本文關(guān)鍵詞：服務(wù)庫(kù)多級(jí)索引模型性能分析　出處：《江蘇大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： Web服務(wù) 服務(wù)組合 服務(wù)發(fā)現(xiàn) 服務(wù)存儲(chǔ) 服務(wù)管理

【摘要】：面向服務(wù)的計(jì)算(Service-Oriented Computing,SOC)為企業(yè)軟件集成和分布式系統(tǒng)提供了一種靈活的解決方案。面向服務(wù)的體系架構(gòu)(Service-Oriented Architecture,SOA)是面向服務(wù)計(jì)算的基礎(chǔ)設(shè)施,其基本的要素是Web服務(wù)。在最近幾年,Web服務(wù)的數(shù)量急劇增加,同時(shí)快速發(fā)展的云計(jì)算也促進(jìn)了服務(wù)的發(fā)展,大量的服務(wù)匯集于以云平臺(tái)為中心的服務(wù)庫(kù)。服務(wù)發(fā)現(xiàn)和服務(wù)組合是當(dāng)前服務(wù)計(jì)算領(lǐng)域的兩個(gè)重要研究方向。因此,如何高效地管理一個(gè)大規(guī)模的服務(wù)庫(kù)并能有效促進(jìn)服務(wù)發(fā)現(xiàn)和服務(wù)組合將是一個(gè)非常重要且具有挑戰(zhàn)性的問題。本文首先從服務(wù)的研究背景及意義入手,研究與介紹了服務(wù)發(fā)現(xiàn)和服務(wù)組合的研究現(xiàn)狀,然后在此基礎(chǔ)上進(jìn)一步研究了服務(wù)索引結(jié)構(gòu),對(duì)倒排索引以及多級(jí)索引模型的三種部署模型進(jìn)行了較為詳細(xì)的研究,具體的研究?jī)?nèi)容包括:(1)目前大規(guī)模服務(wù)庫(kù)主要是以倒排索引的結(jié)構(gòu)進(jìn)行存儲(chǔ),雖然倒排索引可以在很大程度上縮小服務(wù)發(fā)現(xiàn)和組合的查找空間,但仍然存在大量的信息冗余,導(dǎo)致效率低下。針對(duì)倒排索引信息冗余問題,多級(jí)索引模型以及其三種部署模型的存儲(chǔ)結(jié)構(gòu)在最大程度上消除了信息冗余問題,并能有效促進(jìn)服務(wù)發(fā)現(xiàn)和服務(wù)組合效率。但針對(duì)特定的服務(wù)庫(kù),多級(jí)索引模型并沒有給出一個(gè)用來(lái)選取其效率最高的索引模型的有效標(biāo)準(zhǔn),針對(duì)這個(gè)問題,本文首先對(duì)不同索引模型結(jié)構(gòu)進(jìn)行了性能分析,并根據(jù)服務(wù)庫(kù)以及服務(wù)的特點(diǎn)給出了期望函數(shù),作為服務(wù)庫(kù)選取高效服務(wù)索引模型的依據(jù)。然后給出了詳細(xì)的五種索引模型的期望函數(shù)的公式推導(dǎo)過程,并通過優(yōu)化部署方案分析了各個(gè)索引模型的特點(diǎn)以及其適用的服務(wù)庫(kù)的特點(diǎn)等。最后,實(shí)驗(yàn)結(jié)果驗(yàn)證了所提優(yōu)化部署方案以及期望函數(shù)的準(zhǔn)確性和可靠性。(2)針對(duì)多級(jí)索引模型中的鍵值優(yōu)化選擇算法進(jìn)行了初步探討。首先,對(duì)多級(jí)索引模型中的鍵值選取原理進(jìn)行了深入的分析與介紹,并通過多種簡(jiǎn)單示例幫助理解。然后分析了鍵值選取中存在的問題。最后,通過實(shí)驗(yàn)對(duì)比鍵值優(yōu)化與非優(yōu)化狀態(tài)下索引結(jié)構(gòu)的檢索性能,說明了鍵值在隨機(jī)選取情況下并沒有對(duì)索引結(jié)構(gòu)的檢索性能造成任何影響,證明了鍵值優(yōu)化選取中存在的問題,也證實(shí)了對(duì)鍵值優(yōu)化選擇問題的初步探討的正確性。
[Abstract]:Service-Oriented Computing. Service-Oriented Architecture provides a flexible solution for enterprise software integration and distributed systems. SOA) is the infrastructure of service-oriented computing, whose basic element is Web services. In recent years, the number of web services has increased dramatically, and the rapid development of cloud computing has also promoted the development of services. A large number of services are gathered in the cloud platform-centric service library. Service discovery and service composition are two important research directions in the field of service computing. How to efficiently manage a large-scale service library and effectively promote service discovery and service composition will be a very important and challenging issue. This paper starts with the research background and significance of service. This paper studies and introduces the research status of service discovery and service composition, and then further studies the service index structure. In this paper, three deployment models of inverted index and multilevel index model are studied in detail. The specific research contents include: 1) at present, the large scale service library is mainly stored in the structure of inverted index. Although inverted index can reduce the search space of service discovery and composition to a large extent, there is still a lot of information redundancy, which leads to inefficiency. Multi-level index model and its storage structure of three deployment models can eliminate the problem of information redundancy to the maximum extent, and can effectively promote the efficiency of service discovery and service composition. However, the storage structure of the multi-level index model and its three deployment models can effectively promote the efficiency of service discovery and service composition. The multilevel index model does not give an effective standard to select the most efficient index model. In order to solve this problem, this paper first analyzes the performance of different index models. According to the characteristics of the service library and the characteristics of the service, the expected function is given as the basis for the service library to select the efficient service index model. Then, the derivation process of the expectation function of the five index models is given in detail. And through the optimization of deployment scheme analysis of the characteristics of each index model and its applicable service library characteristics and so on. Finally. The experimental results verify the proposed optimal deployment scheme and the accuracy and reliability of the expected function. The selection principle of key and value in multilevel index model is deeply analyzed and introduced, and some simple examples are used to help us understand. Then, the problems in key value selection are analyzed. Finally. By comparing the retrieval performance of the index structure in the state of optimization and non-optimization, it is shown that the key value has no effect on the retrieval performance of the index structure in the case of random selection. The problems in the selection of key and value are proved, and the correctness of the preliminary discussion on the selection of key and value is also confirmed.
【學(xué)位授予單位】：江蘇大學(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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