MLFD: The Implementation and Performance Evaluation of an LSTM-based, SmartNIC-Offloadable Failure Detector
Year: 2025
Type: article
Abstract: This paper explores the feasibility of using machine learning algorithms for failure detection services. Our implementation and profiling results show that a DPDK-based failure detector (FD) using long short-term neural network performs well in terms of accuracy, at the cost of reasonable additional computation resources for training and inference. We also show that our implementation can be offloaded to a mainstream SmartNIC-NVIDIA BlueField-2, while retaining comparable quality of service. The viability of offloading MLFD to SmartNIC further frees up the concern that it will take up too much computation resources.
Author: Yuncheng Yao
Institution: New York University Shanghai
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4408556773