DynamicAdaptiveClimb: Adaptive Cache Replacement with Dynamic Resizing

TL;DR

This paper introduces DynamicAdaptiveClimb, a novel cache replacement policy that dynamically adjusts cache size and promotion strategies to improve hit ratios and performance in diverse, changing workloads.

Contribution

The paper proposes DynamicAdaptiveClimb, an adaptive cache replacement policy that automatically tunes cache size and promotion parameters for improved performance.

Findings

Achieves up to 29% higher hit ratio compared to FIFO baseline.

Outperforms AdaptiveClimb and SIEVE by 10-15% in fluctuating workloads.

Demonstrates effectiveness across 1067 real-world traces from six datasets.

Abstract

Efficient cache management is critical for optimizing the system performance, and numerous caching mechanisms have been proposed, each exploring various insertion and eviction strategies. In this paper, we present AdaptiveClimb and its extension, DynamicAdaptiveClimb, two novel cache replacement policies that leverage lightweight, cache adaptation to outperform traditional approaches. Unlike classic Least Recently Used (LRU) and Incremental Rank Progress (CLIMB) policies, AdaptiveClimb dynamically adjusts the promotion distance (jump) of the cached objects based on recent hit and miss patterns, requiring only a single tunable parameter and no per-item statistics. This enables rapid adaptation to changing access distributions while maintaining low overhead. Building on this foundation, DynamicAdaptiveClimb further enhances adaptability by automatically tuning the cache size in response to workload demands. Our comprehensive evaluation across a diverse set of real-world traces, including 1067 traces from 6 different datasets, demonstrates that DynamicAdaptiveClimb consistently achieves substantial speedups and higher hit ratios compared to other state-of-the-art algorithms. In particular, our approach achieves up to a 29% improvement in hit ratio and a substantial reduction in miss penalties compared to the FIFO baseline. Furthermore, it outperforms the next-best contenders, AdaptiveClimb and SIEVE [43], by approximately 10% to 15%, especially in environments characterized by fluctuating working set sizes. These results highlight the effectiveness of our approach in delivering efficient performance, making it well-suited for modern, dynamic caching environments.