Blockchain-based Edge Resource Sharing for Metaverse
Zhilin Wang, Qin Hu, Minghui Xu, Honglu Jiang

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…
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Taxonomy
TopicsIoT and Edge/Fog Computing · Blockchain Technology Applications and Security · Privacy-Preserving Technologies in Data
