Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning framework for dynamic resource management in the Metaverse, improving revenue and request acceptance rates by effectively grouping applications and learning optimal policies.

Contribution

It presents a novel semi-Markov decision process-based approach for real-time resource allocation and application grouping in Metaverse environments, enhancing efficiency and revenue.

Findings

Achieves up to 120% higher revenue for service providers.

Increases application request acceptance probability by 178.9%.

Demonstrates effectiveness through extensive simulations.

Abstract

This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Specifically, by studying functions of Metaverse applications, we first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications to enhance resource usage efficiency. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision process-based framework and propose an intelligent algorithm that can gradually learn the optimal admission policy to maximize the revenue and resource usage efficiency for the Metaverse service provider and at the same time enhance the Quality-of-Service for Metaverse users. Extensive simulation results show that our proposed approach can achieve up to 120% greater revenue for the Metaverse service providers and up to 178.9% higher acceptance probability for Metaverse application requests than those of other baselines.