Enabling decision support for the delivery of real-time services

The domain of high assurance distributed systems has focused greatly on the areas of fault tolerance and dependability. As a result the paradigm of service orientated architectures (SOA) has been commonly applied to realize the significant benefits of loose coupling and dynamic binding. However, there has been limited research addressing the issues of managing real-time constraints in SOAs that are by their very nature dynamic. Although the paradigm itself is derived from fundamental principles of dependability, these same principles appear to not be applied when considering the timed dimension of quality of service. As a result the current state-of-the-art in SOA research only addresses soft real-time and does not seek to provide concrete guarantees about a systems performance. When a distributed system is deployed we do not understand enough the emerging behavior that will occur. This paper therefore proposes an approach that probabilistically monitors system state within a given workflow's execution window. Utilizing a real distributed system we experiment with services from the computer vision domain, with clear real-time constraints, evaluating the performance of each system component. Our approach successfully models the likelihood of the service meeting providing various levels of QoS, providing the basis for a more dynamic and intelligent approach to real-time service orientation.