Dynamic Formation and Strategic Management of Web Services Communities

In the last few years, communities of services have been studied in a certain numbers of proposals as virtual pockets of similar expertise. The motivation is to provide these services with high chance of discovery through better visibility, and to enhance their capabilities when it comes to provide requested functionalities. There are some proposed mechanisms and models on aggregating web services and making them cooperate within their communities. However, forming optimal and stable communities as coalitions to maximize individual and group efficiency and income for all the involved parties has not been addressed yet. Moreover, in the proposed frameworks of these communities, a common assumption is that residing services, which are supposed to be autonomous and intelligent, are competing over received requests. However, those services can also exhibit cooperative behaviors, for instance in terms of substituting each other. When competitive and cooperative behaviors and strategies are combined, autonomous services are said to be "coopetitive". Deciding to compete or cooperate inside communities is a problem yet to be investigated. In this thesis, we first identify the problem of defining efficient algorithms for coalition formation mechanisms. We study the community formation problem in two different settings: 1) communities with centralized manager having complete information using cooperative game-theoretic techniques; and 2) communities with distributed decision making mechanisms having incomplete information using training methods. We propose mechanisms for community membership requests and selections of web services in the scenarios where there is interaction between one community and many web services and scenarios where web services can join multiple established communities. Then in order to address the coopetitive relation within communities of web services, we propose a decision making mechanism for our web services to efficiently choose competition or cooperation strategies to maximize their payoffs. We prove that the proposed decision mechanism is efficient and can be implemented in time linear in the length of the time period considered for the analysis and the number of services in the community. Moreover, we conduct extensive simulations, analyze various scenarios, and confirm the obtained theoretical results using parameters from a real web services dataset

On The Assessment of Communities of Web Services

The notion of community of web services has been recently proposed and investigated to gather functionally similar web services in the same virtual space. This allows increasing the visibility of web services and their collaboration, which makes their discovery and composition easier. Using the community infrastructure, users are supposed to direct their requests to the community's manager (called master), that is in charge of selecting the appropriate web service. Because many communities providing the same functionality are available, selecting the best community to deal with, from the users and providers perspectives, is a key factor that still needs to be investigated. Another particularly challenging issue yet to be addressed is the selection by the master of the appropriate web service to be hosted in the community. Reputation has been proposed as a means to help users, providers, and masters evaluate and rank different candidates. However, reputation is mainly based on users feedback, which is not always accurate. Moreover, other performance parameters should be considered in the selection game. In this thesis, we propose a new assessment process that focuses on various performance aspects of the community rather than just its reputation. This assessment considers the performance parameters from the users, providers, and masters perspectives. In this approach, the communities performance rate is mainly based on the web services hosted by those communities. Such an assessment approach helps the master of the community differentiate between web services so that only the appropriate ones can be invited or accepted to join based on the communities requirements. It also helps the users and providers select the best available communities. The proposed method works on three steps. The first step focuses on defining and iv computing the evaluation metrics used in the assessment process while considering the requirements of all the stakeholders, namely users, providers, and communities. Thus, each community or web service is described by a vector of metrics. The second step includes the clustering of the evaluated communities and web services using the resulted vectors from the first step. During the third step, the resulting clusters are ranked using a function called goodness function. Web services and communities belonging to the best cluster are then selected. The effectiveness of the proposed assessment approach is tested by simulation and comparison to two other approaches in the literature