High-Confidence Compositional Reliability Assessment of SOA-Based Systems Using Machine Learning Techniques

Service-oriented architecture (SOA) techniques are being increasingly used for developing critical applications, especially network-centric systems. While the SOA paradigm provides flexibility and agility to better respond to changing business requirements, the task of assessing the reliability of SOA-based systems is quite challenging. Deriving high confidence reliability estimates for mission-critical systems can require huge costs and time. SOAsystems/ applications are built by using either atomic or composite services as building blocks. These services are generally assumed to be realized with reuse and logical composition of components. One approach for assessing the reliability of SOA-based systems is to use AI reasoning techniques on dynamically collected failure data of each service and its components as one of the evidences together with results from random testing. Memory-Based Reasoning technique and Bayesian Belief Net-works are verified as the reasoning tools best suited to guide the prediction analysis. A framework constructed from the above approach identifies the least tested and “high usage” input subdomains of the service(s) and performs necessary remedial actions depending on the predicted results.