Lo, Chih TingChih TingLoChen, Yun ChihYun ChihChenChang, Yuan HaoYuan HaoChangTEI-WEI KUO2024-01-312024-01-312023-01-01979835031559210923152https://scholars.lib.ntu.edu.tw/handle/123456789/639413In-memory index delivers low-latency responses for data services. It has been ported to high-capacity persistent memory (PM) to accommodate more data. However, read-heavy, extremely-skewed, and highly-dynamic workloads can suffer from degraded performance on PM-based indexes. We present HAPIC, a scalable cache over PM-based indexes to capture the constantly-changing query hotspots in skewed workloads. HAPIC embodies the data access frequency gradient in a hierarchy of hash tables to efficiently identify hotspots and reacts quickly to workload changes with epoch-based promotion. Compared with the state-of-the-art strategy, HAPIC reacts to hotspot shifts significantly faster, with up to 14% higher stable read throughput, 26% lower median latency, and 13% lower P99 latency.conference paper10.1109/ICCAD57390.2023.103236082-s2.0-85181400614https://api.elsevier.com/content/abstract/scopus_id/85181400614