本文選題：多租戶 + 性能模型�。� 參考：《山東大學(xué)》2017年碩士論文

【摘要】：云計(jì)算時(shí)代的多租戶SaaS(SoftwareasaService)應(yīng)用的需求越來越大。多租戶數(shù)據(jù)管理是SaaS應(yīng)用快速開發(fā)和高效運(yùn)行的關(guān)鍵。多租戶數(shù)據(jù)管理通過共享資源節(jié)省了租戶數(shù)據(jù)管理與維護(hù)成本,但從租戶的角度來說,每個(gè)租戶會(huì)與供應(yīng)商簽訂服務(wù)水平協(xié)議(Service Level Agreement,SLA)。在本文中專注于與SLA相關(guān)聯(lián)的性能指標(biāo)-查詢的響應(yīng)時(shí)間,因此多租戶數(shù)據(jù)查詢?nèi)孕铦M足租戶的服務(wù)水平目標(biāo)(Service Level Objectives,SLO),即描述各方設(shè)定的基準(zhǔn)或目標(biāo),涉及供應(yīng)商在給定時(shí)期內(nèi)為租戶提供的服務(wù)。隨著云計(jì)算時(shí)代多租戶SaaS應(yīng)用的推廣,越來越多的供應(yīng)商采用這種方式為租戶提供服務(wù)。雖然多租戶數(shù)據(jù)管理技術(shù)通過把租戶整合在相同的節(jié)點(diǎn)上節(jié)約了資源和成本,但是租戶之間資源共享也帶來了一系列的問題和挑戰(zhàn)。首先,為了提高系統(tǒng)的可用性和容錯(cuò)性,租戶一般具有多個(gè)副本。不同節(jié)點(diǎn)上放置的租戶副本是不一樣的,而租戶因?yàn)闃I(yè)務(wù)性質(zhì)的不同,對(duì)資源的需求程度也會(huì)不一樣。有的租戶對(duì)CPU資源需求較多,對(duì)I/O資源需求較少,有的租戶則剛好相反。如果把消耗CPU資源較多,I/O資源較少的租戶查詢調(diào)度到同一個(gè)節(jié)點(diǎn)上,就會(huì)造成該節(jié)點(diǎn)CPU資源利用率過高,不能滿足租戶的資源需求,而I/O資源利用率過低。因此,不合理的查詢調(diào)度會(huì)導(dǎo)致資源的浪費(fèi),降低系統(tǒng)的性能。然后,多租戶數(shù)據(jù)訪問負(fù)載具有混合性,大波動(dòng),多變化等特征。因?yàn)樽鈶舴胖迷谝黄?共享資源,所以租戶之間的性能是相互影響的。租戶叢發(fā)型的工作負(fù)載會(huì)造成節(jié)點(diǎn)的資源利用率過高,處于過載狀態(tài),也會(huì)使節(jié)點(diǎn)上的其他租戶得資源需求得不到滿足,查響應(yīng)時(shí)間過長(zhǎng),無法滿足租戶的SLO,產(chǎn)生性能危機(jī)。因此,如何平衡節(jié)點(diǎn)之間的資源使用,提高節(jié)點(diǎn)的資源利用率,消除節(jié)點(diǎn)的性能危機(jī)成為供應(yīng)商越來越關(guān)注的問題。本文從用戶實(shí)際存在的需求出發(fā),針對(duì)現(xiàn)有工作存在的不足,對(duì)多租戶環(huán)境中的過載問題進(jìn)行了一系列的研究,并提出了資源動(dòng)態(tài)平衡機(jī)制平衡數(shù)據(jù)節(jié)點(diǎn)的資源使用和消除節(jié)點(diǎn)的性能危機(jī)。本文的具體工作和貢獻(xiàn)概括如下:1.提出了三種負(fù)載模型并詳細(xì)介紹了這三種負(fù)載模型的構(gòu)建過程。本文分別定義了三種負(fù)載模型:查詢負(fù)載模型、租戶負(fù)載模型和節(jié)點(diǎn)負(fù)載模型來分別表示查詢、租戶和節(jié)點(diǎn)的負(fù)載。首先根據(jù)查詢的特點(diǎn)構(gòu)建查詢的負(fù)載模型,以其所消耗的服務(wù)器資源作為服務(wù)器總資源的百分比作為負(fù)載模型的基準(zhǔn)。然后基于查詢的負(fù)載模型采用線性累加方法構(gòu)建租戶負(fù)載模型和節(jié)點(diǎn)負(fù)載模型。最后通過實(shí)驗(yàn)驗(yàn)證了負(fù)載模型的準(zhǔn)確性。2.針對(duì)多租戶環(huán)境中不合理的查詢調(diào)度策略造成數(shù)據(jù)節(jié)點(diǎn)資源使用不平衡問題,本文提出了基于負(fù)載模型和節(jié)點(diǎn)性能的動(dòng)態(tài)查詢調(diào)度策略。首先節(jié)點(diǎn)的性能與節(jié)點(diǎn)的資源消耗水平密切相關(guān),為了實(shí)時(shí)監(jiān)測(cè)節(jié)點(diǎn)的性能,本文基于節(jié)點(diǎn)中的資源使用率,訓(xùn)練節(jié)點(diǎn)的性能標(biāo)簽來標(biāo)記節(jié)點(diǎn)的資源消耗水平。然后采用動(dòng)態(tài)查詢調(diào)度策略實(shí)時(shí)檢測(cè)和統(tǒng)計(jì)所有數(shù)據(jù)節(jié)點(diǎn)的性能標(biāo)簽,并基于查詢負(fù)載模型和節(jié)點(diǎn)的性能標(biāo)簽,動(dòng)態(tài)的分配查詢,在租戶副本所在的數(shù)據(jù)節(jié)點(diǎn)上選擇合適的節(jié)點(diǎn)執(zhí)行,平衡數(shù)據(jù)節(jié)點(diǎn)之間的資源使用,提高節(jié)點(diǎn)資源利用率。3.針對(duì)多租戶環(huán)境中由于租戶叢發(fā)型的工作負(fù)載造成性能危機(jī)的問題,本文提出了一種輕量級(jí)的消除節(jié)點(diǎn)性能危機(jī)的負(fù)載均衡機(jī)制。在多租戶數(shù)據(jù)庫環(huán)境中,節(jié)點(diǎn)產(chǎn)生性能危機(jī),會(huì)使查詢的響應(yīng)時(shí)間過長(zhǎng),租戶的SLO違反率過高。本文提出一種通過交換租戶的主副本和輔助副本的角色消除性能危機(jī)的輕量級(jí)的負(fù)載均衡機(jī)制。消除性能危機(jī)的原理是租戶具有一個(gè)主副本和多個(gè)輔助副本,主副本和輔助副本都可以承擔(dān)只讀查詢,但是寫查詢只能在主副本上。因此主副本承擔(dān)的工作負(fù)載要比輔助副本多。如果某個(gè)節(jié)點(diǎn)負(fù)載過高,把節(jié)點(diǎn)上租戶的主副本與其他節(jié)點(diǎn)上該租戶的輔助副本交換角色,就可以把在該節(jié)點(diǎn)上的查詢轉(zhuǎn)移到其他節(jié)點(diǎn)上,節(jié)點(diǎn)的負(fù)載降低,性能危機(jī)消除。本文通過實(shí)驗(yàn)驗(yàn)證了資源動(dòng)態(tài)平衡機(jī)制的有效性,資源動(dòng)態(tài)平衡機(jī)制能夠平衡數(shù)據(jù)節(jié)點(diǎn)的資源使用并快速有效的消除節(jié)點(diǎn)產(chǎn)生的性能危機(jī)。
