Edge Caching Optimization with PPO and Transfer Learning for Dynamic Environments

• URL: http://arxiv.org/abs/2411.09812v1
• Date: Thu, 14 Nov 2024 21:01:29 GMT
• Title: Edge Caching Optimization with PPO and Transfer Learning for Dynamic Environments
• Authors: Farnaz Niknia, Ping Wang,
• Abstract summary: In dynamic environments, changes in content popularity and variations in request rates frequently occur, making previously learned policies less effective as they were optimized for earlier conditions. We develop a mechanism that detects changes in content popularity and request rates, ensuring timely adjustments to the caching strategy. We also propose a transfer learning-based PPO algorithm that accelerates convergence in new environments by leveraging prior knowledge.
• Score: 3.720975664058743
• License:
• Abstract: This paper addresses the challenge of edge caching in dynamic environments, where rising traffic loads strain backhaul links and core networks. We propose a Proximal Policy Optimization (PPO)-based caching strategy that fully incorporates key file attributes such as size, lifetime, importance, and popularity, while also considering random file request arrivals, reflecting more realistic edge caching scenarios. In dynamic environments, changes such as shifts in content popularity and variations in request rates frequently occur, making previously learned policies less effective as they were optimized for earlier conditions. Without adaptation, caching efficiency and response times can degrade. While learning a new policy from scratch in a new environment is an option, it is highly inefficient and computationally expensive. Thus, adapting an existing policy to these changes is critical. To address this, we develop a mechanism that detects changes in content popularity and request rates, ensuring timely adjustments to the caching strategy. We also propose a transfer learning-based PPO algorithm that accelerates convergence in new environments by leveraging prior knowledge. Simulation results demonstrate the significant effectiveness of our approach, outperforming a recent Deep Reinforcement Learning (DRL)-based method.

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