Resource Sharing in Multicore Mixed-Criticality Systems: Utilization Bound and Blocking Overhead

被引:15
|
作者
Han, Jian-Jun [1 ]
Tao, Xin [1 ]
Zhu, Dakai [2 ]
Yang, Laurence T. [1 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Hubei, Peoples R China
[2] Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
基金
中国国家自然科学基金; 美国国家科学基金会;
关键词
Mixed-criticality systems; multicore; shared resources; resource access contention; utilization bound; run-time overhead; REAL-TIME SYSTEMS; TASK SYNCHRONIZATION; MULTIPROCESSORS; LOCKING;
D O I
10.1109/TPDS.2017.2677442
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In mixed-criticality (MC) system, diverse application activities with various certification requirements (different criticality) can share a computing platform, where multicore processors have emerged as the prevailing computing engines. Focusing on the problem of resource access contention in multicore MC systems, we analyze the synchronization issues and blocking characteristics of the Multiprocessor Stack Resource Policy (MSRP) with both priority and criticality inversions among MC tasks being considered. We develop the first criticality-aware utilization bound under partitioned Earliest Deadline First (EDF) and MSRP by taking the worst case synchronization overheads of tasks into account. The non-monotonicity of the bound where it may decrease when more cores are deployed is identified, which can cause anomalies in the feasibility tests. With the objective to improve system schedulability, a novel criticality-cognizant and resource-oriented analysis approach is further studied to tighten the bound on the synchronization overheads for MC tasks scheduled under partitioned EDF and MSRP. The simulation results show that the new analysis approach can effectively reduce the blocking times for tasks (up to 30 percent) and thus improve the schedulability ratio (e.g., 10 percent more). The actual implementation in Linux kernel further shows the practicability of partitioned-EDF with MSRP (with run-time overhead being about 3 to 7 percent of the overall execution time) for MC tasks running on multicores with shared resources.
引用
收藏
页码:3626 / 3641
页数:16
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