Dynamic fault-tolerance management (DFTM) was previously introduced as a means of providing environment and workload-driven adaptation for failure-prone battery powered systems.

This paper introduces and analyzes the role of local decision policies in a DFTM environment, and presents a precise formulation for when it is beneficial to activate a given DFTM algorithm with respect to metrics that combine performance, reliability, power consumption and battery life. In particular, local decision algorithms are described in the context of an imaging array application running on a network of resource-constrained processing elements. It is demonstrated that DFTM algorithms, in conjunction with appropriately chosen activation times, increase the mean computation before battery failure for a single battery, by a factor between 1.1 to 5.8, for the application investigated.