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We evaluated Intel ® Optane™ DC Persistent Memory and found that Intel's persistent memory is highly sensitive to data locality, size, and access patterns, which becomes clearer by optimizing both virtual memory page size and data layout for locality. Using the Polybench high-performance computing benchmark suite and controlling for mapped page size, we evaluate persistent memory (PMEM) performance relative to DRAM. In particular, the Linux PMEM support maps preferentially maps persistent memory in large pages while always mapping DRAM to small pages. We observed using large pages for PMEM and small pages for DRAM can create a 5x difference in performance, dwarfing other effects discussed in the literature. We found PMEM performance comparable to DRAM performance for the majority of tests when controlled for page size and optimized for data locality. 

The increasing demand of big data analytics for more main memory capacity in datacenters and exascale computing environments is driving the integration of heterogeneous memory technologies. The new technologies exhibit vastly greater differences in access latencies, bandwidth and capacity compared to the traditional NUMA systems. Leveraging this heterogeneity while also delivering application performance enhancements requires intelligent data placement. We present Kleio, a page scheduler with machine intelligence for applications that execute across hybrid memory components. Kleio is a hybrid page scheduler that combines existing, lightweight, history-based data tiering methods for hybrid memory, with novel intelligent placement decisions based on deep neural networks. We contribute new understanding toward the scope of benefits that can be achieved by using intelligent page scheduling in comparison to existing history-based approaches, and towards the choice of the deep learning algorithms and their parameters that are effective for this problem space. Kleio incorporates a new method for prioritizing pages that leads to highest performance boost, while limiting the resulting system resource overheads. Our performance evaluation indicates that Kleio reduces on average 80% of the performance gap between the existing solutions and an oracle with knowledge of future access pattern. Kleio provides hybrid memory systems with fast and effective neural network training and prediction accuracy levels, which bring significant application performance improvements with limited resource overheads, so as to lay the grounds for its practical integration in future systems. 

Server systems with large amounts of physical memory can benefit from using some of the available memory capacity for in-memory snapshots of the ongoing computations. In-memory snapshots are useful for services such as scaling of new workload instances, debugging, during scheduling, etc., which do not require snapshot persistence across node crashes/reboots. Since increasingly more frequently servers run containerized workloads, using technologies such as Docker, the snapshot, and the subsequent snapshot restore mechanisms, would be applied at granularity of containers. However, CRIU, the current approach to snapshot/restore containers, suffers from expensive filesystem write/read operations on image files containing memory pages, which dominate the runtime costs and impact the potential benefits of manipulating in-memory process state. In this paper, we demonstrate that these overheads can be eliminated by using MVAS -- kernel support for multiple independent virtual address spaces (VAS), designed specifically for machines with large memory capacities. The resulting VAS-CRIU stores application memory as a separate snapshot address space in DRAM and avoids costly file system operations. This accelerates the snapshot/restore of address spaces by two orders of magnitude, resulting in an overall reduction in snapshot time by up to 10× and restore time by up to 9×. We demonstrate the utility of VAS-CRIU for container management services such as fine-grained snapshot generation and container instance scaling. 

For hosting data-serving and caching workloads based on key-value stores in clouds, the cost of memory represents a significant portion of the hosting expenses. The emergence of cheaper, but slower, types of memories, such as NVDIMMs, opens opportunities to reduce the hosting costs for such workloads. The question explored in this paper is how to determine adequate allocations of different memory types in future systems with heterogeneous memory components, so as to retain desired performance SLOs and maximize the cost efficiency of the memory resource. We develop Mnemo, a memory sizing and data tiering consultant, that permits quick exploration of the cost-benefit tradeoffs associated with different configurations of the hybrid memory components used by key-value store workloads. Using experimental evaluation with different workload patterns, Mnemo is able to afford applications such as Redis, Memcached and DynamoDB, with substantial reduction in their hosting costs, at negligible impact on application performance, thus improving the overall system memory cost efficiency.