Main memory dominates data center server cost, and hence data center operators are exploring alternative technologies such as CXL-attached and persistent memory to improve cost without jeopardizing performance. Introducing multiple tiers of memory introduces new challenges, such as selecting the appropriate memory configuration for a given workload mix. In particular, we observe that inefficient configurations increase cost by up to 2.6× for clients, and resource stranding increases cost by 2.2× for cloud operators. To address this challenge, we introduce TMC, a system for recommending cloud configurations according to workload characteristics and the dynamic resource utilization of a cluster. Whereas prior work utilized extensive simulation or costly machine learning techniques, incurring significant search costs, our approach profiles applications to reveal internal properties that lead to fast and accurate performance estimations. Our novel configuration-selection algorithm incorporates a new heuristic, packing penalty, to ensure that recommended configurations will also achieve good resource efficiency. Our experiments demonstrate that TMC reduces the search cost by up to 4× over the state-of-the-art, while improving resource utilization by up to 17% as compared to a naive policy that requests optimal tiered memory allocations in isolation.

Non-Volatile Random Access Memory (NVRAM) technologies are closing the performance gap between traditional storage and memory. However, the integrity of persistent data structures after an unclean shutdown remains a major concern. Logging is commonly used to ensure consistency of NVRAM systems, but it imposes significant performance overhead and causes additional wear out by writing extra data into NVRAM. Our goal is to eliminate the extra writes that are needed to achieve consistency. SSP (i) exploits a novel cache-line-level remapping mechanism to eliminate redundant data copies in NVRAM, (ii) minimizes the storage overheads using page consolidation and (iii) removes failure-atomicity overheads from the critical path, significantly improving the performance of NVRAM systems. Our evaluation results demonstrate that SSP reduces overall write traffic by up to 1.8×, reduces extra NVRAM writes in the critical path by up to 10× and improves transaction throughput by up to 1.6×, compared to a state-of-the-art logging design.

Current data-center applications tend to process increasingly large volume of data sets. The caching effect of page cache is reduced by its limited capacity. Emerging flash-based solid state drives (SSD) have latency and price advantages compared to hard disk and DRAM. Thus, SSD-based caching is widely deployed in data centers. However, SSD caching faces two challenges. First, SSD has limited write endurance, which requires cache manager to reduce write amount to SSD. Second, data-center workloads exhibit a diverse I/O access patterns, which requires one to figure out SSD caching friendly access patterns. This paper first classifies 6 I/O access patterns among 32 data-center workloads using a cost-benefit analysis. We derive implications for designing SSD cache from analyzing the access patterns. We then propose an SSD cache manager S-RAC with re-adding blocks and ghost cache adaptation to retain SSD friendly blocks in SSD. The experimental evaluation shows the efficiency of S-RAC in reducing SSD write amount while improving/maintaining cache hit ratio.