TY - GEN
T1 - Generative AI-Driven Adaptive Consistency Maintenance for Distributed Simulation Systems
AU - Liu, Menghan
AU - Ding, Gangyi
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026
Y1 - 2026
N2 - Conventional consistency maintenance methods in distributed simulations degrade under unstable networks due to fixed rules and limited adaptability. To address this, we propose an AI-driven, two-layer adaptive consistency framework. In Tier-1, lightweight diagnostic tools use rule-based checking for immediate anomaly detection and initiate a protective "Soft-pause."In Tier-2, an edge-deployed Large Language Model acts as a decision engine, analyzing multi-dimensional network metrics to generate adaptive repair strategies via structured JSON. Extensive experiments under dynamic network degradation demonstrate our framework achieves a 98.6% Detection Accuracy and over 93% Consistency Maintenance Rate, significantly outperforming traditional baselines in preventing system crashes despite seconds-level repair latency.
AB - Conventional consistency maintenance methods in distributed simulations degrade under unstable networks due to fixed rules and limited adaptability. To address this, we propose an AI-driven, two-layer adaptive consistency framework. In Tier-1, lightweight diagnostic tools use rule-based checking for immediate anomaly detection and initiate a protective "Soft-pause."In Tier-2, an edge-deployed Large Language Model acts as a decision engine, analyzing multi-dimensional network metrics to generate adaptive repair strategies via structured JSON. Extensive experiments under dynamic network degradation demonstrate our framework achieves a 98.6% Detection Accuracy and over 93% Consistency Maintenance Rate, significantly outperforming traditional baselines in preventing system crashes despite seconds-level repair latency.
KW - Adaptive Repair
KW - Anomaly Detection
KW - Consistency Maintenance
KW - Distributed Simulation
KW - Generative AI
KW - Large Language Models
UR - https://www.scopus.com/pages/publications/105041651957
U2 - 10.1109/GAIIS69281.2026.11519245
DO - 10.1109/GAIIS69281.2026.11519245
M3 - Conference contribution
AN - SCOPUS:105041651957
T3 - 2026 International Conference on Generative Artificial Intelligence and Information Security, GAIIS 2026
SP - 124
EP - 129
BT - 2026 International Conference on Generative Artificial Intelligence and Information Security, GAIIS 2026
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2026 International Conference on Generative Artificial Intelligence and Information Security, GAIIS 2026
Y2 - 27 March 2026 through 29 March 2026
ER -