶終端的應(yīng)用被表示為由子任務(wù)以及子任務(wù)之間的數(shù)據(jù)約束組成的工作流,通過將子任務(wù)被分配到系統(tǒng)中的多個(gè)接入點(diǎn)分別處理,提高工作流處理的并行度,縮短用戶等待處理結(jié)果的時(shí)間。本論文研究的主要內(nèi)容是M3C系統(tǒng)的計(jì)算資源與通信資源分配方案。首先,本文將已有的粒子群搜索(PSO, Particle Swarm Optimization)算法、異構(gòu)最短結(jié)束時(shí)間(HEFT, Heterogeneous Earliest- Finish-Time)算法、部分關(guān)鍵路徑(PCP, Partial Critical Path)算法應(yīng)用于M3C系統(tǒng)中。其中,PSO算法屬于隨機(jī)搜索算法,該算法在迭代次數(shù)較多時(shí)可以達(dá)到較好的結(jié)果,但是計(jì)算量大。HEFT算法與PCP算法均屬于列表啟發(fā)式算法,分配過程包括兩部分：計(jì)算列表、按列表次序分配。不同的列表啟發(fā)式算法區(qū)別在于列表排序策略和分配策略。HEFT中按照任務(wù)優(yōu)先級(jí)進(jìn)行排序,再將任務(wù)分配到能夠最早完成該任務(wù)的接入點(diǎn)；PCP算法在任務(wù)優(yōu)先級(jí)的基礎(chǔ)上,將任務(wù)劃歸到若干個(gè)集合中,以集合(PCP)為單位進(jìn)行分配,同一個(gè)集合中的任務(wù)被分配到同一個(gè)接入點(diǎn)。其次,本文以計(jì)算資源分配為基礎(chǔ),獲得系統(tǒng)無線鏈路集合,存在無線通信需求的接入點(diǎn)之間的距離需小于距離閾值,無線鏈路集合對(duì)應(yīng)了一組接入點(diǎn)間距離約束條件。本文分別利用窮舉和動(dòng)態(tài)規(guī)劃方法對(duì)接入點(diǎn)進(jìn)行部署,接入點(diǎn)位置不僅滿足距離約束條件且覆蓋面積最大化。在進(jìn)行信道分配時(shí),考慮鏈路間的相互干擾,避免同一條信道被相互干擾的鏈路同時(shí)占用,通過多次循環(huán)實(shí)現(xiàn)均勻分配信道。最后,設(shè)計(jì)一系列仿真實(shí)驗(yàn)用于評(píng)估系統(tǒng)性能。每次實(shí)驗(yàn)包括四步：計(jì)算資源分配、接入點(diǎn)部署、信道資源分配以及運(yùn)行模擬。仿真實(shí)驗(yàn)的內(nèi)容包括：M3C系統(tǒng)與ICloudlet系統(tǒng)對(duì)比仿真,以證明接入點(diǎn)之間的協(xié)作能夠縮短應(yīng)用工作流處理時(shí)間；控制變量法分析M3C系統(tǒng)中的接入點(diǎn)個(gè)數(shù)、服務(wù)器切片數(shù)、計(jì)算資源量、信道假設(shè)以及通信資源量對(duì)系統(tǒng)性能的影響,仿真結(jié)果表明,系統(tǒng)性能不僅取決于計(jì)算資源與通信資源的分配情況,也和計(jì)算資源與通信資源比例有關(guān),合理設(shè)計(jì)計(jì)算與通信資源比例,才能有效提高系統(tǒng)性能。
[Abstract]:Intelligent mobile devices have gradually become an indispensable part of people's lives. However, it can not overcome the contradiction between portability and its limited resources. In the mobile cloud computing model, mobile applications are transferred to cloud resource processing. At present, the cloud resources can be used in mobile cloud computing, including: remote public cloud, near micro-cloud. Compared with the resources of remote cloud computing center. Proximity microcloud has the advantage of short communication delay, but it also has the disadvantage of limited single-point computing resources. Aiming at the limitation of single-point computing resources, mobile cooperative micro-cloud computing is proposed in this paper. The Mobile Cooperative Cloudlet computing system. An application from a mobile user terminal is represented as a workflow composed of sub-tasks and data constraints between sub-tasks, which are processed separately by assigning sub-tasks to a plurality of access points in the system. Improve the parallelism of workflow processing and shorten the time for users to wait for processing results. The main content of this paper is the allocation of computing and communication resources in M3C system. First of all. In this paper, the existing particle swarm optimization (PSO) algorithm, Particle Swarm optimization algorithm, is used to calculate the minimum end time of isomerism. Heterogeneous Earliest-Finish-Timealgorithm, some critical paths. Partial Critical path algorithm is applied to M3C system. The algorithm can get better results when the number of iterations is more, but the algorithm of. Heft and PCP both belong to list heuristic algorithm, and the assignment process includes two parts: computing list. The difference between different list heuristic algorithms is that the list sorting strategy and the assignment strategy .HEFT sort according to the priority of the task. The task is then assigned to the access point that can finish the task as early as possible; On the basis of the priority of tasks, the PCP algorithm divides the tasks into several sets and assigns the tasks in the same set to the same access point. Secondly, the tasks in the same set are assigned to the same access point. On the basis of computing resource allocation, the system wireless link set is obtained. The distance between access points with wireless communication needs to be less than the distance threshold. The wireless link set corresponds to a set of distance constraints between access points. In this paper, we use exhaustive and dynamic programming methods to deploy access points. The location of the access point not only satisfies the distance constraint condition but also maximizes the coverage area. In the channel allocation, the interference between links is considered to avoid the same channel being occupied by the interference link simultaneously. Finally, a series of simulation experiments are designed to evaluate the performance of the system. Each experiment consists of four steps: computing resource allocation, access point deployment. Simulation of channel resource allocation and operation. The simulation results show that the collaboration between access points can shorten the processing time of application workflow. The control variable method is used to analyze the influence of the number of access points, the number of server slices, the amount of computing resources, the channel assumption and the amount of communication resources on the performance of M3C system. The system performance depends not only on the allocation of computing resources and communication resources, but also on the ratio of computing resources to communication resources.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN929.5;TP3

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