[Abstract]:The demand for multi tenant SaaS (SoftwareasaService) applications in the cloud computing era is increasing. Multi tenant data management is the key to rapid development and efficient operation of SaaS applications. Multi tenant data management saves tenant data management and maintenance costs by sharing resources, but from the tenant's point of view, each tenant will sign the service with the supplier. Service Level Agreement (SLA). In this article, it is focused on the performance index associated with SLA - the response time of the query, so the multi tenant data query still needs to meet the tenant's service level target (Service Level Objectives, SLO), which describes the benchmarks or targets set by the parties, involving the supplier for the tenant in a given period. With the promotion of multi tenant SaaS applications in the cloud computing era, more and more vendors have used this approach to provide services to tenants. Although multi tenant data management techniques have saved resources and costs by integrating tenants on the same nodes, the sharing of resources among tenants also brings a series of problems and challenges. First, in order to improve the availability and fault tolerance of the system, the tenant usually has multiple copies. The tenant replicas on different nodes are different, and the tenants need different degree of demand for the resources because of the different business nature. Some tenants need more CPU resources, less I/O resources, and some tenants just the opposite. If the user with more CPU resources and less I/O resources is dispatched to the same node, the resource utilization rate of the node will be too high to satisfy the resource requirement of the tenant, but the utilization rate of the I/O resource is too low. Therefore, the unreasonable query scheduling will lead to the waste of the resources and reduce the performance of the system. Then, the multi tenant data will be reduced. The performance of the tenants is interacted with each other. The tenant - hairstyle workload will cause the resource utilization of the node to be too high and overloaded, and the other tenants on the node will not be satisfied with the resource requirements. When the response time is too long, it is unable to meet the SLO of the tenants and produce a performance crisis. Therefore, how to balance the use of resources between nodes, improve the utilization of the nodes and eliminate the performance crisis of the nodes has become a problem that the suppliers pay more and more attention to. A series of research on the problem of overload in the household environment is carried out, and the resource dynamic balance mechanism is proposed to balance the use of data nodes and the performance crisis of eliminating nodes. The specific work and contributions of this paper are summarized