Dynamic contention management for distributed applications

Abstract

Distributed applications often make use of replicated state to afford a greater level of availability and throughput. This is achieved by allowing individual processes to progress without requiring prior synchronisation. This approach, termed optimistic replication, results in divergent replicas that must be reconciled to achieve an overall consistent state. Concurrent operations to shared objects in the replicas result in conflicting updates that require reconciliatory action to rectify. This typically takes the form of compensatory execution or simply undoing and rolling back client state. When considering user interaction with the application, there exists relationships and intent in the ordering and execution of these operations. The enactment of reconciliation that determines one action as conflicted may have far reaching implications with regards to the user’s original intent. In such scenarios, the compensatory action applied to a conflict may require previous operations to also be undone or compensated such that the user’s intent is maintained. Therefore, an ability to manage the contention to the shared data across the distributed application to pre-emptively lower conflicts resulting from these infringements is desirable. The aim is to not hinder throughput, achieved from the weaker consistency model known as eventual consistency. In this thesis, a model is presented for a contention management framework that schedules access using the expected execution inherent in the application domain to best inform the contention manager. A backoff scheme is employed to create an access schedule, preserving user intent for applications that require this high level of maintenance for user actions. By using such an approach, this results in a performance improvement seen in the reduction of the overall number of conflicts, while also improving overall system throughput. This thesis describes how the contention management scheme operates and, through experimentation, the performance benefits received.