Self-Healing Memory Systems in AI Fabrics: Machine Learning-Driven Predictive Detection and Autonomous Mitigation of Memory Leaks in High Performance Network Switches

Authors

  • Srinivas Yadam

Keywords:

Self-Healing Networks, Memory Leak Prediction, Lstm Forecasting, Reinforcement Learning, Ai Fabric Reliability

Abstract

The explosive growth of AI workloads is driving the shift today towards ultra-high-speed 800G and infuture 1.6T networks, where control-plane processes in network switches increasingly suffer from hiddenmemory leaks and non

References

Ellie Lipe, "Energy Efficient Scheduling of AI/ML Workloads on Multi-Instance Gpus with Dynamic Repartitioning," in 2025 IEEE 25th International Symposium on Cluster, Cloud and Internet Computing (CCGrid), 30 June 2025. https://ieeexplore.ieee.org/document/11044810

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Published

2025-11-25

How to Cite

Srinivas Yadam. (2025). Self-Healing Memory Systems in AI Fabrics: Machine Learning-Driven Predictive Detection and Autonomous Mitigation of Memory Leaks in High Performance Network Switches . Journal of Computational Analysis and Applications (JoCAAA), 34(11), 675–691. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4226

Issue

Vol. 34 No. 11 (2025): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.