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

  • Authors:
  • Chanhee Lee;Sungchan Kim;Hyunok Oh;Soonhoi Ha

  • Affiliations:
  • Seoul National University, Seoul, Korea 151-744;Chonbuk National University, Jeonbuk, Korea 561-756;Hanyang University, Seoul, Korea 133-791;Seoul National University, Seoul, Korea 151-744

  • Venue:
  • Journal of Signal Processing Systems
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.