Search arXiv⌕ Search

arXiv · 2609.30468

SR-Gadgets: Make Scan-Resistant Caching Practical

Abstract

Block caches commonly serve scan-heavy I/O workloads, motivating extensive studies on scan-resistant eviction algorithms. Many of these algorithms adopt a multi-queue structure. However, they focus primarily on one-time scans and do not handle repeated scans well. Two important challenges from repeated scans are miss-ratio cliffs, where a small increase in cache size sharply reduces the miss ratio, and Belady's anomalies, where increasing the cache size increases the miss ratio. In this paper, we first develop two quantitative metrics to measure these behaviors. With these metrics, we find that LIRS is the only multi-queue algorithm that is scan-resistant (almost cliff- and anomaly-free). Contrary to conventional wisdom, we show that stack distance is not the secret sauce that makes LIRS scan-resistant. Instead, regulating the queues are the key to its scan resistance. Based on these insights, we design the \gadgetprefix Gadgets, easy-to-integrate augmentations that make existing algorithms scan-resistant without changing their eviction heuristics or queue structures. We implement the \gadgetprefix Gadgets in five algorithms: S3-FIFO, SIEVE, ARC, 2Q, and TinyLFU, and make them scan-resistant. Evaluated on 5,538 production traces, all augmented algorithms outperform their base versions, reducing miss ratios by up to 23.1% while consistently reducing cliffs and Belady's anomalies across the production traces.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yunjia Zheng, Juncheng Yang. 2026-09-24. SR-Gadgets: Make Scan-Resistant Caching Practical. https://arxiv.org/abs/2609.30468

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Efficient Fault Localization in a Cloud Stack Using End-to-End Application Service Topology

Cloud application services are distributed and have components across the stack working together to deliver the end-user experience. The widespread adoption of microservice architecture exacerbates failure management due to the increased number of service components. To be effective, strategies to enhance application service resilience need to be autonomous and developed at the service granularity, considering its end-to-end components. However, the massive amount of observability data generated by all these components across the service stack poses a significant challenge in reacting to anomalies and restoring the service quality in real time. Towards this end, the article presents the Topology-Aware Root Cause Detection (TA-RCD) algorithm, which leverages the end-to-end application service topology to accurately pinpoint root causes and facilitate swift remediation of cloud service anomalies. Additionally, because effective root-cause localization depends on identifying the most informative observability signals across the cloud service stack, this article presents a topology-guided metric selection strategy that selects relevant metrics from end-to-end layers to enable efficient, explainable, and accurate anomaly remediation. TA-RCD is comprehensively evaluated on the public RCAEval benchmark and a controlled in-house testbed. Experimental results show that the proposed approach consistently achieves higher average Top-3 and Top-5 localization accuracy than state-of-the-art baselines, improving AC@5 by at least 2$\times$ and AC@3 by at least 4$\times$ over standard RCD in the controlled testbed. On the public RCAEval benchmark, TA-RCD further improves metric-level AC@5 by 18\% and Avg@5 by 19\% over the strongest baseline.

cs.PF↗

Tandem Queues with Hybrid Abandonment in Heavy Traffic

Hybrid-abandonment systems, where jobs abandon from certain stages of service but not others, occur frequently in medical, service, and computing systems. Abandonment affects the heavy-traffic behavior of both the stages with and without abandonment. In contrast to existing heavy-traffic works that focus on cases where there is abandonment at either all stages or no stages, we focus on the heavy-traffic behavior for these hybrid-abandonment systems. We provide the first heavy-traffic characterization of the second queue length in a two-stage tandem queue with abandonment only in the first stage. We characterize the scaled mean of the second stage queue length and prove that the scaled second queue length is asymptotically exponentially distributed and asymptotically independent of the first stage queue length. We derive our results by analyzing Markovian arrival process queues with countably infinite phases. We do this by extending techniques used to analyze queues with Markov-modulated arrivals to the broader class of infinite Markovian arrival process queues. We prove asymptotic results for a more general infinite phase process condition, an infinite birth-death phase process, and the hybrid-abandonment tandem case. We also empirically demonstrate that our heavy-traffic approximations are good approximations across various system settings.

cs.PF↗

Outperforming Multiserver SRPT at All Loads

A well-designed scheduling policy can unlock significant performance improvements with no additional resources. Multiserver SRPT (SRPT-k) is known to achieve asymptotically optimal mean response time in the heavy-traffic limit, as load approaches capacity. No better policy is known for the M/G/k queue in any regime. We introduce a new policy, SRPT-Except-k+1 & Modified SRPT (SEK-SMOD), which is the first policy to provably achieve lower mean response time than SRPT-k. SEK-SMOD outperforms SRPT-k across all loads and all job size distributions. The key idea behind SEK-SMOD is to prioritize large jobs over small jobs in specific scenarios to improve server utilization, and thereby improve the response time of subsequent jobs in expectation. Our proof is a novel application of hybrid worst-case and stochastic techniques to relative analysis, where we analyze the deviations of our proposed SEK-SMOD policy away from the SRPT-k baseline policy. Furthermore, we design Practical SEK (a simplified variant of SEK-SMOD) and empirically verify the improvement over SRPT-k via simulation.

cs.PF↗