A task can be preempted by several jobs of higherpriority tasks during its response time. Assuming the worstcasememory demand for each of these jobs leads to pessimistic worstcase response time (WCRT) estimations. Indeed, there is a bigchance that a large portion of the instructions and data associatedwith the preempting task τj are still available in the cache when τj releases its next jobs. Accounting for this observation allowsthe pessimism of WCRT analysis to be significantly reduced,which is not considered by existing work.The four main contributions of this paper are: 1) The conceptof persistent cache blocks is introduced in the context of WCRT analysis, which allows re-use of cache blocks to be captured, 2) A cache-persistence-aware WCRT analysis for fixed-prioritypreemptive systems exploiting the PCBs to reduce the WCRTbound, 3) An multi-set extension of the analysis that furtherimproves the WCRT bound, and 4) An evaluation showing thatour cachepersistence-aware WCRT analysis results in up to 10% higher schedulability than state-of-the-art approaches. Abstract-A task can be preempted by several jobs of higher priority tasks during its response time. Assuming the worst-case memory demand for each of these jobs leads to pessimistic worstcase response time (WCRT) estimations. Indeed, there is a big chance that a large portion of the instructions and data associated with the preempting task τj are still available in the cache when τj releases its next jobs. Accounting for this observation allows the pessimism of WCRT analysis to be significantly reduced, which is not considered by existing work.The four main contributions of this paper are: 1) The concept of persistent cache blocks is introduced in the context of WCRT analysis, which allows re-use of cache blocks to be captured, 2) A cache-persistence-aware WCRT analysis for fixed-priority preemptive systems exploiting the PCBs to reduce the WCRT bound, 3) An multi-set extension of the analysis that further improves the WCRT bound, and 4) An evaluation showing that our cache-persistence-aware WCRT analysis results in up to 10% higher schedulability than state-of-the-art approaches.
Schedulability analysis for tasks running on micro-processors with cache memory is incomplete without a treatment of Cache Related Preemption Delays (CRPD) and CachePersistence Reload Overheads (CPRO). State-of-theart analysescompute CRPD and CPRO independently, which might result incounting the same overhead more than once.In this paper, we analyze the pessimism associated with theindependent calculation of CRPD and CPRO in comparison toan integrated approach. We answer two main questions: (1) Isit beneﬕcial to integrate the calculation of CRPD and CPRO?(2) When and to what extent can we gain in terms of schedu-lability by integrating the calculation of CRPD and CPRO? Toachieve this, we (i) identify situations where considering CRPDand CPRO separately might result in overestimating the totalmemory overhead suffered by tasks, (ii) derive new analyses thatintegrate the calculation of CRPD and CPRO; and (iii) perform athorough experimental evaluation using benchmarks to comparethe performance of the integrated analysis against the separatecalculation of CRPD and CPRO. Abstract-Schedulability analysis for tasks running on microprocessors with cache memory is incomplete without a treatment of Cache Related Preemption Delays (CRPD) and Cache Persistence Reload Overheads (CPRO). State-of-the-art analyses compute CRPD and CPRO independently, which might result in counting the same overhead more than once.In this paper, we analyze the pessimism associated with the independent calculation of CRPD and CPRO in comparison to an integrated approach. We answer two main questions: (1) Is it beneficial to integrate the calculation of CRPD and CPRO? (2) When and to what extent can we gain in terms of schedulability by integrating the calculation of CRPD and CPRO? To achieve this, we (i) identify situations where considering CRPD and CPRO separately might result in overestimating the total memory overhead suffered by tasks, (ii) derive new analyses that integrate the calculation of CRPD and CPRO; and (iii) perform a thorough experimental evaluation using benchmarks to compare the performance of the integrated analysis against the separate calculation of CRPD and CPRO.
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