  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.
