动态调整资源与检查点，提升大模型在云环境下的容错能力。
Adaptive Fault Tolerance Mechanisms of Large Language Models in Cloud Computing Environments
- 基于实时性能预测故障，动态分配资源与调整检查点策略。
- 系统宕机时间减少30%，模型可用性优于传统容错机制。
- 适合高负载云环境中的大模型部署与运维人员参考。
随着大语言模型（LLMs）在云计算环境中大规模应用，如何保障其在故障场景下的安全与效率成为关键挑战。针对频繁的资源故障、网络问题和计算开销，本文提出一种新型自适应容错机制。该机制融合检查点、冗余与状态转移等经典方法，引入基于实时性能指标的故障预测与动态资源分配。通过云编排中间件集成数据驱动的深度学习异常检测技术，实现故障的前瞻性预防。同时，自适应检查点与恢复策略根据负载和系统状态动态调整，最大限度降低对模型性能的影响并减少停机时间。实验表明，所提模型显著提升了大规模云环境下的容错能力，系统宕机时间减少30%，模型可用性优于传统机制。
原文摘要 · Abstract (English)
With the rapid evolution of Large Language Models (LLMs) and their large-scale experimentation in cloud-computing spaces, the challenge of guaranteeing their security and efficiency in a failure scenario has become a main issue. To ensure the reliability and availability of large-scale language models in cloud computing scenarios, such as frequent resource failures, network problems, and computational overheads, this study proposes a novel adaptive fault tolerance mechanism. It builds upon known fault-tolerant mechanisms, such as checkpointing, redundancy, and state transposition, introducing dynamic resource allocation and prediction of failure based on real-time performance metrics. The hybrid model integrates data driven deep learning-based anomaly detection technique underlining the contribution of cloud orchestration middleware for predictive prevention of system failures. Additionally, the model integrates adaptive checkpointing and recovery strategies that dynamically adapt according to load and system state to minimize the influence on the performance of the model and minimize downtime. The experimental results demonstrate that the designed model considerably enhances the fault tolerance in large-scale cloud surroundings, and decreases the system downtime by $\mathbf{30\%}$, and has a better modeling availability than the classical fault tolerance mechanism.
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