Method for derivation of data-aware upper and lower bounds of QoS attributes for service compositions
Manuel Carro <email@example.com> Universidad Politécnica de Madrid
This method tries to infer upper and lower bounds of QoS metrics as functions of input data, and use them for adaptive QoS-aware matching and predictive monitoring. Automated complexity analysis is applied to Horn clause representations of service choreographies to deduce safe upper and lower approximations of computational complexity as functions of input data. These approximations are combined with infrastructure metrics to obtain (predicted) QoS bounds. Area: Service
BPEL representation of a service orchestration is translated into a Horn Clause program, and subjected to the computational cost analysis using the Ciao Development Environment cost analyzer.
(no online demo available)
Ivanović, D., Carro, M., & Hermenegildo, M. (2010). Towards Data-Aware QoS-Driven Adaptation for Service Orchestrations. In Proceedings of the 2010 IEEE International Conference on Web Services (ICWS 2010), Miami, FL, USA, 5-10 July 2010. IEEE.
Software Engineering Life-Cycle, Service adaptation,Grid & Cloud Computing
Prototype (single scenario)
Relationship with Future Internet and Internet of Services
Analyzing the computation cost of service orchestration, in terms of the number of events (e.g. steps, invocations of a component service, loop iterations, etc.) and data sizes (e.g. number of nodes or depth of a XML data structure) can be very useful for proactive adaptation, service binding and match-making under given QoS constraints.
Relationship with Cloud
(currently no own web site)