Modern workloads tend to have huge working sets and low locality. Despite this trend, the capacity of DRAM has not been increased enough to accommodate such huge working sets. Therefore, memory management mechanisms optimized for such modern workloads are widely required today. For such optimizations, knowing the data access pattern of given workloads is essential. However, manually extracting such patterns from huge and complex workloads is exhaustive. Worse yet, existing memory access analysis tools incur unacceptably high overheads for unnecessarily detailed analysis results. To mitigate this situation, we introduce a tool that is designed for data access pattern tracing. Two core mechanisms in this tool, a region-based sampling and an adaptive region adjustment, allow users to limit the tracing overhead in a bounded range regardless of the size and complexity of target workloads, while preserving the quality of results. Our empirical evaluations that conducted with 20 realistic workloads show the high quality, low overhead, and a potential use case of this tool.

Most modern multi-core processors provide a shared last level cache (LLC) where data from all cores are placed to improve performance. However, this opens a new challenge for cache management, owing to cache pollution. With cache pollution, data with weak temporal locality can evict other data with strong temporal locality when both are mapped into the same cache set. In this article, we propose page reusability-based cache partitioning (PRCP) for multi-core systems to maximize cache utilization by minimizing cache pollution. To achieve this, PRCP divides pages into two groups: (1) highly-reused pages and (2) lowly-reused pages. The reusability of each page is collected online via periodic page table scans. PRCP then dynamically partitions the shared cache into two corresponding areas using page coloring technique. We have implemented PRCP in Linux kernel and evaluated it using SPEC CPU2006 benchmarks. The results show that our scheme can achieve comparable performance to the optimal offline MRC-guided process-based cache partitioning scheme without a priori knowledge of workloads.

ZNS SSDs (Zoned Namespace SSD) are block devices that provide stable performance and low price by forcing sequential writes, however their users have to pay the price to guarantee strong write order. In addition, to get the best performance from small zoned ZNS SSDs that give the users control over device-internal parallel elements, the users need to utilize the SSDs in detail. Due to these overheads, Ceph, one of the distributed storage systems, has up to 69% lower performance when using ZNS SSDs compared to using legacy SSD. In this paper, we present zCeph which solves the problems that occur when using small zoned ZNS SSD in storage systems. We implemented zCeph based on legacy Ceph and evaluated it using synthesized and real-world workloads, showing that the performance improved by up to 4.1x and 7x, respectively, compared to the legacy Ceph using ZNS SSD.