Adopting Deep Reinforcement Learning in Edge Caching Strategy
Year: 2024
Type: conference-paper
Abstract: The increasing demand for digital content is putting a strain on network infrastructures, prompting a shift towards edge server architecture, which aims to decentralize data processing and reduce content delivery latency. This study investigates edge server caching optimization using Deep Reinforcement Learning (DRL). The motivation stems from the need to alleviate the burden on origin servers and to expedite content delivery. An edge server locates closer to the users in a network rather than relying on a centralized server. DRL contains an agent learns to make decisions through trial and error. Experiments conducted across different simulated user request patterns show that modifications can enhance cache hit rates.
Authors: Haolin Feng, Xinfeng Ye, Sathiamoorthy Manoharan
Institution: University of Auckland
Language: English
Topic: Caching and Content Delivery
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405709887