Accountability monitoring and reasoning in service-oriented architectures

Abstract

Service-oriented architecture (SOA) provides a powerful paradigm to compose service processes using individual atomic services. When running a service process, SOA needs an efficient and effective mechanism to detect service delivery failures and to identify the individual service(s) that causes the problem. In this research, we study the model of accountability to detect, diagnose, and defuse the real cause of a problem when service errors (such as incorrect result or SLA violation) occur in a service process. Our approach leverages Bayesian networks to identify the most likely problematic services in a process and selectively inspect those services. An evidence channel selection algorithm is designed to specify which services in a service network should be monitored to achieve the best cost-efficiency. We model the channels selection as the classic facilities location problem. We also adopt a continuous knowledge learning process to manage the dynamic nature of SOA. The performance study shows that our proposed accountability mechanism is effective on identifying the root cause of problems and can achieve significant cost savings: with 50% of services’ outputs monitored as evidence, the comprehensive diagnosis correctness can reach 80% after only 20% of services are inspected.