Cloud Service Matchmaking using Constraint Programming
This is an incremental improvement for cloud service marketplaces, enabling better service comparison based on quality of service requirements.
The paper tackled the problem of matching cloud services for users with limited technical knowledge by addressing list-typed QoS properties and preferences for value directions, using constraint programming to handle soft constraints and cover all QoS types.
Service requesters with limited technical knowledge should be able to compare services based on their quality of service (QoS) requirements in cloud service marketplaces. Existing service matching approaches focus on QoS requirements as discrete numeric values and intervals. The analysis of existing research on non-functional properties reveals two improvement opportunities: list-typed QoS properties as well as explicit handling of preferences for lower or higher property values. We develop a concept and constraint models for a service matcher which contributes to existing approaches by addressing these issues using constraint solvers. The prototype uses an API at the standardisation stage and discovers implementation challenges. This paper concludes that constraint solvers provide a valuable tool to solve the service matching problem with soft constraints and are capable of covering all QoS property types in our analysis. Our approach is to be further investigated in the application context of cloud federations.