Semantic-aware update control improves secure metaverse computing
Semantic-Aware Update Admission and Adaptive Byzantine Redundancy for Secure Computation Offloading in COIN-Enabled Metaverse Systems
Distributed, Parallel, and Cluster Computing
Summary
Metaverse applications need fast and secure computing, but keeping the shared data safe on blockchain can slow things down. The authors study how to pick which updates to secure, how to add fault tolerance against bad actors, and where to run tasks efficiently. They create a method that combines game theory and machine learning to make these decisions smarter, balancing security and speed. Their results show better performance and timely secure updates compared to existing methods.
What this means in practice
- •For network schedulers: Manage task placement and update security dynamically to improve latency and reliability in networked metaverse services.
- •For blockchain infrastructure teams: Optimize blockchain update commitments and redundancy to reduce resource use while maintaining secure state in distributed applications.
Authors
Ibrahim Aliyu, Jaehyung Park, Jinsul Kim
Abstract
Computing in the network (COIN) supports latency-sensitive Metaverse services, but blockchain commitment of persistent shared-state updates competes with application execution for limited resources. We consider the joint problem of deciding which updates to commit, how much Byzantine redundancy to provision, and where to execute Metaverse application tasks. We formulate this problem under resource, latency, and practical Byzantine fault tolerance (PBFT) constraints, accounting for update persistence and semantic significance. We develop a hierarchical redundancy-aware PBFT (R-PBFT) approach that decomposes the decision process into slot-wise computation offloading and sequential update-admission and redundancy control. We formulate the former as an identical-interest game and address the latter using a feasibility-filtered double deep Q-network (DDQN). Analytical downlink allocation accounts for the energy and delivery-feasibility effects of consensus latency. For fixed slot state and redundancy, we establish the game's exact-potential property and finite termination of strict sequential improvements. Feasible-action filtering enforces configured Byzantine redundancy and blockchain-budget constraints independently of learning. Numerical results show relative improvements of up to $46.7\%$ in reward averaged across tested user loads and seeds over Minimum Secure, the strongest baseline on average. At the highest tested Byzantine bound, the secure timely-update ratio improves by $62.3\%$ over the same baseline. Ablations assess the contributions of feasibility filtering and adaptive deferral.