本文選題：地學(xué)數(shù)據(jù)　切入點(diǎn)：服務(wù)組合　出處：《中國(guó)礦業(yè)大學(xué)(北京)》2017年博士論文

【摘要】：網(wǎng)絡(luò)技術(shù)促進(jìn)地球探測(cè)信息技術(shù)不斷向著網(wǎng)絡(luò)服務(wù)的方向發(fā)展,而云計(jì)算技術(shù)更加推動(dòng)空間數(shù)據(jù)共享和交互操作向著應(yīng)用服務(wù)的方向發(fā)展。隨著地球探測(cè)過(guò)程中越來(lái)越復(fù)雜的數(shù)據(jù)采集、數(shù)據(jù)處理和數(shù)據(jù)解釋等業(yè)務(wù)過(guò)程,網(wǎng)絡(luò)服務(wù)技術(shù)中單個(gè)的原子服務(wù)提供的業(yè)務(wù)流程處理功能有限,無(wú)法滿足用戶的需求,需要通過(guò)各種分布式個(gè)體服務(wù)組合起來(lái)才能實(shí)現(xiàn)高質(zhì)量和復(fù)雜的地學(xué)業(yè)務(wù)功能。為了確保地學(xué)數(shù)據(jù)應(yīng)用系統(tǒng)運(yùn)行的高效性,提前發(fā)現(xiàn)應(yīng)用系統(tǒng)中潛在的錯(cuò)誤以減少系統(tǒng)重新部署需要的成本等,滿足復(fù)雜的業(yè)務(wù)功能需求和有效部署在云計(jì)算平臺(tái)中,地學(xué)數(shù)據(jù)服務(wù)的建模分析顯得尤為重要和日益迫切,地學(xué)數(shù)據(jù)服務(wù)組合的模型被提出作為地學(xué)信息服務(wù)的研究基礎(chǔ)�；诳臻g數(shù)據(jù)的特征和網(wǎng)絡(luò)服務(wù)技術(shù)基礎(chǔ)上,研究首先構(gòu)建地學(xué)數(shù)據(jù)服務(wù)模型,提出基于Petri網(wǎng)的地學(xué)數(shù)據(jù)服務(wù)組合模型,完成地學(xué)數(shù)據(jù)服務(wù)組合模型的正確性驗(yàn)證分析,然后完整描述地學(xué)數(shù)據(jù)服務(wù)組合網(wǎng)絡(luò)應(yīng)用系統(tǒng)的建模分析和系統(tǒng)實(shí)現(xiàn),最后在地學(xué)數(shù)據(jù)服務(wù)組合模型的基礎(chǔ)上,將地學(xué)數(shù)據(jù)服務(wù)組合模型部署到云計(jì)算平臺(tái)中,開展云計(jì)算平臺(tái)中地學(xué)數(shù)據(jù)服務(wù)組合的部署策略研究和優(yōu)化研究。研究的主要成果包括如下五個(gè)部分:1.提出了基于Petri網(wǎng)的地學(xué)數(shù)據(jù)服務(wù)組合模型,并從可達(dá)性,死鎖性,有界性和優(yōu)化性方面驗(yàn)證模型的正確性。地學(xué)數(shù)據(jù)服務(wù)的描述是基于基本的服務(wù)實(shí)體的描述基礎(chǔ)上的。地學(xué)數(shù)據(jù)服務(wù)組合的建模需要服務(wù)的描述和服務(wù)之間的關(guān)系描述,地學(xué)數(shù)據(jù)服務(wù)組合的建模過(guò)程是基于服務(wù)網(wǎng)絡(luò)建模方法和四種基本的結(jié)構(gòu)模式完成的,用于詳細(xì)表述異步和并發(fā)的復(fù)雜地學(xué)數(shù)據(jù)服務(wù)組合過(guò)程。在將服務(wù)、服務(wù)組合應(yīng)用服務(wù)網(wǎng)建模之后,服務(wù)組合模型的正確性的驗(yàn)證問(wèn)題就轉(zhuǎn)變成服務(wù)網(wǎng)的活性、有界性和死鎖性等的驗(yàn)證。基于Petri網(wǎng)的地學(xué)數(shù)據(jù)服務(wù)組合模型及其正確性驗(yàn)證,可以在地學(xué)數(shù)據(jù)服務(wù)組合模型的建模階段發(fā)現(xiàn)潛在的錯(cuò)誤,避免地學(xué)數(shù)據(jù)服務(wù)組合模型在運(yùn)行階段錯(cuò)誤執(zhí)行,能夠縮短運(yùn)行階段查找錯(cuò)誤的時(shí)間,減少重新部署所需要的成本,增加業(yè)務(wù)流程的可實(shí)現(xiàn)度,以較低的成本達(dá)到整體最優(yōu)。2.完整的描述了地學(xué)數(shù)據(jù)服務(wù)組合網(wǎng)絡(luò)應(yīng)用系統(tǒng)的建模階段和實(shí)現(xiàn)階段。三維地質(zhì)模型的切割過(guò)程是一個(gè)典型的業(yè)務(wù)過(guò)程,研究中以地質(zhì)模型切割的業(yè)務(wù)過(guò)程為例,完整的描述了地學(xué)數(shù)據(jù)服務(wù)組合網(wǎng)絡(luò)應(yīng)用系統(tǒng)的建模過(guò)程和實(shí)現(xiàn)過(guò)程。為了實(shí)現(xiàn)應(yīng)用系統(tǒng)執(zhí)行的高效性和準(zhǔn)確性,結(jié)構(gòu)的完整性是必須的。建模階段集中在地學(xué)數(shù)據(jù)服務(wù)組合的建模和分析方面,實(shí)現(xiàn)階段集中在地學(xué)數(shù)據(jù)服務(wù)組合網(wǎng)絡(luò)應(yīng)用系統(tǒng)的執(zhí)行方面。在提出的服務(wù)網(wǎng)基礎(chǔ)上,復(fù)雜的服務(wù)流程可應(yīng)用服務(wù)組合網(wǎng)的方法建模。首先對(duì)地學(xué)數(shù)據(jù)服務(wù)組合建模并且驗(yàn)證分析其正確性,然后以三維地質(zhì)模型切割為例,提出地學(xué)數(shù)據(jù)服務(wù)組合網(wǎng)絡(luò)應(yīng)用系統(tǒng)設(shè)計(jì)的通用框架,最后描述地學(xué)數(shù)據(jù)服務(wù)組合網(wǎng)絡(luò)應(yīng)用系統(tǒng)的實(shí)現(xiàn)過(guò)程。服務(wù)組合應(yīng)用于地學(xué)數(shù)據(jù)網(wǎng)絡(luò)應(yīng)用系統(tǒng)的優(yōu)勢(shì),包括成本低,效率高,易于應(yīng)用,靈活性,可復(fù)用性和易于部署等。3.研究了云計(jì)算平臺(tái)中地學(xué)數(shù)據(jù)服務(wù)組合的部署策略問(wèn)題。