Caching is extensively used in various networking environments to optimize performance by reducing latency, bandwidth, and energy consumption. To optimize performance, caches often advertise their content using indicators, which are data structures that trade space efficiency for accuracy. However, this tradeoff introduces the risk of false indications. Existing solutions for cache content advertisement and cache selection often lead to inefficiencies, failing to adapt to dynamic network conditions. This paper introduces SALSA2, a Scalable Adaptive and Learning-based Selection and Advertisement Algorithm, which addresses these limitations through a dynamic and adaptive approach. SALSA2 accurately estimates mis-indication probabilities by considering inter-cache dependencies and dynamically adjusts the size and frequency of indicator advertisements to minimize transmission overhead while maintaining high accuracy. Our extensive simulation study, conducted using a variety of real-world cache traces, demonstrates that SALSA2 achieves up to 84\% bandwidth savings compared to the state-of-the-art solution and close-to-optimal service cost in most scenarios. These results highlight SALSA2's effectiveness in enhancing cache management, making it a robust and versatile solution for modern networking challenges.

翻译：缓存技术广泛应用于各类网络环境中，通过降低延迟、减少带宽消耗和节约能耗来优化系统性能。为提升性能，缓存节点通常使用指示器来通告其内容，这类数据结构以空间效率换取准确性，但此种权衡会引入误指示风险。现有的缓存内容通告与缓存选择方案常导致效率低下，难以适应动态变化的网络条件。本文提出SALSA2——一种可扩展的自适应学习型选择与通告算法，通过动态自适应机制解决上述局限。SALSA2通过考虑缓存间的依赖关系精确估计误指示概率，并动态调整指示器通告的规模与频率，在保持高准确性的同时最小化传输开销。我们基于多种真实世界缓存轨迹开展的广泛仿真研究表明，相较于现有最优方案，SALSA2可实现高达84%的带宽节约，在多数场景下达到接近最优的服务成本。这些结果凸显了SALSA2在增强缓存管理方面的有效性，使其成为应对现代网络挑战的鲁棒且通用的解决方案。