MetaShard: A Novel Sharding Blockchain Platform for Metaverse Applications

TL;DR

MetaShard introduces a sharding blockchain framework tailored for Metaverse applications, enhancing scalability, security, and resource incentives through innovative consensus and management schemes.

Contribution

The paper presents MetaShard, a novel sharding blockchain with a new consensus mechanism and an efficient sharding management scheme optimized for Metaverse needs.

Findings

Achieves up to 66.6% higher throughput than existing solutions.

Provides polynomial-time solutions for complex optimization problems.

Demonstrates effective shard reconfiguration and security enhancements.

Abstract

Due to its security, transparency, and flexibility in verifying virtual assets, blockchain has been identified as one of the key technologies for Metaverse. Unfortunately, blockchain-based Metaverse faces serious challenges such as massive resource demands, scalability, and security concerns. To address these issues, this paper proposes a novel sharding-based blockchain framework, namely MetaShard, for Metaverse applications. Particularly, we first develop an effective consensus mechanism, namely Proof-of-Engagement, that can incentivize MUs' data and computing resource contribution. Moreover, to improve the scalability of MetaShard, we propose an innovative sharding management scheme to maximize the network's throughput while protecting the shards from 51% attacks. Since the optimization problem is NP-complete, we develop a hybrid approach that decomposes the problem (using the binary search method) into sub-problems that can be solved effectively by the Lagrangian method. As a result, the proposed approach can obtain solutions in polynomial time, thereby enabling flexible shard reconfiguration and reducing the risk of corruption from the adversary. Extensive numerical experiments show that, compared to the state-of-the-art commercial solvers, our proposed approach can achieve up to 66.6% higher throughput in less than 1/30 running time. Moreover, the proposed approach can achieve global optimal solutions in most experiments.