地學(xué)數(shù)據(jù)服務(wù)組合模型及其網(wǎng)絡(luò)應(yīng)用系統(tǒng)在構(gòu)建和實(shí)現(xiàn)之后,被部署到云計(jì)算平臺(tái)中。云計(jì)算平臺(tái)中地學(xué)數(shù)據(jù)服務(wù)組合網(wǎng)絡(luò)應(yīng)用系統(tǒng)高效運(yùn)行的核心問(wèn)題是如何選擇最優(yōu)的服務(wù)組合成復(fù)雜的服務(wù),執(zhí)行服務(wù)組合,以滿足復(fù)雜的功能需求和實(shí)現(xiàn)高服務(wù)質(zhì)量,被歸結(jié)為優(yōu)化問(wèn)題。服務(wù)組合的部署策略是影響地學(xué)數(shù)據(jù)服務(wù)組合質(zhì)量的一個(gè)很重要的因素,因此采用何種合適的部署策略是一個(gè)重要的問(wèn)題。本研究中考慮的服務(wù)質(zhì)量因素包括成本和響應(yīng)時(shí)間,首先在應(yīng)用系統(tǒng)和服務(wù)之間建立關(guān)聯(lián)模型,應(yīng)用有向非循環(huán)圖來(lái)描述部署在云計(jì)算平臺(tái)中的應(yīng)用系統(tǒng)的復(fù)雜服務(wù)之間的關(guān)聯(lián)關(guān)系,然后服務(wù)部署問(wèn)題被映射為圖的k分割優(yōu)化問(wèn)題,最后應(yīng)用兩階段方法解決圖的分割優(yōu)化問(wèn)題。一系列的實(shí)驗(yàn)驗(yàn)證了所提出的服務(wù)部署策略的可行性和有效性,所提出的服務(wù)部署策略明顯優(yōu)于改進(jìn)的貪心算法,其中改進(jìn)的貪心算法經(jīng)常用于圖的分割問(wèn)題。4.構(gòu)建云計(jì)算平臺(tái)中網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)優(yōu)化的模型。在進(jìn)一步探討云計(jì)算平臺(tái)中地學(xué)數(shù)據(jù)服務(wù)組合的優(yōu)化研究之前,需要構(gòu)建云計(jì)算平臺(tái)中的網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)優(yōu)化模型。云計(jì)算平臺(tái)中網(wǎng)絡(luò)拓?fù)涞膬?yōu)化問(wèn)題描述為,云計(jì)算系統(tǒng)中存在成千上萬(wàn)的處于不同地理位置的服務(wù)器,如何將服務(wù)器有效組織是云計(jì)算系統(tǒng)高效穩(wěn)定運(yùn)行的關(guān)鍵問(wèn)題之一,被歸結(jié)為網(wǎng)絡(luò)拓?fù)鋬?yōu)化問(wèn)題。考慮到云資源提供者和云用戶,抽象出通用的云計(jì)算平臺(tái)中網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu),提出了以成本低和路徑最短為目標(biāo)函數(shù)的網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)優(yōu)化的模型,應(yīng)用遺傳算法和模式搜索算法的混合算法優(yōu)化。遺傳算法關(guān)注全局最優(yōu)解而模式搜索更關(guān)注局部最優(yōu)解。在應(yīng)用混合算法的過(guò)程中,云計(jì)算平臺(tái)中網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)的魯棒性得到驗(yàn)證。5.探討云計(jì)算平臺(tái)中地學(xué)數(shù)據(jù)服務(wù)組合的優(yōu)化算法研究,在已有的理論方法研究基礎(chǔ)上,統(tǒng)一服務(wù)質(zhì)量QoS參數(shù),解決從服務(wù)池中選擇合適服務(wù),提出服務(wù)組合限制,確定重要服務(wù)質(zhì)量QoS,最終提出云計(jì)算平臺(tái)中地學(xué)數(shù)據(jù)服務(wù)組合優(yōu)化的算法。探討云計(jì)算平臺(tái)中地學(xué)數(shù)據(jù)服務(wù)組合的資源調(diào)度算法研究,重點(diǎn)分析服務(wù)質(zhì)量QoS要求,識(shí)別最佳工作負(fù)載-資源對(duì),為云工作負(fù)載調(diào)度合適資源。在資源供應(yīng)方面,根據(jù)云用戶的服務(wù)質(zhì)量QoS要求為給定工作負(fù)載識(shí)別足夠資源。在資源調(diào)度方面,根據(jù)資源供應(yīng)選擇的資源映射執(zhí)行云用戶工作負(fù)載。在已有的不同資源調(diào)度標(biāo)準(zhǔn)和參數(shù)下的資源調(diào)度算法研究基礎(chǔ)上,提出云計(jì)算平臺(tái)中地學(xué)數(shù)據(jù)資源調(diào)度的算法。探討云計(jì)算平臺(tái)中地學(xué)數(shù)據(jù)服務(wù)組合的負(fù)載均衡算法研究,重點(diǎn)分析云計(jì)算平臺(tái)中的負(fù)載均衡定量指標(biāo)如響應(yīng)時(shí)間,可擴(kuò)展性,資源利用率,通量,容錯(cuò)和性能等。在已有的云計(jì)算平臺(tái)中負(fù)載均衡算法的研究基礎(chǔ)上,提出云計(jì)算平臺(tái)中地學(xué)數(shù)據(jù)服務(wù)組合的負(fù)載均衡算法。
