Cache partitioning techniques have been successfully adopted to mitigate interference among concurrently executing real-time tasks on multi-core processors. Considering that the execution time of a cache-sensitive task strongly depends on the cache available for it to use, co-optimizing cache partitioning and task allocation improves the system's schedulability. In this paper, we propose a hybrid multi-layer design space exploration technique to solve this multi-resource management problem. We explore the interplay between cache partitioning and schedulability by systematically interleaving three optimization layers, viz., (i) in the outer layer, we perform a breadth-first search combined with proactive pruning for cache partitioning; (ii) in the middle layer, we exploit a first-fit heuristic for allocating tasks to cores; and (iii) in the inner layer, we use the well-known recurrence relation for the schedulability analysis of non-preemptive fixed-priority (NP-FP) tasks in a uniprocessor setting. Although our focus is on NP-FP scheduling, we evaluate the flexibility of our framework in supporting different scheduling policies (NP-EDF, P-EDF) by plugging in appropriate analysis methods in the inner layer. Experiments show that, compared to the state-of-the-art techniques, the proposed framework can improve the real-time schedulability of NP-FP task sets by an average of 15.2% with a maximum improvement of 233.6% (when tasks are highly cache-sensitive) and a minimum of 1.6% (when cache sensitivity is low). For such task sets, we found that clustering similar-period (or mutually compatible) tasks often leads to higher schedulability (on average 7.6%) than clustering by cache sensitivity. In our evaluation, the framework also achieves good results for preemptive and dynamic-priority scheduling policies.

翻译：缓存分区技术已被成功应用于减少多核处理器上并发实时任务之间的干扰。考虑到缓存敏感型任务的执行时间高度依赖于其可用的缓存容量，协同优化缓存分区与任务分配能够提升系统的可调度性。本文提出一种混合多层设计空间探索技术来解决这一多资源管理问题。我们通过系统性地交织三个优化层来探索缓存分区与可调度性之间的相互作用，即：(i) 在外层，我们采用结合主动剪枝的广度优先搜索进行缓存分区；(ii) 在中间层，我们利用首次适应启发式算法将任务分配到核心；(iii) 在内层，我们使用经典的递推关系对单处理器环境下的非抢占式固定优先级（NP-FP）任务进行可调度性分析。尽管本文重点聚焦于NP-FP调度，但我们通过在内层植入相应的分析方法，评估了框架支持不同调度策略（NP-EDF、P-EDF）的灵活性。实验表明，与现有最先进技术相比，所提框架可将NP-FP任务集的实时可调度性平均提升15.2%，最大提升233.6%（当任务高度缓存敏感时），最小提升1.6%（当缓存敏感度较低时）。对于此类任务集，我们发现将相似周期（或相互兼容）的任务聚类通常比按缓存敏感性聚类能获得更高的可调度性（平均提升7.6%）。在评估中，该框架对抢占式和动态优先级调度策略也取得了良好效果。