Cloud computing, mobile computing, Service-Oriented Computing (SOC), and Software as a Service (SaaS) indicate that the Internet emerges to an anonymous service market where service functionality can be dynamically and ubiquitously consumed. Among functionally similar services, service consumers are interested in the consumption of the services which perform best towards their optimization preferences. The experienced performance of a service at consumer side is expressed in its non-functional properties (NFPs). Selecting the best-fit service is an individual challenge as the preferences of consumers vary. Furthermore, service markets such as the Internet are characterized by perpetual change and complexity. The complex collaboration of system environments and networks as well as expected and unexpected incidents may result in various performance experiences of a specific service at consumer side. The consideration of certain call side aspects that may distinguish such differences in the experience of NFPs is reflected in various call contexts. Service optimization based on a collaborative knowledge base of previous experiences of other, similar consumers with similar preferences is a desirable foundation. The research work described in this dissertation aims at an individually optimized selection of services considering the individual call contexts that have an impact on the performance, or NFPs in general, of a service as well as the various consumer preferences. The presented approach exploits shared measurement information about the NFP behavior of a service gained from former service calls of previous consumptions. Gaining selection/recommendation knowledge from shared experience benefits existing as well as new consumers of a service before its (initial) consumption. Our approach solely focuses on the optimization and collaborative information exchange among service consumers. It does not require the contribution of service providers or other non-consuming entities. As a result, the contribution among the participating entities also contributes to their own overall optimization benefit. With the initial focus on a single-tier optimization, we additionally provide a conceptual solution to a multi-tier optimization approach for which our recommendation framework is prepared in general. For a consumer-sided optimization, we conducted a literature study of conference papers of the last decade in order to find out what NFPs are relevant for the selection and consumption of services. The ranked results of this study represent what a broad scientific community determined to be relevant NFPs for service selection. We analyzed two general approaches for the employment of machine learning methods within our recommendation framework as part of the preparation of the actual recommendation knowledge. Addressing a future service market that has not fully developed yet and due to the fact that it seems to be impossible to be aware of the actual NFP data of different Web services at identical call contexts, a real-world validation is a challenge. In order to conduct an evaluation and also validation that can be considered to be close approximations to reality with the flexibility to challenge the machine learning approaches and methods as well as the overall recommendation approach, we used generated NFP data whose characteristics are influenced by measurement data gained from real-world Web services. For the general approach with the better evaluation results and benefits ratio, we furthermore analyzed, implemented, and validated machine learning methods that can be employed for service recommendation. Within the validation, we could achieve up to 95% of the overall achievable performance (utility) gain with a machine learning method that is focused on drift detection, which in turn, tackles the change characteristic of the Internet being an anonymous service market.

Nowadays, a multitude of functionally equal web services are available. By thisbroad offer, the need of a service recommendation based on non-functional characteristics(e.g. price, response time, availability) is increasing. The static ServiceLevel Agreements (SLAs) of service providers cannot suffice this need. SLAs arenot reliable enough, due to the fact that they do not cover the dynamic performanceand quality changes of services during their lifetime. This bachelor’s thesis waswritten within a research project of the Linnaeus University in Sweden and the KarlsruheUniversity of Applied Science in Germany. The goal of this research projectis to eliminate the issues as described above. For this reason, a framework for anoptimized service selection was developed. Instead of using the static SLAs, measurementsof each service call are taken. On the basis of the measurements and therequirements of the consumer, the framework then provides an automated best-fitservice selection. The purpose of this thesis is to involve the geographic location of each serviceconsumer in the automated service selection. Therefore, a mobile app was developedto get a sufficient amount of real world test data. This app measures service calls andadditionally records the geographic location of the user. Based on the geographiclocation, the collected measurement data then were grouped into regions. Thereby,it could be shown that the geographic location of the user can be used to improve theoptimal service selection. / Service-Oriented Computing