[Abstract]:Network technology to promote the development of earth exploration information technology continuously toward the direction of network services, and cloud computing technology to promote the sharing of spatial data and the development of interactive operation toward the direction of the service. With the application of data acquisition in the process of earth exploration is more and more complex, data processing and data interpretation of business process, a single web service technology in atomic service the business process function is limited, unable to meet the needs of users, through a variety of distributed individual services together to achieve high quality and complex to learn business functions. To ensure that the learning efficiency of data application system, to discover their potential applications in the system to reduce the system error re deployment needs cost. To meet the functional requirements of complex and effective deployment in the cloud computing platform, modeling data service analysis is particularly As an important and urgent, models of geo data service composition is put forward as the basic research of geological information services. Based on spatial data characteristics and network service technology on the basis of research first builds geoscience data service model, put forward Petri network learning model based on the analysis of data service composition, verify the complete study the combined data service model, implementation analysis and system modeling and complete description of the geo data services combination network application system, finally combining data service model based on the geological data, the service composition model deployed in cloud computing platform, cloud computing platform to carry out geological data service composition deployment strategy research and optimization research. The main results of the research includes five parts as follows: 1. proposed Petri network data service model based on the combination of science, and from the reachability, deadlock and boundedness The correctness of verification and optimization of the model. To describe data services is based on the description of the basic service entity. The description of the relationship between modeling combined data service needs and description of service, the modeling process of geoscience data service composition is to complete the service network modeling method and four kinds of based on the basic structure model, for detailed asynchronous and concurrent complex geo data service composition process. In the service, after the service composition application service network modeling, verified the correctness of the service composition model into service network activity, verification of circles and deadlock. The Petri network study of data service composition model and its correctness is verified based on the modeling stage can combine data services model in the discovery of potential errors, avoid data service composition model in operation Phase error, can shorten the operation time of the phase errors, reduce re deployment costs, increase the realization of business process, with low cost to achieve the optimal overall modeling stage.2. complete description of the data service composition of network