本文選題：監(jiān)控 + 云計算�。� 參考：《浙江大學》2017年碩士論文

【摘要】：隨著云計算的發(fā)展,其提供的功能也越來越豐富,管理的計算機集群規(guī)模也逐漸上升,云基礎架構(gòu)和部署的高效管理是目前的一個引人注目的話題。監(jiān)控工具和監(jiān)控技術在這方面可以發(fā)揮重要作用,收集相關的信息,提供給管理員或用戶以便他們做出相應的決策。本文通過對自搭建的OpenStack云環(huán)境得到的實驗數(shù)據(jù)以及來自某公有云平臺的監(jiān)控數(shù)據(jù)的分析,發(fā)現(xiàn)并找出數(shù)據(jù)中隱藏的規(guī)律、特征。通過分析了線性回歸算法和馬爾科夫鏈算法兩個預測算法在預測云環(huán)境的監(jiān)控數(shù)據(jù)的應用以及它們的缺陷,利用發(fā)現(xiàn)的規(guī)律、特征對預測算法進行優(yōu)化改進,提高算法預測的命中率、降低算法的某些開銷。本文的主要研究工作如下:1)研究云計算的監(jiān)控,通過對歷史數(shù)據(jù)的分析,發(fā)現(xiàn)數(shù)據(jù)中的規(guī)律、特征,將一天分為兩個不同的時間段:忙碌期和平穩(wěn)期,在不同的時期內(nèi)由于用戶訪問量的差異而引起資源消耗的差異,進而影響到在不同時期內(nèi)收集到的數(shù)據(jù)特征的不同;2)研究時間序列預測算法,針對數(shù)據(jù)的特征,提出新的算法模型——結(jié)合線性回歸算法與馬爾科夫鏈算法(line discrete time markov chain,稱為L-DTMC算法),并且根據(jù)不同的時間段采用不同的算法,然后根據(jù)誤差容忍度(Error Tolerant Degree,ETD)決定是否需要更新算法模型以及是否需要向服務器發(fā)送更新數(shù)據(jù);3)通過部署一個小型的云平臺,分別針對被監(jiān)控節(jié)點與數(shù)據(jù)服務器的數(shù)據(jù)的一致性、算法的預測效果以及執(zhí)行該算法對系統(tǒng)的影響這三個方面對本文提出的算法進行模擬驗證實驗,通過分析實驗結(jié)果驗證了本文工作的有效性。
[Abstract]:With the development of cloud computing, the functions provided by cloud computing are becoming more and more abundant, and the scale of managed computer clusters is increasing gradually. The efficient management of cloud infrastructure and deployment is a noticeable topic at present. Monitoring tools and monitoring techniques can play an important role in this regard, collecting relevant information and providing it to administrators or users so that they can make appropriate decisions. Based on the analysis of the experimental data obtained from OpenStack cloud environment and the monitoring data from a public cloud platform, the hidden rules and features of the data are found and found in this paper. Based on the analysis of the application of linear regression algorithm and Markov chain algorithm in the monitoring data of cloud environment prediction and their defects, the prediction algorithm is optimized and improved by using the discovered rules and features. Improve the hit ratio of the algorithm prediction, reduce some of the cost of the algorithm. The main research work of this paper is as follows: 1) Research cloud computing monitoring, through the analysis of historical data, find the laws and characteristics of the data, divide the day into two different time periods: busy period and stationary period. In different periods, the difference of resource consumption caused by the difference of user visits will affect the different features of the data collected in different periods. (2) to study the time series prediction algorithm, aiming at the characteristics of the data. A new algorithm model, combining linear regression algorithm and Markov chain algorithm called L-DTMC algorithm, is proposed, and different algorithms are used according to different time periods. Then according to error tolerance (ETD) to decide whether to update the algorithm model and whether to send update data to the server. By deploying a small cloud platform, the consistency of the data between the monitored node and the data server is analyzed, respectively. The prediction effect of the algorithm and the effect of executing the algorithm on the system are simulated and validated. The effectiveness of the proposed algorithm is verified by the analysis of the experimental results.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP301.6;O211.62

【參考文獻】

相關期刊論文 前2條

1 李君;;云計算監(jiān)控研究綜述[J];信息通信技術;2014年01期

2 龔強;;云計算應用展望與思考[J];信息技術;2013年01期

相關碩士學位論文 前2條

1 李嵐清;云平臺監(jiān)控系統(tǒng)的研究和實現(xiàn)[D];浙江大學;2016年

2 張棋勝;云計算平臺監(jiān)控系統(tǒng)的研究與應用[D];北京交通大學;2011年

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本文編號：2059782