Accountability monitoring and reasoning in service-oriented architectures
Journal
Service Oriented Computing and Applications
Journal Volume
1
Journal Issue
1
Pages
35-50
Date Issued
2007
Date
2007
Author(s)
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. © Springer-Verlag London Limited 2007.
Subjects
Accountability; Bayesian networks; Diagnosis; Monitor; SOA
SDGs
Other Subjects
Algorithms; Bayesian networks; Channel state information; Cost effectiveness; Learning systems; Problem solving; Accountability; Channel selection; Service-oriented architecture (SOA); Network architecture
Type
journal article
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