Tootfinder

Design of a Timer Queue Supporting Dynamic Update Operations
Zekun Wang, Binghao Yue, Weitao Pan, Jiangyi Shi, Yue Hao
https://arxiv.org/abs/2508.10283 https://

Design of a Timer Queue Supporting Dynamic Update Operations
Large-scale timers are ubiquitous in network processing, including flow table entry expiration control in software defined network (SDN) switches, MAC address aging in Ethernet bridges, and retransmission timeout management in TCP/IP protocols. Conventional implementations suffer from critical limitations: low timing accuracy due to large-scale timer traversal and high computational overhead for new timer insertion. This paper presents a hybrid-architecture hardware priority queue based on syst…

Pegasus: A Universal Framework for Scalable Deep Learning Inference on the Dataplane
Yinchao Zhang, Su Yao, Yong Feng, Kang Chen, Tong Li, Zhuotao Liu, Yi Zhao, Lexuan Zhang, Xiangyu Gao, Feng Xiong, Qi Li, Ke Xu
https://arxiv.org/abs/2506.05779

Pegasus: A Universal Framework for Scalable Deep Learning Inference on the Dataplane
The paradigm of Intelligent DataPlane (IDP) embeds deep learning (DL) models on the network dataplane to enable intelligent traffic analysis at line-speed. However, the current use of the match-action table (MAT) abstraction on the dataplane is misaligned with DL inference, leading to several key limitations, including accuracy degradation, limited scale, and lack of generality. This paper proposes Pegasus to address these limitations. Pegasus translates DL operations into three dataplane-orien…