The computing in the network (COIN) paradigm has emerged as a potential solution for computation-intensive applications like the metaverse by utilizing unused network resources. The blockchain (BC) guarantees task-offloading privacy, but cost reduction, queueing delays, and redundancy elimination remain open problems. This paper presents a redundancy-aware BC-based approach for the metaverse's partial computation offloading (PCO). Specifically, we formulate a joint BC redundancy factor (BRF) and PCO problem to minimize computation costs, maximize incentives, and meet delay and BC offloading constraints. We proved this problem is NP-hard and transformed it into two subproblems based on their temporal correlation: real-time PCO and Markov decision process-based BRF. We formulated the PCO problem as a multiuser game, proposed a decentralized algorithm for Nash equilibrium under any BC redundancy state, and designed a double deep Q-network-based algorithm for the optimal BRF policy. The BRF strategy is updated periodically based on user computation demand and network status to assist the PCO algorithm. The experimental results suggest that the proposed approach outperforms existing schemes, resulting in a remarkable 47% reduction in cost overhead, delivering approximately 64% higher rewards, and achieving convergence in just a few training episodes.

翻译：网络内计算（COIN）范式通过利用闲置网络资源，为元宇宙等计算密集型应用提供了潜在解决方案。区块链（BC）能够保障任务卸载的隐私性，但成本降低、排队延迟和冗余消除仍是尚未解决的问题。本文提出了一种面向元宇宙部分计算卸载（PCO）的冗余感知区块链方法。具体而言，我们构建了联合区块链冗余因子（BRF）与PCO问题，以最小化计算成本、最大化激励收益，并满足延迟与区块链卸载约束。我们证明了该问题为NP难问题，并根据其时间相关性将其转化为两个子问题：实时PCO问题与基于马尔可夫决策过程的BRF问题。我们将PCO问题建模为多用户博弈，提出了一种可在任意区块链冗余状态下实现纳什均衡的分布式算法，并设计了基于双深度Q网络的算法以求解最优BRF策略。该BRF策略根据用户计算需求与网络状态周期性更新，以辅助PCO算法。实验结果表明，所提方法优于现有方案，实现了成本开销降低47%、奖励收益提升约64%，且仅需少量训练回合即可收敛。