Traffic engineering (TE) is widely used to optimize network performance in modern networks. Typically, TE is formulated as a multiple-commodity flow (MCF) optimization problem and solved using mathematical solvers or machine learning approaches, but it becomes unscalable as the network size grows. Existing methods often limit available paths for flow allocation to speed up problem-solving, but this compromises TE performance. Achieving both high performance and fast decisionmaking with free path selection remains a significant challenge. This paper proposes TELD, a scalable and high-performance TE framework with free path selection. TELD leverages Graph Neural Networks (GNNs) that are widely proven with high efficiency in capturing network-specific characteristics and enabling faster decision-making than mathematical solvers. Our key idea is to reformulate the MCF problem into a learningfriendly representation and integrate TE constraints directly into GNN training and inference. The key challenge here is how to efficiently combine the problem reformulation with GNN. TELD tackles this with two critical designs. First, observing that GNNs work better with continuous features, TELD relaxes the freepath MCF formulation by treating flow allocation variables as continuous rather than discrete. Second, TELD introduces a multi-constraint hybrid GNN and a result fine-tuning mechanism to further improve GNN efficiency in TE. Extensive experiments show that TELD outperforms the state-of-the-art GNN-based TE framework by$\sim 55\%$and reduces decision latency by three orders of magnitude compared to mathematical solvers.

As data continues to grow exponentially, the importance of online learning across various domains has increased significantly. However, most existing studies assume that the true class label for each incoming data point is readily available, an assumption that is often impractical. To address this issue, this paper introduces a novel algorithm called Density-based Clustering and One-Class Ensemble Active Learning (DCOE-AL). This approach constructs an ensemble model by combining a clustering algorithm with the One-Class Broad Learning System (OCBLS), which represents clusters within the feature space. Furthermore, a new active learning mechanism is developed to enable DCOE-AL to effectively handle challenges associated with concept drift and label scarcity. The proposed method is evaluated on multiple synthetic data streams that exhibit diverse types of concept drift, as well as on several real-world data streams. Comparative evaluations demonstrate that DCOE-AL achieves superior performance while requiring significantly fewer labeled samples.

Current traffic engineering systems utilize machine learning to optimize traffic distribution but encounter scalability challenges due to the exponential growth of potential paths. They often resort to fixed path approaches, which result in suboptimal traffic distribution compared to unconstrained methods. In this paper, we construct a compact learning model for the free path TE formulation, which can scale to large networks without compromising the feasible region. Additionally, we introduce a Lagrangian-based loss function to drive the solution towards the feasible region. Our preliminary evaluation of real-world topologies demonstrates up to 6000 times speedup and satisfies 97.4% of the total flow compared with the solver.

Enabling low-latency end-to-end interdomain communication is critical in edge networks. However, the current network architecture results in unnecessarily long communication paths, leading to high latency between devices. To address this issue, we propose a novel interdomain edge peering framework called Collie. In Collie, edge networks belonging to different network providers collaborate to forward traffic towards destinations, effectively reducing end-to-end communication latency. Importantly, Collie allows network providers to maintain their autonomy in link usage strategy. We also develop a distributed algorithm in Collie that enables edge nodes from different networks to collectively determine optimal routing and traffic assignment, ensuring low-latency delivery while respecting network policies without exposing them. We implement a prototype of Collie and extensively evaluate its performance using real-world topologies. Our results demonstrate that Collie achieves a tight approximation ratio and exhibits scalability in large interdomain edge networks.

Reproducing research results is important for the networking community. The current best practice typically resorts to: (1) looking for publicly available prototypes; (2) contacting the authors to get a private prototype; or (3) manually implementing a prototype following the description of the publication. However, most published network research does not have public prototypes and private ones are hard to get. As such, most reproducing efforts are spent on manual implementation based on the publications, which is both time and labor consuming and error-prone. In this paper, we boldly propose reproducing network research results using the emerging large language models (LLMs). We first prove its feasibility with a small-scale experiment, in which four students with essential networking knowledge each reproduces a different networking system published in prominent conferences and journals by prompt engineering ChatGPT. We report our observations and lessons and discuss future open research questions of this proposal.