application system and implementation stage. The cutting process of 3D geological model is a typical business process, business process in order to study the geological model of cutting as an example, a complete description of the modeling process of geo data services combination of network application and implementation process of the system. In order to achieve high efficiency and accuracy of application system implementation, structural integrity is a must. The modeling stage of centralized modeling and analysis science data service composition in the implementation stage of centralized data network application system in the service composition execution. In the service network based on complex Service process modeling method of application service combination network. Firstly, the geo data service composition modeling and analysis to verify its correctness, and as an example to 3D model cutting, put forward general framework of geo data services combination network application system design, implementation process and finally describe geoscience data service composition of network application system. A combination in the geo data network application system advantages, including low cost, high efficiency, easy to use, flexibility, reusability and deployment strategy can be easily deployed on the.3. cloud computing platform in geo data service composition. Geoscience data service composition model and network application system in the construction and implementation. To be deployed in cloud computing platform. Cloud computing platform in the core issue for the efficient operation of data service composition of network application system is how to select the optimal service group The synthesis of complex services, implementation of the service composition, in order to meet the functional requirements of complex and high quality of service, is formulated as an optimization problem. The service portfolio deployment strategy is a very important factor to influence quality of data service composition, so the appropriate deployment strategy is an important issue. The quality factors considered in this study include the cost and response time, first built the relation model between the application systems and services, application of directed acyclic graph to describe the relationship between the deployment of complex service computing application system platform in the cloud between the service deployment problem is then mapped to the optimization problem of graph K segmentation. The application of two stage method to solve the optimization problem of segmentation graph. A series of experiments to test the proposed service deployment strategy is feasible and effective, the proposed service deployment strategy in Ming Dynasty Improved significantly better than the greedy algorithm, the improved greedy algorithm is frequently used.4. segmentation