Optimal Miner Allocation Scheme for Sub-metaverses:From Multi-knapsack Problem Perspective
Metaverses is a new type of internet social ecosystem that promotes user interaction,provides virtual services,and enables digital asset transactions.Blockchain,as the underlying technology of metaverses,supports the circulation of digital assets such as Non-Fungible Token(NFT)within the metaverse.However,the increase in consensus nodes can decrease the consensus efficiency of digital asset transactions.Therefore,a multi-metaverse digital assets transaction management framework based on edge computing and cross-chain technology is proposed.Firstly,cross-chain technology is utilized to connect multiple sub-metaverses into a multi sub-metaverse system.Secondly,edge devices are allocated as miners in various sub-metaverses,contributing idle computational resources to enhance the efficiency of digital asset transactions.Additionally,the paper models the edge device allocation problem as a multi-knapsack problem and designs a miner selection approach.To address the dynamic allocation problem caused by environmental changes,the Deep Reinforcement Learning Proximal Policy Optimization(DRL-PPO)algorithm from deep reinforcement learning is employed.Simulation results demonstrate the effectiveness of the proposed scheme in achieving secure,efficient,and flexible cross-chain NFT transactions and sub-metaverse management.
万方数据
维普
