本文選題：云計(jì)算 + 可信服務(wù)　； 參考：《南京航空航天大學(xué)》2016年博士論文

【摘要】：面向服務(wù)的計(jì)算模式能夠?qū)⒐δ軉我坏姆⻊?wù)進(jìn)行組合,形成新的增值服務(wù)來滿足用戶的復(fù)雜需求。隨著云計(jì)算的普及,服務(wù)組合也面臨新的問題。開放動(dòng)態(tài)的云環(huán)境給服務(wù)的評(píng)估帶來了更多的不確定性,而海量的云服務(wù)也對(duì)現(xiàn)有服務(wù)組合技術(shù)的有效性和實(shí)時(shí)性提出了新的挑戰(zhàn)。隨著云服務(wù)的多樣化和規(guī)模化,如何從眾多的候選服務(wù)中快速選擇出滿足用戶要求的服務(wù)進(jìn)行組合是目前服務(wù)計(jì)算領(lǐng)域的研究熱點(diǎn)之一。針對(duì)上述問題,本文在分析現(xiàn)有服務(wù)評(píng)估和組合技術(shù)的基礎(chǔ)上,從云服務(wù)的可信評(píng)估、提高服務(wù)組合效率、以及復(fù)雜需求下的服務(wù)組合等方面開展了系統(tǒng)而深入的研究。本文的主要?jiǎng)?chuàng)新工作概括如下:(1)提出了一種基于一致性強(qiáng)度的云服務(wù)可信評(píng)估方法。在云服務(wù)評(píng)估框架下為基礎(chǔ)設(shè)施服務(wù)和應(yīng)用服務(wù)分別設(shè)計(jì)了可信評(píng)估指標(biāo);并提出了基于一致性強(qiáng)度的模糊評(píng)估方法,通過引入語義折扣因子和一致性強(qiáng)度,可從模糊的服務(wù)評(píng)價(jià)信息中合理地分析出確定的評(píng)估值,從而解決了不確定環(huán)境下云服務(wù)的可信評(píng)估問題,并在仿真實(shí)驗(yàn)平臺(tái)NetLogo上驗(yàn)證了所提出方法的實(shí)用性和有效性。(2)提出了一種基于人工蜂群算法的可信服務(wù)組合方法。在服務(wù)組合模型中引入時(shí)間衰減函數(shù),提高了近期評(píng)分值的時(shí)間權(quán)重,從而使服務(wù)質(zhì)量更符合當(dāng)前時(shí)刻服務(wù)的特征;將服務(wù)組合問題進(jìn)行非線性整數(shù)規(guī)劃建模,提出了全局指導(dǎo)人工蜂群(DGABC)算法來求解該模型,通過蜂群對(duì)食物源的探索來實(shí)現(xiàn)對(duì)最佳服務(wù)組合方案的搜索。實(shí)驗(yàn)結(jié)果表明在大量服務(wù)信息下,本文提出的DGABC算法可以在保證服務(wù)質(zhì)量的同時(shí)提高服務(wù)組合的效率。(3)提出了一種基于成本效益優(yōu)化的多目標(biāo)服務(wù)組合方法。根據(jù)用戶對(duì)服務(wù)組合的多樣化需求,以最大化服務(wù)質(zhì)量、最小化成本為優(yōu)化目標(biāo),將服務(wù)組合問題建模為多目標(biāo)整數(shù)規(guī)劃模型;提出了基于精英指導(dǎo)的多目標(biāo)人工蜂群算法(EMOABC),在原始人工蜂群算法中加入快速非支配排序、種群選擇、精英指導(dǎo)離散解生成、以及多目標(biāo)適應(yīng)度計(jì)算等多目標(biāo)優(yōu)化策略,實(shí)現(xiàn)對(duì)該模型的求解。相較于同類算法,EMOABC算法無論在運(yùn)行效率還是求解質(zhì)量上均具有明顯優(yōu)勢(shì),從而驗(yàn)證了本文提出的方法可較好地解決復(fù)雜需求下的云服務(wù)組合問題。(4)提出了一種基于skyline計(jì)算的非線性服務(wù)組合方法。通過skyline計(jì)算篩選掉每個(gè)服務(wù)群中的冗余服務(wù),可以減少搜索空間,有效提高服務(wù)組合的效率;進(jìn)而采用建模語言AMPL將服務(wù)組合問題建模為0-1非線性規(guī)劃問題,并利用求解器Bonmin對(duì)所建立的模型進(jìn)行求解。實(shí)驗(yàn)結(jié)果表明本文提出的基于skyline計(jì)算的服務(wù)組合方法在保證服務(wù)質(zhì)量的同時(shí)可顯著提高服務(wù)組合的效率。
[Abstract]:The service-oriented computing model can combine the services with a single function to form a new value-added service to meet the complex needs of users. With the popularity of cloud computing, service composition also faces new problems. The open and dynamic cloud environment brings more uncertainty to the evaluation of services, and the massive cloud services also pose a new challenge to the effectiveness and real-time performance of the existing service composition technology. With the diversification and scale of cloud services, it is one of the research hotspots in the field of service computing that how to quickly select the services that meet the needs of users from many candidate services. In order to solve the above problems, based on the analysis of the existing service evaluation and composition techniques, this paper has carried out a systematic and in-depth study on the trusted evaluation of cloud services, improving the efficiency of service composition, and service composition under complex requirements. The main innovation work of this paper is summarized as follows: (1) A cloud service trust evaluation method based on consistency strength is proposed. In the framework of cloud service evaluation, the trusted evaluation indexes for infrastructure services and application services are designed, and a fuzzy evaluation method based on consistency strength is proposed, which introduces semantic discount factor and consistency strength. We can reasonably analyze the definite evaluation value from the fuzzy service evaluation information, and solve the problem of cloud service credible evaluation in uncertain environment. The practicability and validity of the proposed method are verified on the simulation platform NetLogo) and a trusted service composition method based on artificial bee colony algorithm is proposed. The time attenuation function is introduced into the service composition model, which improves the time weight of the recent scoring value, thus making the quality of service more consistent with the characteristics of the service at present time, and the service composition problem is modeled by nonlinear integer programming. A global directed artificial bee colony (DGABC) algorithm is proposed to solve the model, and the best service composition scheme is searched by the colony searching for food source. The experimental results show that the proposed DGABC algorithm can improve the efficiency of service composition while ensuring the quality of service under a large amount of service information. A multi-objective service composition method based on cost-benefit optimization is proposed. In order to maximize the quality of service and minimize the cost, the service composition problem is modeled as a multi-objective integer programming model. This paper presents a multi-objective artificial bee colony algorithm based on elitist guidance, which includes fast undominated sorting, population selection, elitist directed discrete solution generation and multi-objective fitness calculation. The solution of the model is realized. Compared with the similar algorithm, EMOABC algorithm has obvious advantages in terms of running efficiency and solving quality. It is verified that the proposed method can solve the cloud service composition problem with complex requirements. (4) A nonlinear service composition method based on skyline computation is proposed. The redundant services in each service group can be filtered by skyline calculation, the search space can be reduced and the efficiency of service composition can be improved, and then the service composition problem can be modeled as a 0-1 nonlinear programming problem by using the modeling language AMPL. The model is solved by the solver Bonmin. The experimental results show that the proposed service composition method based on skyline computing can significantly improve the efficiency of service composition while ensuring the quality of service.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP393.09

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