本文選題：云計算　切入點(diǎn)：資源調(diào)度　出處：《北方工業(yè)大學(xué)》2017年碩士論文

【摘要】：云計算作為一種新的計算模型,利用分布式計算、虛擬化等多種技術(shù)將大規(guī)模的硬件資源整合成資源池,使用戶能夠隨時隨地按需通過網(wǎng)絡(luò)的形式訪問計算資源,為了最大限度的利用云計算平臺,提升云資源的利用率,尋找優(yōu)秀的資源調(diào)度策略是云數(shù)據(jù)中心需要解決的重要問題,在研究資源調(diào)度策略時應(yīng)充分考慮應(yīng)用的特點(diǎn)及云計算的實(shí)際場景,使云平臺能夠在達(dá)到負(fù)載均衡的條件下提高資源利用率,從而來制定合理的資源調(diào)度方案。本文通過研究云計算的相關(guān)技術(shù),深入對現(xiàn)有的資源調(diào)度方案進(jìn)行分析,然后提出了應(yīng)用敏感的虛擬資源動態(tài)調(diào)度方案,本文的主要工作如下:首先,我們對應(yīng)用的類型予以區(qū)分,定義了應(yīng)用敏感度,區(qū)別于傳統(tǒng)的資源調(diào)度,只要應(yīng)用缺少資源,就盲目的增加各種資源的方式,資源調(diào)度應(yīng)該根據(jù)應(yīng)用的特點(diǎn),對應(yīng)用所需要的特定資源進(jìn)行分配,并且在云平臺有多個應(yīng)用需要進(jìn)行資源調(diào)度時要有一定的調(diào)度優(yōu)先級,從而整體提升資源的利用率。其次,傳統(tǒng)的資源調(diào)度僅僅通過對資源的監(jiān)控來調(diào)度資源,缺少了資源就增加,多了就減少,或者干脆一次性分配足夠的資源,這樣雖然能夠滿足應(yīng)用在整個運(yùn)行過程中的需求,但必然會導(dǎo)致浪費(fèi),針對這樣的問題,我們提出了基于ARIMA預(yù)測的資源調(diào)度模型,根據(jù)資源的需求情況提前進(jìn)行資源的調(diào)度,從而防患于未然,既在一定程度上避免了資源調(diào)度的延遲性,也避免了資源過度浪費(fèi),提升了資源的利用率。最后,本文實(shí)現(xiàn)了一套基于應(yīng)用敏感的資源調(diào)度機(jī)制的系統(tǒng),本文中所設(shè)計的模型與算法均已應(yīng)用到該系統(tǒng)中,目前系統(tǒng)已經(jīng)通過第三方測試,并完成了交付使用。相比傳統(tǒng)的資源調(diào)度方式,我們提出的方法能夠及時有效的調(diào)度資源,并能夠提高資源利用率,降低云數(shù)據(jù)中心的資源消耗與管理成本。
[Abstract]:As a new computing model, cloud computing integrates large-scale hardware resources into resource pools by using distributed computing, virtualization and other technologies, enabling users to access computing resources anytime, anywhere and on demand through the network. In order to maximize the use of cloud computing platform, improve the utilization of cloud resources, and find an excellent resource scheduling strategy is an important problem that needs to be solved in cloud data center. When studying the resource scheduling strategy, we should fully consider the characteristics of application and the actual situation of cloud computing, so that the cloud platform can improve the resource utilization under the condition of load balance. In this paper, through the research of cloud computing technology, the existing resource scheduling scheme is analyzed, and then the dynamic scheduling scheme of virtual resources is proposed, which is sensitive to virtual resource scheduling. The main work of this paper is as follows: first, we distinguish the types of applications, define the application sensitivity, different from the traditional resource scheduling, as long as the application is short of resources, blindly increase all kinds of resources. Resource scheduling should be based on the characteristics of the application to allocate the specific resources needed by the application, and there should be a certain scheduling priority when there are more than one application in the cloud platform, so as to improve the overall utilization of resources. Traditional resource scheduling only through the monitoring of resources to schedule resources, the lack of resources will increase, reduce the number of resources, or simply allocate enough resources, although this can meet the needs of the application in the entire running process, But it will inevitably lead to waste. In view of this problem, we put forward a resource scheduling model based on ARIMA prediction, which can schedule resources ahead of time according to the demand of resources, so as to prevent trouble in the future. It not only avoids the delay of resource scheduling to some extent, but also avoids the excessive waste of resources, and improves the utilization of resources. Finally, this paper implements a system based on application sensitive resource scheduling mechanism. The model and algorithm designed in this paper have been applied to the system. At present, the system has passed the third party test and completed the delivery. Compared with the traditional resource scheduling method, the proposed method can schedule the resources in a timely and effective manner. It can improve resource utilization and reduce resource consumption and management cost of cloud data center.
【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP393.09

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 孫蘭芳;張曦煌;;基于蜜蜂采蜜機(jī)理的云計算負(fù)載均衡策略[J];計算機(jī)應(yīng)用研究;2016年04期

2 薛濤;馬騰;;基于資源權(quán)重最大資源利用率的動態(tài)資源調(diào)度算法[J];計算機(jī)應(yīng)用研究;2016年05期

3 薛濤;劉龍;;云計算中虛擬機(jī)資源自動配置技術(shù)的研究[J];計算機(jī)應(yīng)用研究;2016年03期

4 王常芳;徐文忠;;一種用于云計算資源調(diào)度的雙向蟻群優(yōu)化算法[J];計算機(jī)測量與控制;2015年08期

5 張恒巍;韓繼紅;衛(wèi)波;王晉東;;基于Map-Reduce模型的云資源調(diào)度方法研究[J];計算機(jī)科學(xué);2015年08期

6 陳海濤;;改進(jìn)的猴群算法在云計算資源分配中的研究[J];計算機(jī)系統(tǒng)應(yīng)用;2015年08期

7 李晨;劉博;李云;;云環(huán)境下基于碎片影響度的提前預(yù)定資源調(diào)度策略[J];合肥工業(yè)大學(xué)學(xué)報(自然科學(xué)版);2015年07期

8 蔣華;張樂乾;王鑫;;基于多維評價模型及改進(jìn)蟻群優(yōu)化算法的云計算資源調(diào)度策略[J];計算機(jī)測量與控制;2015年07期

9 郭麗嬌;王慶生;;云環(huán)境下的虛擬資源調(diào)度智能優(yōu)化策略[J];計算機(jī)應(yīng)用與軟件;2015年06期

10 朱春鴿;張哲宇;劉欣然;孫斌;張鴻;;虛擬計算環(huán)境下基于模糊聚類的資源調(diào)度算法[J];北京郵電大學(xué)學(xué)報;2015年S1期

相關(guān)碩士學(xué)位論文 前1條

1 陳東海;云環(huán)境下虛擬機(jī)資源調(diào)度算法的研究與實(shí)現(xiàn)[D];東北大學(xué);2014年

，

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