TY - JOUR
T1 - Lightweight Robust Size Aware Cache Management
AU - Einziger, Gil
AU - Eytan, Ohad
AU - Friedman, Roy
AU - Manes, Benjamin
N1 - Publisher Copyright: © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2022/8/25
Y1 - 2022/8/25
N2 - Modern key-value stores, object stores, Internet proxy caches, and Content Delivery Networks (CDN) often manage objects of diverse sizes, e.g., blobs, video files of different lengths, images with varying resolutions, and small documents. In such workloads, size-aware cache policies outperform size-oblivious algorithms. Unfortunately, existing size-aware algorithms tend to be overly complicated and computationally expensive.Our work follows a more approachable pattern; we extend the prevalent (size-oblivious) TinyLFU cache admission policy to handle variable-sized items. Implementing our approach inside two popular caching libraries only requires minor changes. We show that our algorithms yield competitive or better hit-ratios and byte hit-ratios compared to the state-of-the-art size-aware algorithms such as AdaptSize, LHD, LRB, and GDSF. Further, a runtime comparison indicates that our implementation is faster by up to 3× compared to the best alternative, i.e., it imposes a much lower CPU overhead.
AB - Modern key-value stores, object stores, Internet proxy caches, and Content Delivery Networks (CDN) often manage objects of diverse sizes, e.g., blobs, video files of different lengths, images with varying resolutions, and small documents. In such workloads, size-aware cache policies outperform size-oblivious algorithms. Unfortunately, existing size-aware algorithms tend to be overly complicated and computationally expensive.Our work follows a more approachable pattern; we extend the prevalent (size-oblivious) TinyLFU cache admission policy to handle variable-sized items. Implementing our approach inside two popular caching libraries only requires minor changes. We show that our algorithms yield competitive or better hit-ratios and byte hit-ratios compared to the state-of-the-art size-aware algorithms such as AdaptSize, LHD, LRB, and GDSF. Further, a runtime comparison indicates that our implementation is faster by up to 3× compared to the best alternative, i.e., it imposes a much lower CPU overhead.
KW - CDN
KW - Software cache management
KW - size aware caching
KW - storage
UR - http://www.scopus.com/inward/record.url?scp=85141171906&partnerID=8YFLogxK
U2 - https://doi.org/10.1145/3507920
DO - https://doi.org/10.1145/3507920
M3 - Article
SN - 1553-3077
VL - 18
JO - ACM Transactions on Storage
JF - ACM Transactions on Storage
IS - 3
M1 - 27
ER -