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.

翻译：高效的缓存管理对于优化系统性能至关重要，已有多种缓存机制被提出，它们各自探索了不同的插入与淘汰策略。本文提出了AdaptiveClimb及其扩展版本DynamicAdaptiveClimb，这两种新颖的缓存替换策略利用轻量级的缓存自适应能力，超越了传统方法。与经典的最近最少使用（LRU）和增量排名进展（CLIMB）策略不同，AdaptiveClimb基于近期的命中与缺失模式动态调整缓存对象的提升距离（跳跃），仅需一个可调参数且无需逐项统计。这使得策略能够快速适应变化的访问分布，同时保持较低的开销。在此基础上，DynamicAdaptiveClimb通过根据工作负载需求自动调整缓存大小，进一步增强了自适应能力。我们在多样化的真实世界轨迹上进行了全面评估，包括来自6个不同数据集的1067条轨迹，结果表明DynamicAdaptiveClimb相较于其他先进算法，始终能实现显著的加速和更高的命中率。具体而言，与先进先出（FIFO）基线相比，我们的方法在命中率上实现了高达29%的提升，并显著降低了缺失惩罚。此外，它在工作集大小波动的环境中，尤其优于次优的竞争者AdaptiveClimb和SIEVE [43]，优势约为10%至15%。这些结果凸显了我们方法在提供高效性能方面的有效性，使其特别适用于现代动态缓存环境。