Indiana University-Purdue University Indianapolis (IUPUI) / This dissertation proposes a framework (TruSStReMark - Trust-based Service Selection and Recommendation for Online Software Marketplaces) to model, quantify, and monitor trust of software services and to perform trust-based service selection and recommendations. It provides methods to analyze and aggregate external reviews, pertaining to specific QoS attributes, of software services by performing subjective logic-based operations. This framework, first, defines trust of a software service using theory of belief and extends the multi-level software specifications to represent the trust-based attributes. It, then, proposes enhancements to two prevalent algorithms for selecting and recommending software services from a marketplace. Finally, the performances of the enhanced selection and recommendation algorithms are improved by parallelizing them. When compared with the prevalent Content-based and Collaborative filtering-based approaches, the results show that, the TruSStReMark is able to produce better results in terms of quality measured using HR (Hit Ratio) and ARHR (Average Reciprocal Hit-Rank) metrics. In addition, the parallelized versions of the trust-based selection and recommendation algorithms improve the end-to-end runtime. The TruSStReMark will enable users to select services, which are trustworthy, from online software marketplaces and use them in composing quality-aware distributed systems.

Web services have been developed as an attractive paradigm for publishing, discovering and consuming services. They are loosely-coupled applications that can be run alone or be composed to create new value-added services. They can be consumed as individual services which provide a unique interface to receive inputs and return outputs; or they can be consumed as components to be integrated into business processes. We call the first consumption case individual use and the second case business process use. The requirement of specific tools to assist consumers in the two service consumption cases involves many researches in both academics and industry. On the one hand, many service portals and service crawlers have been developed as specific tools to assist users to search and invoke Web services for individual use. However, current approaches take mainly into account explicit knowledge presented by service descriptions. They make recommendations without considering data that reflect user interest and may require additional information from users. On the other hand, some business process mechanisms to search for similar business process models or to use reference models have been developed. These mechanisms are used to assist process analysts to facilitate business process design. However, they are labor-intense, error-prone, time-consuming, and may make business analyst confused. In our work, we aim at facilitating the service consumption for individual use and business process use using recommendation techniques. We target to recommend users services that are close to their interest and to recommend business analysts services that are relevant to an ongoing designed business process. To recommend services for individual use, we take into account the user's usage data which reflect the user's interest. We apply well-known collaborative filtering techniques which are developed for making recommendations. We propose five algorithms and develop a web-based application that allows users to use services. To recommend services for business process use, we take into account the relations between services in business processes. We target to recommend relevant services to selected positions in a business process. We define the neighborhood context of a service. We make recommendations based on the neighborhood context matching. Besides, we develop a query language to allow business analysts to formally express constraints to filter services. We also propose an approach to extract the service's neighborhood context from business process logs. Finally, we develop three applications to validate our approach. We perform experiments on the data collected by our applications and on two large public datasets. Experimental results show that our approach is feasible, accurate and has good performance in real use-cases

In the modern world, service oriented applications are becoming more and more popular from year to year. To remain competitive, these Web services must provide the high level of quality. From another perspective, the end user is interested in getting the service, which fits the user's requirements the best: for limited resources get the service with the best available quality. In this work, the model for automated service selection was presented to solve this problem. The main focus of this work was to provide high accuracy of this model during the prediction of Web service’s response time. Therefore, several machine learning algorithms were selected and used in the model as well as several experiments were conducted and their results were evaluated and analysed to select one machine learning algorithm, which coped best with the defined task. This machine learning algorithm was used in final version of the model. As a result, the selection model was implemented, whose accuracy was around 80% while selecting only one Web service as a best from the list of available. Moreover, one strategy for measuring accuracy has also been developed, the main idea of which is the following: not one but several Web services, the difference in the response time of which does not exceed the boundary value, can be considered as optimal ones. According to this strategy, the maximum accuracy of selecting the best Web service was about 89%. In addition, a strategy for selecting the best Web service from the end-user side was developed to evaluate the performance of implemented model. Finally, it should also be mentioned that with the help of specific tool the input data for the experiments was generated, which allowed not only generating different input datasets without huge time consumption but also using the input data with the different type (linear, periodic) for experiments.

