Analyzing reconfigurable algorithms for managing replicated data
We analyze reconfigurable algorithms for managing replicated data to determine how often one should detect and react to failure conditions so that reorganization operations can be performed at the appropriate time to improve the availability of replicated data. We use dynamic voting as a case study to reveal design trade-offs for designing such reconfigurable algorithms and illustrate how often failure detection and reconfiguration activities should be performed so as to maximize data availability. Stochastic Petri nets are used as a tool to facilitate our analysis.
availability dynamic voting stochastic petri nets reconfigurable systems data replication data contention distributed systems