發(fā)布時(shí)間：2018-01-30 22:58

  本文關(guān)鍵詞： 云服務(wù)系統(tǒng) 組件服務(wù)副本 吞吐量約束 拓?fù)淦ヅ?灰色-馬爾科夫預(yù)測(cè) CloudSim　出處：《東北大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著云計(jì)算技術(shù)的日益成熟,云服務(wù)系統(tǒng)已經(jīng)成為了一種重要的軟件開發(fā)模式。在云服務(wù)系統(tǒng)應(yīng)用中,組件服務(wù)被部署在不同的服務(wù)器或者服務(wù)器集群中。由于用戶訪問行為的不確定性,當(dāng)用戶訪問量增加時(shí),云服務(wù)系統(tǒng)可以動(dòng)態(tài)增加組件服務(wù)副本,提高系統(tǒng)的吞吐量進(jìn)而保證云服務(wù)系統(tǒng)性能。從而,如何放置及選擇合適的組件服務(wù)副本以保證云服務(wù)系統(tǒng)的性能已經(jīng)成為了目前研究的一個(gè)熱點(diǎn)。本文首先分析了云服務(wù)系統(tǒng)中組件服務(wù)副本相關(guān)技術(shù),針對(duì)于其中的組件服務(wù)副本數(shù)量估計(jì)、組件服務(wù)副本放置以及組件服務(wù)副本選擇等三個(gè)關(guān)鍵問題展開了研究工作。針對(duì)于組件服務(wù)副本數(shù)量估計(jì)問題,提出了一個(gè)基于吞吐量約束的組件服務(wù)副本數(shù)量估計(jì)算法,該算法根據(jù)所建立的組件服務(wù)吞吐量聚合規(guī)則,面向云服務(wù)系統(tǒng)吞吐量約束保證,計(jì)算每個(gè)組件服務(wù)的吞吐量約束,進(jìn)而得到每個(gè)組件服務(wù)副本的數(shù)量。針對(duì)組件服務(wù)副本放置問題,提出了一個(gè)基于圖拓?fù)淦ヅ涞慕M件服務(wù)副本放置算法,該算法使用聚類技術(shù)獲得組件服務(wù)和計(jì)算節(jié)點(diǎn)的拓?fù)浣Y(jié)構(gòu),并通過匹配兩種拓?fù)浣Y(jié)構(gòu)來進(jìn)行組件服務(wù)副本放置。該算法可以有效的降低云應(yīng)用的執(zhí)行周期和延遲時(shí)間。針對(duì)組件服務(wù)副本選擇問題,通過結(jié)合負(fù)載模型和灰色-馬爾可夫預(yù)測(cè)技術(shù)提出了基于灰色-馬爾可夫預(yù)測(cè)的組件服務(wù)副本選擇算法。在進(jìn)行組件服務(wù)副本選擇時(shí),對(duì)計(jì)算節(jié)點(diǎn)的負(fù)載進(jìn)行預(yù)測(cè),并從中選擇負(fù)載最小的計(jì)算節(jié)點(diǎn)來進(jìn)行組件服務(wù)副本選擇。通過使用該算法,可以有效的提高組件服務(wù)副本的利用率�；�于CloudSim云仿真軟件搭建了一個(gè)仿真實(shí)驗(yàn)環(huán)境并開展了一系列實(shí)驗(yàn),仿真實(shí)驗(yàn)結(jié)果表明了所提出的方案和算法的有效性。
[Abstract]:With the development of cloud computing technology, cloud service system has become an important software development model. Component services are deployed in different servers or server clusters. Because of the uncertainty of user access behavior, cloud service systems can dynamically increase replicas of component services when user traffic increases. Improve the throughput of the system and thus ensure the performance of the cloud service system. How to place and select the appropriate component service replica to ensure the performance of cloud service system has become a hot topic. Firstly, this paper analyzes the component service replica related technology in cloud service system. This paper focuses on the estimation of the number of component service replicas, the placement of component service replicas and the selection of component service replicas, and aims at estimating the number of component service replicas. An algorithm for estimating the number of component service replicas based on throughput constraints is proposed. According to the established aggregation rules of component service throughput, the algorithm is oriented to the throughput constraint assurance of cloud service systems. The throughput constraints of each component service are calculated, and then the number of replicas of each component service is obtained. A component service replica placement algorithm based on graph topology matching is proposed to address the problem of component service replica placement. The algorithm uses clustering technology to obtain the topology of component services and compute nodes. The algorithm can effectively reduce the execution period and delay time of the cloud application, aiming at the problem of component service replica selection. By combining the load model and the grey-Markov prediction technology, this paper proposes a component service replica selection algorithm based on the gray Markov prediction, which is used to select the component service replicas. The load of the computing node is predicted and the computing node with the smallest load is selected to select the copy of the component service. It can effectively improve the utilization rate of component service replicas. A simulation environment based on CloudSim cloud simulation software is built and a series of experiments are carried out. Simulation results show the effectiveness of the proposed scheme and algorithm.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.09
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本文編號(hào)：1477465