Schedulability analysis for non-preemptive fixed-priority multiprocessor scheduling

  • Authors:

  • Nan Guan;Wang Yi;Qingxu Deng;Zonghua Gu;Ge Yu

  • Affiliations:

  • Northeastern University, Shenyang, China and Uppsala University, Uppsala, Sweden;Northeastern University, Shenyang, China and Uppsala University, Uppsala, Sweden;Northeastern University, Shenyang, China;Zhejiang University, Hangzhou, China;Northeastern University, Shenyang, China

  • Venue:
  • Journal of Systems Architecture: the EUROMICRO Journal
  • Year:
  • 2011

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Abstract

Non-preemptive scheduling is usually considered inferior to preemptive scheduling for time critical systems, because the non-preemptive block would lead to poor task responsiveness. Although this is true in single-processor scheduling, we found by empirical simulation experiments that it is not necessarily the case in multiprocessor scheduling. Additionally, non-preemptive scheduling enjoys other benefits like lower implementation complexity and run-time overhead. So non-preemptive scheduling may be a better alternative compared to preemptive scheduling for a considerable part of real-time applications on multiprocessor/multi-core platforms. As the technical contribution, we study the schedulability analysis problem of global non-preemptive fixed-priority scheduling (NP-FP) on multiprocessors. We propose schedulability test conditions for NP-FP, building upon the ''problem window analysis'' by Baruah [8] for preemptive scheduling. We firstly derive a linear-time general schedulability test condition that works on not only NP-FP, but also any other work-conserving non-preemptive scheduling algorithm. Then we improve the analysis and present a test condition of quadratic time-complexity for NP-FP, which has significant performance improvement comparing to the first one. A notable advantage of our proposed test conditions is, while the test in [8] needs to enumerate for a large number of possible problem window sizes, our proposed test conditions only need to be conducted with a single problem window size, and thereby are significantly more efficient. Experiments with randomly generated task sets are conducted to evaluate the performance of the proposed test conditions.