Modeling and Optimizing Latency for Delayed Hit Caching with Stochastic Miss Latency

Fuente: arXiv
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Auteurs principaux: Jiang, Bowen, Ma, Chaofan
Format: Preprint
Publié: 2025
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author Jiang, Bowen
Ma, Chaofan
author_facet Jiang, Bowen
Ma, Chaofan
contents Caching is crucial for system performance, but the delayed hit phenomenon, where requests queue during lengthy fetches after a cache miss, significantly degrades user-perceived latency in modern high-throughput systems. While prior works address delayed hits by estimating aggregate delay, they universally assume deterministic fetch latencies. This paper tackles the more realistic, yet unexplored, scenario where fetch latencies are stochastic. We present, to our knowledge, the first theoretical analysis of delayed hits under this condition, deriving analytical expressions for both the mean and variance of the aggregate delay assuming exponentially distributed fetch latency. Leveraging these insights, we develop a novel variance-aware ranking function tailored for this stochastic setting to guide cache eviction decisions more effectively. The simulations on synthetic and real-world datasets demonstrate that our proposed algorithm significantly reduces overall latency compared to state-of-the-art delayed-hit strategies, achieving a $3\%-30\%$ reduction on synthetic datasets and approximately $1\%-7\%$ reduction on real-world traces.
format Preprint
id arxiv_https___arxiv_org_abs_2505_15531
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Modeling and Optimizing Latency for Delayed Hit Caching with Stochastic Miss Latency
Jiang, Bowen
Ma, Chaofan
Networking and Internet Architecture
Caching is crucial for system performance, but the delayed hit phenomenon, where requests queue during lengthy fetches after a cache miss, significantly degrades user-perceived latency in modern high-throughput systems. While prior works address delayed hits by estimating aggregate delay, they universally assume deterministic fetch latencies. This paper tackles the more realistic, yet unexplored, scenario where fetch latencies are stochastic. We present, to our knowledge, the first theoretical analysis of delayed hits under this condition, deriving analytical expressions for both the mean and variance of the aggregate delay assuming exponentially distributed fetch latency. Leveraging these insights, we develop a novel variance-aware ranking function tailored for this stochastic setting to guide cache eviction decisions more effectively. The simulations on synthetic and real-world datasets demonstrate that our proposed algorithm significantly reduces overall latency compared to state-of-the-art delayed-hit strategies, achieving a $3\%-30\%$ reduction on synthetic datasets and approximately $1\%-7\%$ reduction on real-world traces.
title Modeling and Optimizing Latency for Delayed Hit Caching with Stochastic Miss Latency
topic Networking and Internet Architecture
url https://arxiv.org/abs/2505.15531