SanGA: A Self-Adaptive Network-Aware Approach to Service Composition

Abstract

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.