IoT-Cache: Caching Transient Data at the IoT Edge

Abstract

Explosive traffic and service delay are bottlenecks in providing Quality of Service (QoS) to the Internet of Things (IoT) end-users. Edge caching emerged as a promising solution, but data transiency, limited caching capability, and network volatility trigger the dimensionality curse. Therefore, we propose a Deep Reinforcement Learning (DRL) approach, named IoT-Cache, to caching action optimization. An appropriate reward function is designed to increase the cache hit rate and optimize the overall data-cache allocation. A practical scenario with inconsistent requests and data item sizes is considered, and a Distributed Proximal Policy Optimization (DPPO) algorithm is proposed, enabling IoT edge nodes to learn caching policy. RLlib framework is used to scale the training in distributed Publish/Subscribe network. The performance evaluation demonstrates a significant improvement and faster convergence for IoT-Cache cost function, a trade-off between communication cost and data freshness over existing DRL and baseline caching solutions.