map to build cloud computing network topology optimization model of the platform. Before optimization research in cloud computing platform to further explore the geological data service composition, need to build cloud computing optimization model of network topology description optimization platform. The problem of cloud computing platform for network topology, cloud computing in different geographic locations there are tens of thousands of server systems, how to effectively organize the server is one of the key issues of cloud computing system efficient and stable operation, has been attributed to the network topology optimization problem. Considering the cloud resource providers and cloud users, abstracts the common Cloud Calculation of the topological structure of the network platform, put forward to the low cost and the shortest path for network topology optimization objective function model, using genetic algorithm Hybrid optimization algorithm and pattern search algorithm. The genetic algorithm on the optimal solution and the pattern search is more concerned about the local optimal solution. In the process of application of the hybrid algorithm, optimization algorithm for cloud computing robust network topology in the platform is verified on.5. cloud computing platform in geo data service composition, based on the theory the existing methods of research, unified QoS quality of service parameters, select the appropriate solution service from the pool, the service composition, determine the quality of service QoS, finally put forward the cloud computing platform in the data service composition optimization algorithm. The resource scheduling of cloud computing platform in geo data service composition, key analysis of quality of service requirements of QoS, the best working load on load identification resources, scheduling appropriate resources for cloud work. In the supply of resources, according to the cloud user service quality The amount of QoS required for a given work load identification of sufficient resources. In resource scheduling, resource mapping according to the resource supply chosen to execute the cloud user workloads. Based on the research of resource scheduling algorithms in different resource scheduling and parameters under the existing standard, the cloud computing platform in the data resource scheduling algorithm. Research on load balancing algorithm in the discussion of cloud computing platform, data service composition, focus on the analysis of cloud computing platform in the load balancing quantitative indicators such as response time, scalability, resource utilization, throughput, fault tolerance and performance. In the existing cloud computing platform based on load balancing algorithm on the proposed load balancing algorithm in cloud computing platform in the data service composition.

