A Finer-Grained Blocking Analysis for Parallel Real-Time Tasks with Spin-Locks

Real-time synchronization is one of the essential theories in real-time systems, and the recent booming of parallel real-time tasks has brought new challenges to the synchronization analysis. As the easy implementation and negligible overheads, spin-locks have received much interest since the study for sequential tasks. However, existing spin-based blocking analyses for parallel tasks are relied on execution-time inflation, and the substantially more accurate inflation-free analysis has not been fathomed yet. Moreover, existing analyses suffer an overrepresentation problem, which can be further exacerbated for parallel tasks with spin-locks. To overcome such pessimism, we propose an improved blocking analysis for non-preemptive spin-locks based on a finer-grained shared resource model. In particular, we consider individual length for each shared resource request and use the state-of-the-art linear optimization technique to achieve a pinpoint inflation-free analysis. Empirical evaluations show that the proposed analysis dominated other state-of-the-art analysis, which further shows the improved accuracy achieved by the proposed approach.