SRS: A Decentralized and Rule-Based Approach to Data Dependency Analysis and Failure Recovery in Service-Oriented Environments

Texas Tech University, Lubbock TX

Investigators

Abstract

NSF Proposal 0820152 A Decentralized and Rule-Based Approach to Data Dependency Analysis and Failure Recovery in a Service-Oriented Environment PI: Susan D. Urban The objective of this research is to develop a decentralized approach to data dependency analysis and failure recovery among concurrently executing processes in a loosely-coupled service-oriented environment. The approach involves monitoring externalized data changes of individual service executions. Peer-to-peer, decentralized communication among process execution agents is then used to discover data dependencies among concurrently executing processes that may lead to data inconsistencies during the recovery of a failed process. Process interference rules of dependent processes are used to test user-defined semantic conditions to determine if 1) critical data conditions have been affected by the recovery of a failed process and 2) recovery procedures should be invoked for dependent processes. The research includes the development of a methodology for using process interference rules. The correctness and efficiency of decentralized data dependency analysis and rule-based recovery procedures are also demonstrated for concurrent processes in the context of a service composition model that supports compensation, contingency, rollback, and retry techniques. This research provides a new way of thinking about traditional transaction recoverability concepts, providing a dynamic approach to discovering data dependencies and responding to failures in a manner that guarantees user-defined correctness conditions for concurrent processes that execute without isolation guarantees.