Browsing by Author "Eom, Young Ik"

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    MEMTIS: Efficient Memory Tiering with Dynamic Page Classification and Page Size Determination
    Lee, Taehyung; Monga, Sumit Kumar; Min, Changwoo; Eom, Young Ik (ACM, 2023-10-23)
    The evergrowing memory demand fueled by datacenter workloads is the driving force behind new memory technology innovations (e.g., NVM, CXL). Tiered memory is a promising solution which harnesses such multiple memory types with varying capacity, latency, and cost characteristics in an effort to reduce server hardware costs while fulfilling memory demand. Prior works on memory tiering make suboptimal (often pathological) page placement decisions because they rely on various heuristics and static thresholds without considering overall memory access distribution. Also, deciding the appropriate page size for an application is difficult as huge pages are not always beneficial as a result of skewed accesses within them. We present Memtis, a tiered memory system that adopts informed decision-making for page placement and page size determination. Memtis leverages access distribution of allocated pages to optimally approximate the hot data set to the fast tier capacity. Moreover, Memtis dynamically determines the page size that allows applications to use huge pages while avoiding their drawbacks by detecting inefficient use of fast tier memory and splintering them if necessary. Our evaluation shows that Memtis outperforms state-of-the-art tiering systems by up to 169.0% and their best by up to 33.6%.
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    PRISM: Optimizing Key-Value Store for Modern Heterogeneous Storage Devices
    Song, Yongju; Kim, Wook-Hee; Monga, Sumit Kumar; Min, Changwoo; Eom, Young Ik (ACM, 2023-01-27)
    As data generation has been on an upward trend, storing vast volumes of data cost-effectively as well as efficiently accessing them is paramount. At the same time, today’s storage landscape continues to diversify, from high-bandwidth storage devices such as NVMe SSDs to low-latency non-volatile memory (e.g., Intel Optane DCPMM). These heterogeneous storage devices have the potential to deliver high performance in terms of bandwidth and latency with cost efficiency, while achieving the performance and cost targets together still remains a challenging problem. We provide our solution, PRISM, a novel key-value store that utilizes modern heterogeneous storage devices. PRISM uses heterogeneous storage devices synergistically to harness the advantages of each storage device while suppressing their downsides. We devise new techniques to balance the latency-bandwidth tradeoff when reading from SSD. For ensuring multicore scalability and crash consistency of data across heterogeneous storage media, PRISM proposes cross-storage concurrency control and cross-storage crash consistency protocols. Our evaluation shows that PRISM outperforms state-of-the-art key-value stores by up to 13.1× with significantly lower tail latency.