Service-Oriented Computing enables the composition of loosely coupled services provided with varying Quality of Service (QoS) levels. 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, particularly in the cloud, is rising rapidly, the impact of the network on the QoS keeps increasing. Despite this, current approaches do not differentiate between the QoS of services themselves and the network. Therefore, the computed latency differs from the actual latency, resulting in suboptimal QoS. Thus, we propose a network-aware approach that handles the QoS of services and the QoS of the network independently. First, we build a network model in order to estimate the network latency between arbitrary services and potential users. Our selection algorithm then leverages this model to find compositions with a low latency for a given execution policy. We employ a self-adaptive genetic algorithm which balances the optimization of latency and other QoS as needed and improves the convergence speed. In our evaluation, we show that our approach works under realistic network conditions, efficiently computing compositions with much lower latency and otherwise equivalent QoS compared to current approaches.

Service composition algorithms are used for realizing loosely coupled interactions in Service-Oriented Computing. Starting from an abstract workflow, concrete services are matched, based on their QoS, with the preferences and constraints of users. Current approaches usually only consider static QoS values and find a single solution consisting of one concrete service for each workflow task. In a business-to-business (B2B) environment, though, there are additional requirements for service compositions: 1) a high number of invocations, and 2) a high reliability. Thus, we introduce a probabilistic approach on the basis of a new QoS model to solve the composition problem for such long-term B2B service compositions. For each task and for every point in time, we determine the most appropriate services and backup services for a specific user. Thus, the selection depends on the actual response time and reliability, or recent invocation failures or timeouts. For that purpose, we propose an adaptive genetic algorithm that employs our QoS model and determines backup services dynamically based on the required reliability. Our evaluations show that our approach significantly increases the utility of long-term compositions compared with standard approaches in the envisioned B2B environments.

Optimizing the Quality-of-Service (QoS) levels of a service workflow is essential for the user satisfaction in Service-oriented Computing. For that purpose, QoS computation models are applied to reflect the actual QoS experienced by the user during service execution. Current QoS models ignore the possible dependencies of QoS attributes, such as the dependency on the time of the execution or on the input data supplied to the service. Apart from that, composition approaches consider only single workflows during service selection, narrowing the number of possible compositions. Thus, we introduce a novel QoS model that covers QoS dependencies and discuss how this model can be used to consider multiple workflows at the same time. Moreover, we adopt a multi-objective optimization approach to offer solutions varying in QoS such as finishing time and price, allowing the user to make fine-grained decisions.

Service-Oriented Computing (SOC) enables the composition of loosely coupled services provided with varying Quality of Service (QoS) levels. Selecting a (near-)optimal set of services for a composition in terms of QoS is crucial when many functionally equivalent services are available. With the advent of Cloud Computing, both the number of such services and their distribution across the network are rising rapidly, increasing the impact of the network on the QoS of such compositions. Despite this, current approaches do not differentiate between the QoS of services themselves and the QoS of the network. Therefore, the computed latency differs substantially from the actual latency, resulting in suboptimal QoS for service compositions in the cloud. Thus, we propose a network-aware approach that handles the QoS of services and the QoS of the network independently. First, we build a network model in order to estimate the network latency between arbitrary services and potential users. Our selection algorithm then leverages this model to find compositions that will result in a low latency given an employed execution policy. In our evaluation, we show that our approach efficiently computes compositions with much lower latency than current approaches.

Service-Oriented Architecture enables the composition of loosely coupled services provided with varying Quality of Service (QoS) levels. Given a composition, finding the set of services that optimizes some QoS attributes under given QoS constraints has been shown to be NP-hard. Therefore, heuristic algorithms are widely used, finding acceptable solutions in polynomial time. Still the time complexity of such algorithms can be prohibitive for real-time use, especially if the algorithms are required to run until they find near-optimal solutions. Thus, we propose a heuristic approach based on Hill-Climbing that makes effective use of an initial bias computed with Linear Programming, and works on a reduced search space. In our evaluation, we show that our approach finds near-optimal solutions and achieves a low time complexity.