With the increasing use of web services in everyday tasks we are entering an era of Internet of Services (IoS). Service discovery and selection in both centralized and decentralized environments have become a critical issue in the area of web services, in particular when services having similar functionality but different Quality of Service (QoS). As a result, selecting a high quality service that best suits consumer requirements from a large list of functionally equivalent services is a challenging task. In response to increasing numbers of services in the discovery and selection process, there is a corresponding increase of service consumers and a consequent diversity in Quality of Service (QoS) available. Increases in both sides leads to a diversity in the demand and supply of services, which would result in the partial match of the requirements and offers. Furthermore, it is challenging for customers to select suitable services from a large number of services that satisfy consumer functional requirements. Therefore, web service recommendation becomes an attractive solution to provide recommended services to consumers which can satisfy their requirements. In this thesis, first a service ranking and selection algorithm is proposed by considering multiple QoS requirements and allowing partially matched services to be counted as a candidate for the selection process. With the initial list of available services the approach considers those services with a partial match of consumer requirements and ranks them based on the QoS parameters, this allows the consumer to select suitable service. In addition, providing weight value for QoS parameters might not be an easy and understandable task for consumers, as a result an automatic weight calculation method has been included for consumer requirements by utilizing distance correlation between QoS parameters. The second aspect of the work in the thesis is the process of QoS based web service recommendation. With an increasing number of web services having similar functionality, it is challenging for service consumers to find out suitable web services that meet their requirements. We propose a personalised service recommendation method using the LDA topic model, which extracts latent interests of consumers and latent topics of services in the form of probability distribution. In addition, the proposed method is able to improve the accuracy of prediction of QoS properties by considering the correlation between neighbouring services and return a list of recommended services that best satisfy consumer requirements. The third part of the thesis concerns providing service discovery and selection in a decentralized environment. Service discovery approaches are often supported by centralized repositories that could suffer from single point failure, performance bottleneck, and scalability issues in large scale systems. To address these issues, we propose a context-aware service discovery and selection approach in a decentralized peer-to-peer environment. In the approach homophily similarity was used for bootstrapping and distribution of nodes. The discovery process is based on the similarity of nodes and previous interaction and behaviour of the nodes, which will help the discovery process in a dynamic environment. Our approach is not only considering service discovery, but also the selection of suitable web service by taking into account the QoS properties of the web services. The major contribution of the thesis is providing a comprehensive QoS based service recommendation and selection in centralized and decentralized environments. With the proposed approach consumers will be able to select suitable service based on their requirements. Experimental results on real world service datasets showed that proposed approaches achieved better performance and efficiency in recommendation and selection process.

Service composition integrates existing services to fulfill specific tasks using a set of standards and tools. Existing service composition techniques and tools are mainly designed for SOA professionals. The business processes used in the service composition systems are primarily designed by experienced business analysts who have extensive process knowledge. Process knowledge is the information about a process, including the tasks in a process, the control flow and data flow among tasks. It is challenging for end-users without sufficient service composition skills and process knowledge to find desired services then compose services to perform their daily activities, such as planning a trip. Context-aware techniques provide a promising way to help end-users find services using the context of end-users. However, existing context-aware techniques have limited support for dynamic adapting to new context types (e.g., location, time and activity) and context values (e.g., “New York City”). To shelter end-users from the complexity of service composition, we present our techniques that assist non-IT professional end-users in service composition by dynamically composing and recommending services to meet their requirements. To acquire the desired process knowledge for service composition, we propose an approach to automatically extract process knowledge from existing commercial applications on the Web. By analyzing the context of end-users, our techniques can dynamically adapt to new context types or values and provide personalized service recommendation for end-users. Instead of requiring end-users to specify detailed steps for service composition, the end-users only need to describe their goals using a few keywords. Our approach expands the meaning of an end-user's goal using process knowledge then derives a group of tasks to help the end-user fulfill the goal. The effectiveness of our proposed techniques is demonstrated through a set of case studies. / Thesis (Ph.D, Computing) -- Queen's University, 2011-09-30 11:43:39.151