【學(xué)位授予單位】：中國(guó)礦業(yè)大學(xué)(北京)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：P628

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 熊炎;郭頌;郭立新;;基于消息關(guān)聯(lián)關(guān)系的Web服務(wù)組合模型[J];信陽(yáng)師范學(xué)院學(xué)報(bào)(自然科學(xué)版);2013年02期

2 廖志軍;高春鳴;;基于π演算的異構(gòu)Web服務(wù)組合的互操作[J];中國(guó)科技信息;2007年06期

3 高云全;;一種動(dòng)態(tài)的Web服務(wù)組合及其實(shí)現(xiàn)[J];科技廣場(chǎng);2008年10期

4 吳劍峰;朱稷涵;張廣泉;;帶時(shí)間約束的Web服務(wù)組合建模與分析[J];蘇州大學(xué)學(xué)報(bào)(自然科學(xué)版);2012年01期

5 王玉英;陳平;;Web服務(wù)組合的層次化建模[J];西北大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年01期

6 周文勇;郭頌;張繼軍;;基于服務(wù)質(zhì)量的Web服務(wù)組合模型[J];信陽(yáng)師范學(xué)院學(xué)報(bào)(自然科學(xué)版);2013年03期

7 楊彩;劉曉霞;賈松浩;謝倩茹;;基于領(lǐng)域本體的Web服務(wù)組合的消息處理[J];西北大學(xué)學(xué)報(bào)(自然科學(xué)版);2007年04期

8 孫萍;蔣昌俊;;聚類分析及關(guān)聯(lián)挖掘在Web服務(wù)組合中的應(yīng)用研究[J];高技術(shù)通訊;2008年11期

9 王勇;代桂平;姜正濤;侯亞榮;;服務(wù)組合中信任感知的成員服務(wù)選擇算法[J];高技術(shù)通訊;2010年08期

10 郭頌;柳春華;周明林;;語(yǔ)義Web服務(wù)組合實(shí)現(xiàn)框架研究[J];信陽(yáng)師范學(xué)院學(xué)報(bào)(自然科學(xué)版);2011年04期

相關(guān)會(huì)議論文 前10條

1 段友祥;相鵬;;Web服務(wù)組合引擎的研究與實(shí)現(xiàn)[A];第二十三屆中國(guó)數(shù)據(jù)庫(kù)學(xué)術(shù)會(huì)議論文集（研究報(bào)告篇）[C];2006年

2 吳亮;袁兆山;;基于模糊Petri網(wǎng)的語(yǔ)義Web服務(wù)組合[A];全國(guó)第20屆計(jì)算機(jī)技術(shù)與應(yīng)用學(xué)術(shù)會(huì)議（CACIS·2009）暨全國(guó)第1屆安全關(guān)鍵技術(shù)與應(yīng)用學(xué)術(shù)會(huì)議論文集（上冊(cè)）[C];2009年

3 趙慧杰;;Web服務(wù)的組合計(jì)劃與優(yōu)化[A];2007年中國(guó)智能自動(dòng)化會(huì)議論文集[C];2007年

4 張玉軍;李心科;;面向自然語(yǔ)言描述的Web服務(wù)組合[A];2011中國(guó)儀器儀表與測(cè)控技術(shù)大會(huì)論文集[C];2011年

5 周相兵;馬洪江;楊興江;;一種基于云計(jì)算的語(yǔ)義Web服務(wù)組合模型研究[A];2009年全國(guó)開放式分布與并行計(jì)算機(jī)學(xué)術(shù)會(huì)議論文集(上冊(cè))[C];2009年

6 付曉東;鄒平;;基于元流程的Web服務(wù)組合例外處理模型[A];第二十六屆中國(guó)控制會(huì)議論文集[C];2007年

7 江琦;奚宏生;殷保群;;網(wǎng)絡(luò)新媒體服務(wù)系統(tǒng)事件驅(qū)動(dòng)的動(dòng)態(tài)服務(wù)組合[A];第二十九屆中國(guó)控制會(huì)議論文集[C];2010年

8 徐明迪;趙恒;張煥國(guó);;面向可靠性的動(dòng)態(tài)Web服務(wù)組合選擇策略研究[A];CCF NCSC 2011——第二屆中國(guó)計(jì)算機(jī)學(xué)會(huì)服務(wù)計(jì)算學(xué)術(shù)會(huì)議論文集[C];2011年

9 周獻(xiàn)中;吳奎;蕭毅鴻;;基于蟻群算法的Web服務(wù)自動(dòng)組合[A];決策科學(xué)與評(píng)價(jià)——中國(guó)系統(tǒng)工程學(xué)會(huì)決策科學(xué)專業(yè)委員會(huì)第八屆學(xué)術(shù)年會(huì)論文集[C];2009年

10 徐其興;余鎮(zhèn)危;;一種基于移動(dòng)Agent的服務(wù)組合模型[A];2006年全國(guó)開放式分布與并行計(jì)算學(xué)術(shù)會(huì)議論文集（二）[C];2006年

