本文選題：云服務(wù)　切入點：彈性　出處：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：云服務(wù)是一種基于互聯(lián)網(wǎng)的服務(wù)模式,通常包括了從服務(wù)申請、使用到支付的全部過程。云計算的吸引力就在于其計費標(biāo)準(zhǔn)是按照用戶實際使用的資源付費的,云服務(wù)有能力在任何時間靈活的增加或者減少資源以滿足用戶的需求。彈性作為云計算模式下的一個重要特征,資源的動態(tài)分配機(jī)制實現(xiàn)在需求增加時,相應(yīng)的增加資源;在需求降低時,將分配的資源回收。彈性通過按需付費的模式靈活適應(yīng)用戶波動的需求,在保證服務(wù)等級協(xié)議(SLA,Service Level Agreement)和服務(wù)質(zhì)量(Qo S,Quality of Service)的前提下使資源供應(yīng)與資源需求盡可能接近。彈性調(diào)度是實現(xiàn)云計算系統(tǒng)中動態(tài)、頻繁以及自動變更資源配置關(guān)鍵,其性能很大程度上決定了云計算系統(tǒng)提供服務(wù)的能力。傳統(tǒng)面向可擴(kuò)展性的調(diào)度因無法隨著系統(tǒng)資源需求的變化而動態(tài)伸縮資源,導(dǎo)致大量的資源浪費,以致無法滿足當(dāng)前云服務(wù)的要求。一個可以快速伸縮、精確配置的彈性調(diào)度策略將會有效的提升云計算系統(tǒng)的性能。但是目前的彈性算法主要還存在兩方面的問題:一方面體現(xiàn)在面臨大規(guī)模突發(fā)式與激增式應(yīng)用負(fù)載涌現(xiàn)時,出現(xiàn)調(diào)度時延長(傳統(tǒng)方式下虛擬機(jī)從鏡像啟動到服務(wù)可用的時間通常在5~15分鐘左右)、大量Qo S不滿足以及導(dǎo)致SLA違約等問題;另一方面是目前的彈性調(diào)度算法大多只從用戶或者服務(wù)提供者單方面進(jìn)行優(yōu)化,很少有從雙方共同的角度,以費用最低為原則,以尋求Provision-Cost平衡為目標(biāo)進(jìn)行研究。為解決上述問題,本文通過對調(diào)度算法進(jìn)行深入研究,提出了一種結(jié)合反饋與多級預(yù)測機(jī)制的敏捷彈性在線調(diào)度算法,采用預(yù)測機(jī)制提前預(yù)留資源有效避免了由于資源延遲而導(dǎo)致的服務(wù)違約問題;加入預(yù)測式補償機(jī)制,在資源分配過程中結(jié)合系統(tǒng)反饋信息,在保證預(yù)測結(jié)果準(zhǔn)確性的同時提高資源分配的合理性。文章從資源配置速度與配置精確度兩個方面出發(fā),既考慮到用戶的經(jīng)濟(jì)效益又結(jié)合服務(wù)提供者的切身利益,在保證Qo S和SLA的基礎(chǔ)上,以最小的成本實現(xiàn)最大的資源利用率,并設(shè)計罰金模型作為評測指標(biāo)進(jìn)行驗證。最后,在Cloud Stack云平臺上,部署適用于云環(huán)境下的彈性應(yīng)用,提供多種資源、多種負(fù)載驗證本文算法的有效性。
[Abstract]:Cloud service is an Internet-based service model that typically includes the entire process from service application, usage to payment. Cloud computing is attractive because its billing standards are based on the resources actually used by the user. Cloud services have the ability to flexibly increase or reduce resources at any time to meet the needs of users. Flexibility as an important feature of cloud computing mode, the dynamic resource allocation mechanism is implemented when the demand increases, the corresponding increase in resources; When demand decreases, recycle allocated resources. Flexibility adapts to fluctuating user demand through a pay-on-demand model. On the premise of guaranteeing the service level agreement (SOA) and quality of service (QoS), the resource supply and resource demand are as close as possible. Flexible scheduling is the key to realize dynamic, frequent and automatic resource configuration in cloud computing system. Its performance largely determines the ability of cloud computing systems to provide services. Traditional scalable scheduling can not scale resources dynamically with the change of system resource requirements, resulting in a large amount of waste of resources. Unable to meet the requirements of the current cloud service. The flexible scheduling strategy with precise configuration will effectively improve the performance of cloud computing systems. However, there are still two main problems in the current elastic algorithms: on the one hand, it is reflected in the emergence of large-scale burst and surge application load. The scheduling time is prolonged (in the traditional mode, the time of virtual machine starting from mirror to service is usually about 515 minutes, a large number of QoS is not satisfied and leads to SLA default and so on; On the other hand, most of the current flexible scheduling algorithms are optimized unilaterally by users or service providers, and few of them are based on the principle of minimum cost from the common point of view. In order to solve the above problems, this paper puts forward an agile elastic online scheduling algorithm combining feedback and multilevel prediction mechanism. The use of predictive mechanism to reserve resources in advance effectively avoids the problem of service default caused by the delay of resources, and adds a predictive compensation mechanism to integrate the feedback information of the system in the process of resource allocation. In order to ensure the accuracy of prediction results and improve the rationality of resource allocation, this paper considers the economic benefits of users as well as the vital interests of service providers from the two aspects of resource allocation speed and allocation accuracy. Based on the guarantee of QoS and SLA, the maximum resource utilization is realized at the minimum cost, and the fine model is designed as the evaluation index. Finally, on the Cloud Stack cloud platform, the flexible application is deployed to the cloud environment. Provide a variety of resources and loads to verify the effectiveness of the algorithm.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP393.09

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