Web Services Communities: from Intra-Community Coopetition to Inter-Community Competition

This chapter discusses the structure and management of communities of Web services from two perspectives. The first perspective, called coopetition, shows the simultaneous cooperative and competitive behaviors that Web services exhibit when they reside in the same community. These Web services offer similar functionalities, and hence are competitive, but they can also cooperate as they share the same savoir-faire. The second perspective, called competition, shows the competition that occurs not between Web services but between their communities, which are associated with similar functionalities. To differentiate such communities, a competition model based on a set of metrics is discussed in this chapter

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

Trust and Reputation in Multi-Agent Systems

Multi-Agent systems (MAS) are artificial societies populated with distributed autonomous agents that are intelligent and rational. These self-independent agents are capable of independent decision making towards their predefined goals. These goals might be common between agents or unique for an agent. Agents may cooperate with one another to facilitate their progresses. One of the fundamental challenges in such settings is that agents do not have a full knowledge over the environment and regarding their decision making processes, they might need to request other agents for a piece of information or service. The crucial issues are then how to rely on the information provided by other agents, how to consider the collected data, and how to select appropriate agents to ask for the required information. There are some proposals addressing how an agent can rely on other agents and how an agent can compute the overall opinion about a particular agent. In this context, the trust value reflects the extent to which agents can rely on other agents and the reputation value represents public opinion about a particular agent. Existing approaches for reliable information propagation fail to capture the dynamic relationships between agents and their influence on further decision making process. Therefore, these models fail to adapt agents to frequent environment changes. In general, a well-founded trust and reputation system that prevents malicious acts that are emerged by selfish agents is required for multi-agent systems. We propose a trust mechanism that measures and analyzes the reliability of agents cooperating with one another. This mechanism concentrates on the key attributes of the related agents and their relationships. We also measure and analyze the public reputation of agents in large-scale environments utilizing a sound reputation mechanism. In this mechanism, we aim at maintaining a public reputation assessment in which the public actions of agents are accurately under analysis. On top of the theoretical analysis, we experimentally validate our trust and reputation approaches through different simulations. Our preliminary results show that our approach outperforms current frameworks in providing accurate credibility measurements and maintaining accurate trust and reputation mechanisms

Game-Theoretic Foundations for Forming Trusted Coalitions of Multi-Cloud Services in the Presence of Active and Passive Attacks

The prominence of cloud computing as a common paradigm for offering Web-based services has led to an unprecedented proliferation in the number of services that are deployed in cloud data centers. In parallel, services' communities and cloud federations have gained an increasing interest in the recent past years due to their ability to facilitate the discovery, composition, and resource scaling issues in large-scale services' markets. The problem is that the existing community and federation formation solutions deal with services as traditional software systems and overlook the fact that these services are often being offered as part of the cloud computing technology, which poses additional challenges at the architectural, business, and security levels. The motivation of this thesis stems from four main observations/research gaps that we have drawn through our literature reviews and/or experiments, which are: (1) leading cloud services such as Google and Amazon do not have incentives to group themselves into communities/federations using the existing community/federation formation solutions; (2) it is quite difficult to find a central entity that can manage the community/federation formation process in a multi-cloud environment; (3) if we allow services to rationally select their communities/federations without considering their trust relationships, these services might have incentives to structure themselves into communities/federations consisting of a large number of malicious services; and (4) the existing intrusion detection solutions in the domain of cloud computing are still ineffective in capturing advanced multi-type distributed attacks initiated by communities/federations of attackers since they overlook the attacker's strategies in their design and ignore the cloud system's resource constraints. This thesis aims to address these gaps by (1) proposing a business-oriented community formation model that accounts for the business potential of the services in the formation process to motivate the participation of services of all business capabilities, (2) introducing an inter-cloud trust framework that allows services deployed in one or disparate cloud centers to build credible trust relationships toward each other, while overcoming the collusion attacks that occur to mislead trust results even in extreme cases wherein attackers form the majority, (3) designing a trust-based game theoretical model that enables services to distributively form trustworthy multi-cloud communities wherein the number of malicious services is minimal, (4) proposing an intra-cloud trust framework that allows the cloud system to build credible trust relationships toward the guest Virtual Machines (VMs) running cloud-based services using objective and subjective trust sources, (5) designing and solving a trust-based maxmin game theoretical model that allows the cloud system to optimally distribute the detection load among VMs within a limited budget of resources, while considering Distributed Denial of Service (DDoS) attacks as a practical scenario, and (6) putting forward a resource-aware comprehensive detection and prevention system that is able to capture and prevent advanced simultaneous multi-type attacks within a limited amount of resources. We conclude the thesis by uncovering some persisting research gaps that need further study and investigation in the future