相關(guān)重要報(bào)紙文章 前5條

1 本報(bào)記者 張茜 通訊員 孟慶超;打好服務(wù)組合拳助力民企大發(fā)展[N];萊蕪日?qǐng)?bào);2014年

2 記者 俞永均 通訊員 方平原 王芬;外經(jīng)貿(mào)部門打出金融服務(wù)組合拳[N];寧波日?qǐng)?bào);2012年

3 李鵬春;為地學(xué)數(shù)據(jù)找個(gè)好“歸宿”[N];地質(zhì)勘查導(dǎo)報(bào);2007年

4 高智 本報(bào)記者 王彤;許昌保險(xiǎn)業(yè):亮出服務(wù)組合拳[N];中國(guó)保險(xiǎn)報(bào);2014年

5 沈建苗 編譯;利用現(xiàn)有服務(wù)構(gòu)建SOA應(yīng)用[N];計(jì)算機(jī)世界;2006年

相關(guān)博士學(xué)位論文 前10條

1 許娜;地學(xué)數(shù)據(jù)網(wǎng)絡(luò)服務(wù)組合模型的研究[D];中國(guó)礦業(yè)大學(xué)(北京);2017年

2 馮名正;Web服務(wù)組合關(guān)鍵技術(shù)研究[D];東南大學(xué);2006年

3 何豐;語(yǔ)義Web服務(wù)組合若干關(guān)鍵技術(shù)研究[D];東華大學(xué);2008年

4 周濤;基于策略的服務(wù)組合關(guān)鍵技術(shù)研究及應(yīng)用[D];浙江大學(xué);2012年

5 吳鐘;面向多邊協(xié)同的Web服務(wù)組合市場(chǎng)決策與優(yōu)化管理研究[D];武漢理工大學(xué);2013年

6 李琳;Web服務(wù)組合的形式化驗(yàn)證與可視化方法研究[D];武漢大學(xué);2014年

7 趙欣;面向SLA的服務(wù)組合QoS管理及優(yōu)化技術(shù)研究[D];復(fù)旦大學(xué);2014年

8 陳瑤;Web服務(wù)組合及其異常處理的關(guān)鍵技術(shù)研究[D];武漢大學(xué);2013年

9 齊連永;基于QoS的Web服務(wù)組合關(guān)鍵問(wèn)題研究[D];南京大學(xué);2011年

10 郭志云;服務(wù)計(jì)算中若干關(guān)鍵技術(shù)研究[D];北京郵電大學(xué);2015年

相關(guān)碩士學(xué)位論文 前10條

1 鄒艷妮;基于價(jià)格時(shí)間Petri網(wǎng)的Web服務(wù)組合建模與分析[D];安徽理工大學(xué);2008年

2 翟華偉;Web服務(wù)組合重配置方法研究[D];大連海事大學(xué);2007年

3 楊秀麗;基于有色Petri網(wǎng)的Web服務(wù)組合[D];吉林大學(xué);2007年

4 狄浩軍;數(shù)據(jù)和時(shí)間感知的Web服務(wù)組合形式化建模與驗(yàn)證研究[D];蘇州大學(xué);2012年

5 張金英;基于事務(wù)的Web服務(wù)組合及補(bǔ)償研究[D];陜西師范大學(xué);2013年

6 黃嘉發(fā);基于Artifact的服務(wù)協(xié)同研究與實(shí)現(xiàn)[D];華南理工大學(xué);2015年

7 石美娟;基于場(chǎng)景的Web服務(wù)組合并行測(cè)試生成的研究[D];內(nèi)蒙古大學(xué);2015年

8 林成權(quán);基于QoS的云服務(wù)組合技術(shù)研究[D];大連海事大學(xué);2015年

9 葛琨;基于JADE平臺(tái)的Web服務(wù)組合模擬系統(tǒng)研究與實(shí)現(xiàn)[D];大連海事大學(xué);2015年

10 陳先博;服務(wù)組合的合規(guī)性度量與分析[D];南京理工大學(xué);2015年

，

本文編號(hào)：1654567