Failure-Aware Task Scheduling of Synchronous Data Flow Graphs Under Real-Time Constraints

As more processors are integrated into Multiprocessor System-on-Chips (MPSoCs) via relentless technology scaling, the mean-time-to-failure (MTTF) is reduced to the extent that unexpected processor failures are considered during design time. A popular approach to tolerate processor failures is to migrate tasks on the faulty processor to live processors. This approach, however, is not suitable for real-time digital signal processing (DSP) applications since it may not guarantee real-time constraints. In this paper, we propose the re-scheduling of the entire application to minimize throughput degradation under a latency constraint, given that the application is specified by a Synchronous Data Flow (SDF) graph. We obtain sub-optimal re-scheduling results using a genetic algorithm for each scenario of processor failures at compile-time. If a failure is detected at run-time, the live processors obtain the saved schedule, perform task transfer, and execute the remaining tasks of the current iteration. We compare preemptive and non-preemptive migration policies and propose a hybrid policy to obtain better performance. We demonstrate the viability of the proposed technique through experiments with real-life DSP applications as well as randomly generated graphs under timing constraints and random fault scenarios.Center for Advanced Image and Information Technology National Research Foundation of Korea (NRF) Ministry of Education, Science and Technology MKE (The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) IT R&D program MKE/KEI