本文選題：服務(wù)組合優(yōu)化 + QoS��； 參考：《沈陽理工大學(xué)》2017年碩士論文

【摘要】：互聯(lián)網(wǎng)技術(shù)的不斷發(fā)展,使人們越來越重視對Web服務(wù)的研究以及應(yīng)用。然而,現(xiàn)在Web服務(wù)因具有松散耦合、高度可集成、跨平臺性等特性不斷發(fā)展的同時,擁有相同或者相似功能的服務(wù)數(shù)量卻不斷增多。這些Web服務(wù)由于粒度較小、功能簡單無法滿足用戶復(fù)雜需求,所以對Web服務(wù)進(jìn)行組合優(yōu)化顯得尤為重要。本文首先對當(dāng)前服務(wù)描述模型的優(yōu)缺點進(jìn)行研究,提出QoS描述模型。單個QoS模型包括服務(wù)成本、執(zhí)行時間、可用性、安全性、信譽(yù)度五個屬性。在對單個QoS屬性進(jìn)行量化的基礎(chǔ)上,引入權(quán)重的概念,對QoS進(jìn)行綜合評價。服務(wù)組合優(yōu)化的QoS計算模型是在單個QoS綜合評價的基礎(chǔ)上,根據(jù)服務(wù)組合優(yōu)化的流程結(jié)構(gòu),對服務(wù)的QoS屬性進(jìn)行計算。其次,為了提高服務(wù)組合優(yōu)化的效率和準(zhǔn)確性,對標(biāo)準(zhǔn)粒子群算法的慣性權(quán)重和學(xué)習(xí)因子進(jìn)行改進(jìn),提出一種線性和非線性結(jié)合的慣性權(quán)重遞減策略,并且結(jié)合改進(jìn)的三角函數(shù)變化的學(xué)習(xí)因子,有效的提高了粒子群算法的收斂性。將改進(jìn)的粒子群算法應(yīng)用到服務(wù)組合優(yōu)化中,提高了服務(wù)組合優(yōu)化的效率。最后,研究模糊Petri網(wǎng)的相關(guān)知識,把基于改進(jìn)粒子群的服務(wù)組合優(yōu)化算法與模糊Petri網(wǎng)相結(jié)合。構(gòu)建服務(wù)實例,生成服務(wù)依賴關(guān)系生成圖,用庫所表示一類的抽象服務(wù),每個庫所對應(yīng)一定數(shù)量的具體服務(wù),對這些服務(wù)進(jìn)行服務(wù)組合優(yōu)化,選擇出一組滿足需求的服務(wù),并且開發(fā)一個基于模糊Petri網(wǎng)的服務(wù)組合優(yōu)化的系統(tǒng)。
[Abstract]:With the development of Internet technology, people pay more and more attention to the research and application of Web services. However, with the development of loosely coupled, highly integrated and cross-platform Web services, the number of services with the same or similar functions is increasing. Because of their small granularity and simple functions, these Web services can not meet the complex needs of users, so it is very important to optimize the composition of Web services. In this paper, the advantages and disadvantages of the current service description model are studied, and the QoS description model is proposed. A single QoS model includes five attributes: service cost, execution time, availability, security, and reputation. Based on the quantization of single QoS attribute, the concept of weight is introduced to evaluate QoS synthetically. The QoS computing model of service composition optimization is based on the comprehensive evaluation of a single QoS, according to the process structure of service composition optimization, the QoS attribute of the service is calculated. Secondly, in order to improve the efficiency and accuracy of service composition optimization, the inertia weight and learning factor of standard particle swarm optimization are improved, and a linear and nonlinear inertia weight decreasing strategy is proposed. Combined with the improved trigonometric function change learning factor, the convergence of PSO is improved effectively. The improved particle swarm optimization algorithm is applied to service composition optimization to improve the efficiency of service composition optimization. Finally, the related knowledge of fuzzy Petri nets is studied, and the service composition optimization algorithm based on improved particle swarm optimization is combined with fuzzy Petri nets. The service instance is constructed, the service dependency graph is generated, the abstract services are represented by the library, each library corresponds to a certain number of specific services, and optimizes the service composition of these services, and selects a set of services to meet the requirements. A service composition optimization system based on fuzzy Petri net is developed.
【學(xué)位授予單位】：沈陽理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP393.09;TP301.1

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