Deterministic Memory Abstraction and Supporting Cache Architecture for Real-Time Systems

Poor timing predictability of multicore processors has been a long-standing challenge in the real-time systems community. In this paper, we make a case that a fundamental problem that prevents efficient and predictable real-time com- puting on multicore is the lack of a proper memory abstraction to express memory criticality, which cuts across various layers of the system: the application, OS, and hardware. We therefore propose a new holistic resource management approach driven by a new memory abstraction, which we call Deterministic Memory. The key characteristic of deterministic memory is that the platform - the OS and hardware - guarantees small and tightly bounded worst-case memory access timing. In contrast, we call the conventional memory abstraction as best-effort memory in which only highly pessimistic worst-case bounds can be achieved. We present how the two memory abstractions can be realized with small extensions to existing OS and hardware architecture. In particular, we show the potential benefits of our approach in the context of shared cache management, by presenting a deterministic memory-aware cache architecture and its manage- ment scheme. We evaluate the effectiveness of the deterministic memory-aware cache management approach compared with a conventional way-based cache partitioning approach, using a set of synthetic and real benchmarks. The results show that our approach achieves (i) the same degree of temporal determinism of traditional way-based cache partitioning for deterministic memory, (ii) while freeing up to 49% of additional cache space, on average, for best-effort memory, and consequently improving the cache hit rate by 39%, on average, for non-real-time workloads. We also discuss how the deterministic memory abstraction can be leveraged in other parts of the memory hierarchy, particularly in the memory controller.