Summary Service-Oriented Computing (SOC) enables the composition of loosely coupled service agents provided with varying Quality of Service (QoS) levels, effectively forming a multiagent system (MAS). Selecting a (near-)optimal set of services for a composition in terms of QoS is crucial when many functionally equivalent services are available. As the number of distributed services, especially in the cloud, is rising rapidly, the impact of the network on the QoS keeps increasing. Despite this and opposed to most MAS approaches, current service approaches depend on a centralized architecture which cannot adapt to the network. Thus, we propose a scalable distributed architecture composed of a flexible number of distributed control nodes. Our architecture requires no changes to existing services and adapts from a centralized to a completely distributed realization by adding control nodes as needed. Also, we propose an extended QoS aggregation algorithm that allows to accurately estimate network QoS. Finally, we evaluate the benefits and optimality of our architecture in a distributed environment.
Motivated by the fact that most of the existing QoS service composition solutions have limited scalability, we develop a hierarchical-based solution framework to achieve scalability by means of topology abstraction and routing state aggregation. The paper presents and solves several unique challenges associated with the hierarchical-based QoS service composition solution in overlay networks, including topology formation (cluster detection and dynamic reclustering), QoS and service state aggregation and distribution, and QoS service path computation in a hierarchically structured network topology. In our framework, we (1) cluster network nodes based on their Internet distances and maintain clustering optimality at low cost by means of local reclustering operations when dealing with dynamic membership; (2) use data clustering and Bloom filter techniques to jointly reduce complexity of data representation associated with services within a cluster; and (3) investigate a top-down approach for computing QoS service paths in a hierarchical topology. Services in cloud computing can be categorized into two groups: Application services and Utility Computing Services. Compositions in the application level are similar to the Web service compositions in SOC (Service-Oriented Computing). Compositions in the utility level are similar to the task matching and scheduling in grid computing. Contributions of this paper include: 1) An extensible QoS model is proposed to calculate the QoS values of services in cloud computing. 2) A genetic-algorithm-based approach is proposed to compose services in cloud computing. 3) A comparison is presented between the proposed approach and other algorithms, i.e., exhaustive search algorithms and random selection algorithms. Next generation networks are envisioned to support dynamic and customizable service compositions at Internet scale. To facilitate the communication between distributed software components, on-demand and QoS-aware network service composition across large scale networks emerges as a key research challenge. This paper presents a fast QoS-aware service composition algorithm for selecting a set of interconnected domains with specific service classes. We further show how such algorithm can be used to support network adaptation and service mobility. In simulation studies performed on large scale networks, the algorithm exhibits very high probability of finding the optimal solution within short execution time. In addition, we present a distributed service composition framework utilizing this algorithm.