Deploying deep neural networks on edge devices is challenging due to limited computing resources and highly variable network conditions, e.g., in satellite networks. However, traditional static model segmentation statically deploys network architectures on edge devices, making it unable to adapt to runtime variations (e.g., bandwidth drops, memory contention, or overheating), which leads to degraded inference performance, higher energy costs, and device overload. To this end, we propose a lightweight adaptive model partitioning framework tailored for collaborative edge–cloud inference environments. In our framework, a real-time controller intelligently selects the optimal partition points in the neural network graph by monitoring system metrics such as bandwidth and CPU load. The controller is deployed as a RESTful microservice, introducing only minimal additional overhead to address the challenge of limited computational resources. We validate the effectiveness of our approach in image classification using ResNet-50 and ViT-B backbones across up to 20 simulated edge–cloud scenarios. Experimental results show that our method reduces end-to-end inference latency by 34.6% compared with static partitioning strategies. Moreover, the system demonstrates strong robustness under network jitter and heterogeneous device performance.

Distributed Denial of Service (DDoS) attack is a critical and persistent threat to the Internet. Recent DDoS detection schemes based on emerging programmable switches can achieve higher processing throughput and improve detection accuracy. However, with limited data plane memory, such schemes are not suitable for handling a large number of concurrent flows. Prior arts that attempt to increase memory efficiency have failed to do so without the expense of cost and accuracy. In this paper, we propose NetSentry, the first programmable switch based dynamic pooled testing DDoS detector. NetSentry detects DDoS in a pooled testing manner, where multiple flows are grouped to share the same storage unit on the data plane. NetSentry designs an elastic flow aggregation mechanism to dynamically adjust the detection granularity. Further, to achieve accurate DDoS detection for aggregated flows, NetSentry implements frequency domain DDoS detection on programmable switches. Evaluations of NetSentry’s hardware prototype show that NetSentry can achieve better accuracy while saving up to 91% of the data plane memory required to store flow features compared to the state-of-the-art programmable switch-based flow classification scheme.

The deadline requirements of real-time applications rapidly increase in recent years (e.g., cloud gaming, cloud VR, online conferencing). Due to diverse network conditions, meeting deadline requirements for these applications has become one of the research hotspots. However, the current schemes focus on providing high bitrate instead of meeting deadline requirements. In this paper, we propose D3T, a flexible deadline-aware transmission mechanism that aims to improve user quality of experience (QoE) for real-time video streaming. To fulfill the diverse deadline requirements over fluctuating network conditions, D3T uses a deadline-aware scheduler to select the high priority frame before the deadline. To reduce congestion and retransmission delay, we leverage a deep reinforcement learning algorithm to make decisions of sending rate and FEC (forward error correction) redundancy ratio based on observed network status and frame information. We evaluate D3T via trace-driven simulator spanning diverse network environments, video contents and QoE metrics. D3T significantly improves the frame completion rate by reducing the bandwidth waste before the deadline. In the considered scenarios, D3T outperforms previously approaches with the improvements in average QoE of 57%.

Congestion control plays an important role in the Internet to handle real-world network traffic. It has been dominated by hand-crafted heuristics for decades. Recently, reinforcement learning shows great potentials to automatically learn optimal or near-optimal control policies to enhance the performance of congestion control. However, existing solutions train agents in either simulators or emulators, which cannot fully reflect the real-world environment and degrade the performance of network communication. In order to eliminate the performance degradation caused by training in the simulated environment, we first highlight the necessity and challenges to train a learningbased agent in real-world networks. Then we propose a framework, ARC, for learning congestion control policies in a real environment based on asynchronous execution and demonstrate its effectiveness in accelerating the training. We evaluate our scheme on the real testbed and compare it with state-of-the-art congestion control schemes. Experimental results demonstrate that our schemes can achieve higher throughput and lower latency in comparison with existing schemes.