Logging network traffic plays a crucial role as it serves as the foundation for various network applications. As network scale continues to expand, contemporary network traffic becomes increasingly high-speed, high-volume, and dynamic. This growth poses challenges to traditional server-based solutions. In this paper, we proposeTitanLog, ahierarchicalandelasticlogging system designed specifically for large-scale network traffic. TitanLog utilizes thehierarchical loggingmethodology, which aims to identify the importance of each packet in real-time and log packet data of different importance at different levels. To enhance efficiency, we propose a co-design of the emerging programmable switch and the server, incorporating sketches and RDMA to boost performance. To achieve elasticity, we design mechanisms for run-time adjustments and monitoring for resource insufficiency. TitanLog possesses the capability to switch between these modes at run-time. We fully implement TitanLog on a testbed and conduct extensive evaluations. The experimental results demonstrate that TitanLog supports logging of 100Gbps traffic with a zero packet loss rate and reduces the log volume by up to 96.28%.

Legacy transport protocols such as TCP and QUIC suffer from high packet loss and low link utilization in Starlink. From the measurement data, we figure out the ground-satellite link (GSL) handover is mainly to blame. The periodic handovers result in link interruptions and bursty losses with a fixed interval of 15s, which impair TCP’s performance. Based on this finding, we present a handover-aware transport protocol, StarTCP, which proactively stalls transmission during handovers to avoid bursty losses and erroneous congestion signals. Preliminary results indicate that StarTCP can efficiently reduce packet loss and enhance throughput in Starlink.

The increasing demand for resource-efficient AI training has positioned Mobile Edge Computing (MEC) as a pivotal component in distributed machine learning tasks. Traditional scheduling methods, however, often fail to account for the inherent characteristics of training tasks, such as periodicity and task correlation, leading to sub-optimal resource utilization. To address these limitations, we propose the Multi-Graph to Graph Scheduler (MG2GS), a novel framework designed to optimize resource allocation and scheduling in MEC environments. MG2GS employs a graph neural network-based feature extractor to capture the spatiotemporal availability of network resources, while a reinforcement learning-based scheduler ensures optimal task scheduling decisions. By incorporating task structure and long-term resource distribution, MG2GS enhances resource utilization by more than 20% compared to existing methods. Simulation results demonstrate its effectiveness in increasing the number of scheduled tasks and improving overall resource efficiency for distributed AI model training.

Research in the area of Distributed Denial of Service (DDoS) attack detection is of paramount importance in the field of network security. Many existing studies employ static evaluation methods that fail to account for the reduction in accuracy due to the impact of inference latency on the timeliness of classification results. Furthermore, these studies frequently rely on simulated datasets for experimentation, which often lack the complexity and challenge of real-world attacks. These limitations significantly hinder the applicability of such research in practical scenarios. To overcome these challenges, we propose an evaluation methodology for real-time DDoS attack detection incorporating inference latency considerations. Additionally, we have developed a challenging DDoS dataset named THU-DDoS2024 and conducted experiments across four classification algorithms. This novel evaluation method and the newly generated dataset are integrated into an evaluation framework named E-DDoS. Leveraging E-DDoS, the “Intelligent Classification of High-Speed Network Traffic (ICNT)”, Grand Challenge was initiated. This event aims to motivate both academic and industrial sectors to delve into high-speed traffic classification tasks, thereby enhancing the applicability of research outputs to real-world applications.

By separating the control and forwarding planes, Software-Defined networking (SDN) enables the forwarding paths to be flexibly controlled by the logically centralized controllers using the global network view. To introduce SDN into existing networks, it is necessary to upgrade traditional devices to SDN- enabled ones. However, due to the business, economic and management limitations, it is difficult to realize full SDN deployment. As a result, how to migrate existing devices to SDN-compliant ones becomes the obvious dilemma for every network operator. In this paper, we address this question from the network performance perspective, and study how to leverage the capability of SDN to maximize traffic flow that can be achieved in hybrid SDNs. We formulate the maximum flow problem in networks with partial SDN deployment, and develop a fast Fully Polynomial Time Approximation Scheme (FPTAS) for solving it. Simulation results using real topologies show that hybrid SDNs outperform traditional networks, and we can obtain a near optimal network performance when 50% of SDN nodes are deployed.

Software Defined Networking (SDN) is an emerging networking paradigm that assumes a logically centralized control plane separated from the data plane. Despite all its advantages, separating the control and data planes introduces new challenges regarding resilient communications between the two. That is, disconnections between switches and their controllers could result in substantial packet loss and performance degradation. To achieve resilient control traffic forwarding, this paper investigates the protection of control traffic in SDNs with multiple controllers. We propose a control traffic protection scheme that combines both local rerouting and constrained reverse forwarding protections. This scheme enables switches to locally react to failures and redirect the control traffic to controllers by using standby backup forwarding options. Our goal is then to find a set of primary routes for control traffic, called protection control network, where as much control traffic as possible can benefit from the proposed protection scheme. We formulate the protection control network problem and develop an algorithm to solve it. Simulation results on real topologies show that our approach significantly improves the resilience of control traffic.