Fault-Tolerant Dynamic Task Graph Scheduling

In this paper, we present an approach to fault tolerant execution of dynamic task graphs scheduled using work stealing. In particular, we focus on selective and localized recovery of tasks in the presence of soft faults. From users, we elicit the basic task graph structure in terms of successor and predecessor relationships. The work-stealing-based algorithm to schedule such a task graph is augmented to enable recovery when the data and metadata associated with a task get corrupted. We use this redundancy, and knowledge of the task graph structure, to selectively recover from faults with low space and time overheads. We show that the fault tolerant design retains the essential properties of the underlying work stealing-based task scheduling algorithm, and that the fault tolerant execution is asymptotically optimal when task re-execution is taken into account. Experimental evaluation demonstrates the low cost of recovery under various fault scenarios.