AI-Driven Self-Healing High-Frequency Trading (HFT) Infrastructure for Fault Prediction and Latency Optimization

Author(s)	Sai Nitesh Palamakula
Country	United States
Abstract	High-Frequency Trading (HFT) systems operate under extreme latency requirements, where microsecond deviations can result in significant financial impact. This paper introduces a cloud-native, agent-based self-healing infrastructure utilizing large language models (LLMs) for fault prediction and latency mitigation. Without the overhead of training bespoke machine learning models, pre-deployed LLM agents interpret structured telemetry streams—CPU load, network jitter, queue depth, and disk I/O—to generate actionable insights. These agents communicate with cloud services such as Azure Monitor, Open Telemetry, and Kubernetes to trigger automated failover and scaling actions. The architecture integrates FPGA (Field-Programmable Gate Array) acceleration and kernel bypass technologies for deterministic low-latency processing. This approach yields a scalable, explainable, and operationally efficient framework for real-time fault recovery in HFT environments.
Keywords	High-Frequency Trading, LLM Agents, Cloud-Native Architecture, Fault Prediction, Latency Spike Mitigation, Self-Healing Systems, FPGA (Field-Programmable Gate Array), Kernel Bypass, Open Telemetry, Azure.
Field	Engineering
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-08-10
DOI	https://doi.org/10.36948/ijfmr.2025.v07i04.53007
Short DOI	https://doi.org/g9wnrn

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International Journal For Multidisciplinary Research