Blockchain-based Edge Resource Sharing for Metaverse

TL;DR

This paper presents a blockchain-based mobile edge computing platform for resource sharing in the Metaverse, utilizing a learning-based task allocation algorithm to efficiently manage heterogeneous resources and improve service access.

Contribution

It introduces a novel blockchain-enabled MEC system with a reinforcement learning approach for optimal task offloading in the Metaverse context.

Findings

The proposed system effectively improves resource utilization.

The learning-based algorithm achieves polynomial-time task allocation.

Experiments demonstrate enhanced efficiency and effectiveness.

Abstract

Although Metaverse has recently been widely studied, its practical application still faces many challenges. One of the severe challenges is the lack of sufficient resources for computing and communication on local devices, resulting in the inability to access the Metaverse services. To address this issue, this paper proposes a practical blockchain-based mobile edge computing (MEC) platform for resource sharing and optimal utilization to complete the requested offloading tasks, given the heterogeneity of servers' available resources and that of users' task requests. To be specific, we first elaborate the design of our proposed system and then dive into the task allocation mechanism to assign offloading tasks to proper servers. To solve the multiple task allocation (MTA) problem in polynomial time, we devise a learning-based algorithm. Since the objective function and constraints of MTA are significantly affected by the servers uploading the tasks, we reformulate it as a reinforcement learning problem and calculate the rewards for each state and action considering the influences of servers. Finally, numerous experiments are conducted to demonstrate the effectiveness and efficiency of our proposed system and algorithms.