An initial proposal for data-aware resource analysis of orchestrations with applications to predictive monitoring

Abstract

Several activities in service oriented computing can benefit from knowing ahead of time future properties of a given service composition. In this paper we focus on how statically inferred computational cost functions on input data, which represent safe upper and lower bounds, can be used to predict some QoS-related values at runtime. In our approach, BPEL processes are translated into an intermediate language which is in turn converted into a logic program. Cost and resource analysis tools are applied to infer functions which, depending on the contents of some initial incoming message, return safe upper and lower bounds of some resource usage measure. Actual and predicted time characteristics are used to perform predictive monitoring. A validation is performed through simulation. © 2010 Springer-Verlag.