Les services Web proposent un paradigme intéressant pour la publication, la découverte et la consommation de services. Ce sont des applications faiblement couplées qui peuvent être exécutées seules ou être composées pour créer de nouveaux services à valeur ajoutée. Ils peuvent être consommés comme des services individuels qui fournissent une interface unique qui reçoit des inputs et retourne des outputs (cas 1), ou bien ils peuvent être consommés en tant que composants à intégrer dans des procédés métier (cas 2). Nous appelons le premier cas de consommation « utilisation individuelle » et le second cas de consommation « utilisation en procédé métier ». La nécessité d'avoir des outils dédiés pour aider les consommateurs dans les deux cas de consommation a impliqué de nombreux travaux de recherche dans les milieux académiques ou industriels. D'une part, beaucoup de portails et de moteurs de recherche de services ont été développés pour aider les utilisateurs à rechercher et invoquer les services Web pour une utilisation individuelle. Cependant, les approches actuelles prennent principalement en compte les connaissances explicites présentées par les descriptions de service. Ils font des recommandations sans tenir compte des données qui reflètent l'intérêt des utilisateurs et peuvent demander des informations supplémentaires aux utilisateurs. D'autre part, plusieurs techniques et mécanismes associées aux procédés métier ont été élaborés pour rechercher des modèles de procédé métiers similaires, ou utiliser des modèles de référence. Ces mécanismes sont utilisés pour assister les analystes métiers à la conception de procédés métiers. Cependant, ils sont lents, source d'erreurs, grands consommateurs de ressources humaines, et peuvent induire à l’erreur les analystes métier. Dans notre travail, nous cherchons à faciliter la consommation de services Web pour une utilisation individuelle ou en procédé métier en proposant des techniques de recommandation. Notre objectif est de recommander aux utilisateurs des services qui sont proches de leur intérêt et de recommander aux analystes métier des services qui sont pertinents pour un procédé métier en cours de conception. Pour recommander des services pour une utilisation individuelle, nous prenons en compte l’historique des données d'utilisation de l'utilisateur qui reflètent ses intérêts. Nous appliquons des techniques de filtrage collaboratif bien connues pour faire des recommandations. Nous avons proposé cinq algorithmes et développé une application Web qui permet aux utilisateurs d'utiliser des services recommandés. Pour recommander des services pour une utilisation en procédé métier, nous prenons en compte les relations entre les services du procédé métier. Nous proposons de recommander les services en fonction de leurs localisations dans le procédé métier. Nous avons définit le contexte de voisinage d'un service. Nous avons présenté des recommandations basées sur l'appariement de contexte de voisinage. Par ailleurs, nous avons développé un langage de requête pour permettre aux analystes métier d'exprimer formellement des contraintes de filtrage. Nous avons proposé également une approche pour extraire le contexte de voisinage à partir de traces d’exécution de procédés métier. Enfin, nous avons développé trois applications afin de valider notre approche. Nous avons effectué des expérimentations sur des données recueillies par nos applications et sur deux grands ensembles de données publiques. Les résultats expérimentaux montrent que notre approche est faisable, précise et performante dans des cas d'utilisation réels / Web services have been developed as an attractive paradigm for publishing, discovering and consuming services. They are loosely-coupled applications that can be run alone or be composed to create new value-added services. They can be consumed as individual services which provide a unique interface to receive inputs and return outputs; or they can be consumed as components to be integrated into business processes. We call the first consumption case individual use and the second case business process use. The requirement of specific tools to assist consumers in the two service consumption cases involves many researches in both academics and industry. On the one hand, many service portals and service crawlers have been developed as specific tools to assist users to search and invoke Web services for individual use. However, current approaches take mainly into account explicit knowledge presented by service descriptions. They make recommendations without considering data that reflect user interest and may require additional information from users. On the other hand, some business process mechanisms to search for similar business process models or to use reference models have been developed. These mechanisms are used to assist process analysts to facilitate business process design. However, they are labor-intense, error-prone, time-consuming, and may make business analyst confused. In our work, we aim at facilitating the service consumption for individual use and business process use using recommendation techniques. We target to recommend users services that are close to their interest and to recommend business analysts services that are relevant to an ongoing designed business process. To recommend services for individual use, we take into account the user's usage data which reflect the user's interest. We apply well-known collaborative filtering techniques which are developed for making recommendations. We propose five algorithms and develop a web-based application that allows users to use services. To recommend services for business process use, we take into account the relations between services in business processes. We target to recommend relevant services to selected positions in a business process. We define the neighborhood context of a service. We make recommendations based on the neighborhood context matching. Besides, we develop a query language to allow business analysts to formally express constraints to filter services. We also propose an approach to extract the service's neighborhood context from business process logs. Finally, we develop three applications to validate our approach. We perform experiments on the data collected by our applications and on two large public datasets. Experimental results show that our approach is feasible, accurate and has good performance in real use-cases