Collaborative Edge Caching: a Meta Reinforcement Learning Approach with Edge Sampling

TL;DR

This paper introduces a meta reinforcement learning framework with edge sampling for collaborative edge caching, effectively adapting to dynamic content popularity and improving cache hit rates.

Contribution

It proposes a novel meta-learning-based strategy combined with an edge sampling method to enhance local cache adaptation in dynamic environments.

Findings

Improves cache hit rate by up to 10.12%

Effectively adapts to changing content popularity

Demonstrates superior performance over baselines

Abstract

Current learning-based edge caching schemes usually suffer from dynamic content popularity, e.g., in the emerging short video platforms, users' request patterns shift significantly over time and across different edges. An intuitive solution for a specific local edge cache is to collect more request histories from other edge caches. However, uniformly merging these request histories may not perform satisfactorily due to heterogeneous content distributions on different edges. To solve this problem, we propose a collaborative edge caching framework. First, we design a meta-learning-based collaborative strategy to guarantee that the local model can timely meet the continually changing content popularity. Then, we design an edge sampling method to select more "valuable" neighbor edges to participate in the local training. To evaluate the proposed framework, we conduct trace-driven experiments to demonstrate the effectiveness of our design: it improves the average cache hit rate by up to $10.12\%$ (normalized) compared with other baselines.