Runtime QoS control and revenue optimization within service oriented architecture

The paradigms of service–oriented computing (SOC) and its underlying service–oriented architecture (SOA) have received a lot of attention recently and have changed the way software applications are designed, developed, deployed, and consumed. Due to these paradigms, software engineers can realize applications by service composition, using services offered by third parties. In the competitive market of composite services, the commercial success of composite service providers (CSP) is directly related to their ability to offer services at sharp price/quality ratios. This raises the need to realize desired client perceived Quality of Service (QoS) levels at minimal cost. The problem of controlling QoS in SOC is complex in that the ownership of the services is decentralized, as a composite service makes use of services offered by third parties. Although a plethora of well–known QoS–control mechanisms exists for “atomic” Web services used for the composition, it remains a challenge how to exploit these mechanisms for QoS–control in SOA in a cost–effective way. The great potential for composite service providers to realize dramatic cost savings and/or revenue improvements by optimizing the QoS–control in SOA has not been exploited much so far. To address this issue, proper modelling of the effects of QoS–control parameters is required. Once the models are specified, analysis of these models to derive the optimal settings of the parameters is a natural next step. This thesis contributes models and methods to address these QoS–control issues within SOA. We develop the models of the runtime end–to–end QoS–control mechanisms, that are used to satisfy QoS requirements of an individual composite service request (e.g. response time) while optimizing some long–term goal (e.g. execution cost minimization, expected revenue). These models, based on per–request, per–task service selection, facilitate development (using, among others, dynamic programming approach) of simple, yet effective optimal decision–making policies in order to satisfy specified QoS levels. We demonstrate the effectiveness of the developed solutions as well as significant revenue improvements by extensive numerical experiments. The derived policies have negligible overhead with respect to the decision–making process and control actions to be taken by the CSP. Besides, the implementation of these policies is relatively simple, e.g. as a lookup table. The control actions may be automated, and allow for fast reactions to the changes in the volatile service execution environment. In our view this thesis presents a significant step forward to envisioned autonomous, economically profitable systems of services and applications of the future. Our approach opens many interesting opportunities for further research in the challenging area of QoS–control of such “system of systems”.