本文選題：云計(jì)算 + 云數(shù)據(jù)中心　； 參考：《北京郵電大學(xué)》2014年碩士論文

【摘要】：云計(jì)算是當(dāng)下具有巨大潛力價(jià)值的新興計(jì)算技術(shù),它利用大規(guī)模的硬件和虛擬資源為用戶(hù)提供動(dòng)態(tài)的應(yīng)用服務(wù)。為了最大程度的利用云資源,充分發(fā)揮云計(jì)算的最大潛能,挖掘高效的資源調(diào)度策略是我們的當(dāng)務(wù)之急。資源調(diào)度策略負(fù)責(zé)在異構(gòu)云資源池中選擇匹配合適的資源來(lái)執(zhí)行對(duì)任務(wù)請(qǐng)求的處理。在制訂資源調(diào)度策略時(shí)應(yīng)充分考慮云計(jì)算的實(shí)際場(chǎng)景,以尋找到最優(yōu)的調(diào)度方案。 本文通過(guò)學(xué)習(xí)云計(jì)算資源調(diào)度相關(guān)技術(shù),深入對(duì)現(xiàn)有調(diào)度算法進(jìn)行研究分析。然后主要針對(duì)兩種場(chǎng)景下的云資源調(diào)度策略進(jìn)行優(yōu)化,旨在提高服務(wù)質(zhì)量的同時(shí)提高系統(tǒng)性能。本文的主要研究工作如下： 首先,云環(huán)境下的應(yīng)用存在一定數(shù)量的輕量級(jí)任務(wù)。將細(xì)粒度任務(wù)請(qǐng)求配置到高計(jì)算性能的資源池會(huì)增加系統(tǒng)整體的等待時(shí)間和周轉(zhuǎn)時(shí)間。大量的細(xì)粒度任務(wù)將會(huì)花費(fèi)很多時(shí)間在任務(wù)調(diào)度和傳輸上。此外,細(xì)粒度任務(wù)分配至高計(jì)算性能的資源節(jié)點(diǎn)會(huì)明顯的降低資源利用率。針對(duì)該云場(chǎng)景,基于資源優(yōu)先級(jí)的動(dòng)態(tài)資源調(diào)度策略并將細(xì)粒度任務(wù)以分組方式整合后再執(zhí)行處理。同時(shí)綜合考慮資源的帶寬狀況,對(duì)調(diào)度策略加以?xún)?yōu)化。 其次,當(dāng)前的云資源調(diào)度策略在資源節(jié)點(diǎn)的負(fù)載均衡上并不理想,易出現(xiàn)節(jié)點(diǎn)間的負(fù)載不均衡�，F(xiàn)存的調(diào)度算法大多數(shù)并未考慮用戶(hù)群體的差異,致使VIP用戶(hù)并不能獲得更優(yōu)質(zhì)的服務(wù)。為了解決上述云系統(tǒng)瓶頸,以Min-Min調(diào)度算法作為基礎(chǔ)進(jìn)行研究分析。該算法的復(fù)雜度較低易實(shí)現(xiàn),但其短板在于資源負(fù)載不均。因此針對(duì)此現(xiàn)象,首先對(duì)經(jīng)典Min-Min調(diào)度算法加以進(jìn)化,使其負(fù)載均勻分布并保證VIP級(jí)服務(wù)質(zhì)量。 最后,對(duì)論文的研究?jī)?nèi)容進(jìn)行總結(jié)陳述。整理陳述本文的研究成果,并以對(duì)資源調(diào)度技術(shù)的系統(tǒng)學(xué)習(xí)作為基礎(chǔ),展望未來(lái)的研究方向。
[Abstract]:Cloud computing is a new computing technology with great potential value at present. It uses large-scale hardware and virtual resources to provide users with dynamic application services. In order to maximize the use of cloud resources, full play the maximum potential of cloud computing, mining efficient resource scheduling strategy is our urgent task. Resource scheduling strategy is responsible. Select the appropriate resources in the heterogeneous cloud resource pool to perform the task request processing. In making the resource scheduling strategy, the actual scene of the cloud computing should be fully considered in order to find the optimal scheduling scheme.
In this paper, we study and analyze the existing scheduling algorithms through learning the technology of cloud computing resource scheduling. Then we mainly optimize the cloud resource scheduling strategy under the two scenarios, aiming at improving the quality of service and improving the performance of the system.
First, there are a certain number of lightweight tasks for applications in the cloud environment. Configuring fine grained task requests to a high computing resource pool will increase the overall waiting time and turnover time of the system. A large number of fine-grained tasks will spend a lot of time on task scheduling and transmission. In addition, fine-grained tasks are allocated to high computing performance. The resource nodes will obviously reduce the resource utilization rate. In this cloud scenario, the dynamic resource scheduling strategy based on resource priority is implemented and the fine-grained tasks are integrated after grouping, and the bandwidth status of the resources is taken into consideration, and the scheduling strategy is optimized.
Secondly, the current scheduling strategy of cloud resource scheduling is not ideal in the load balancing of resource nodes, and the load imbalance between nodes is easy to occur. Most of the existing scheduling algorithms do not take into account the difference between the user groups and cause the VIP users not to get better service. In order to solve the bottleneck of the above cloud system, the Min-Min scheduling algorithm is used as the base. Based on the research and analysis, the complexity of the algorithm is low and easy to be realized, but its short board lies in the uneven resource load. Therefore, the classic Min-Min scheduling algorithm is first evolved to make the load evenly distributed and ensure the quality of VIP level service.
Finally, the research content of the paper is summarized and stated. The research results of this paper are stated, and the system learning of resource scheduling technology is used as the basis for the future research direction.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP393.01

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 楊習(xí)勛;;云計(jì)算及應(yīng)用前景分析[J];才智;2012年19期

2 陳全;鄧倩妮;;云計(jì)算及其關(guān)鍵技術(shù)[J];計(jì)算機(jī)應(yīng)用;2009年09期

3 錢(qián)瓊芬;李春林;張小慶;李臘元;;云數(shù)據(jù)中心虛擬資源管理研究綜述[J];計(jì)算機(jī)應(yīng)用研究;2012年07期

4 段桂英;;淺談?dòng)?jì)算機(jī)云計(jì)算[J];科技信息;2010年30期

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本文編號(hào)：1966839