as follows: 1. three load models are proposed and the construction process of the three load models is introduced in detail. Three load models are defined: the query load model, the tenant load model and the node load model to represent the load of the query, tenant and node respectively. First, the load model of the query is built according to the characteristics of the query, and the server resource is used as the base of the load model. The model of the load model and the node load model are constructed by linear addition method. Finally, the experiment verifies the accuracy of the load model.2. for the unbalance of data node resources using the unreasonable query scheduling strategy in the multi tenant environment. This paper proposes a dynamic load model based on the load model and the performance of the node. The performance of the node is closely related to the resource consumption level of the node. In order to monitor the performance of the node in real time, this paper trains the node's resource consumption based on the resource usage in the node, and then uses the dynamic query adjustment strategy to detect and count all data nodes in real time. Performance labels, based on query load model and node performance label, dynamically allocate queries, select appropriate node execution on the data nodes located in the tenant replica, balance the use of resources between data nodes, improve the utilization of node resources, and cause the performance of.3. in the multi tenant environment due to the tenant bushes' work load. In this paper, a lightweight load balancing mechanism for eliminating node performance crisis is proposed in this paper. In the multi tenant database environment, the node produces a performance crisis, which makes the response time of the query too long and the SLO violation rate is too high. This paper proposes a role of exchanging the main copy and auxiliary copy of the tenant to eliminate the performance danger. A lightweight load balancing mechanism for a machine. The principle of eliminating the performance crisis is that the tenant has a master copy and multiple auxiliary replicas, the master copy and the auxiliary copy can bear a read-only query, but the write query can only be on the master copy. So the master replica takes more work load than the auxiliary copy. If a node is overloaded, the The main copy of the tenant on the node is exchanged with the auxiliary copy of the tenant on other nodes, and the query on the node can be transferred to other nodes. The load of the node is reduced and the performance crisis is eliminated. This paper validates the effectiveness of the dynamic balance mechanism of the resource, and the dynamic balance mechanism of the resource can balance the data node. The use of resources and quickly and effectively eliminate the node performance crisis.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP393.09

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本文編號(hào)：1985590