Traffic data contains deep domain-specific knowledge, making labeling challenging, and the lack of labeled data adversely impacts the accuracy of learning-based traffic analysis. The pre-training technology is widely adopted in the fields of vision and natural language to address the problem of limited labeled data. However, the exploration in the domain of traffic analysis remains insufficient. This paper proposes an efficient pre-training model, TrafficFormer, for traffic data. In the pre-training stage, TrafficFormer introduces a fine-grained multi-classification task to enhance the representation capabilities of traffic data; in the fine-tuning stage, TrafficFormer proposes a traffic data augmentation method utilizing the random initialization feature of fields, which helps the traffic model focus on key information. We evaluate TrafficFormer using both traffic classification tasks and protocol understanding tasks. The experimental results show that TrafficFormer achieves superior performance on six traffic classification datasets, with improvements of up to 10% in the F1 score and demonstrates significantly superior protocol understanding capabilities compared to existing traffic pre-training models.

Random Early Detection (RED) has been integrated into datacenter switches as a fundamental Active Queue Management (AQM) for decades. The accurate configuration of RED parameters is crucial to achieving high throughput and low latency. However, due to the highly dynamic nature of workloads in datacenter networks, maintaining consistently high performance with statically configured RED thresholds poses a challenge. Prior work applies reinforcement learning to predict proper thresholds, but their real-world deployment has been hindered by poor tail performance caused by instability. In this paper, we propose$\textsf {PRED}$, a novel system that enables automatic and stable RED parameter adjustment in response to traffic dynamics. Specifically, the system employs a Multiplicative-Increase Multiplicative-Decrease (MIMD) strategy to dynamically adapt to flow concurrency while utilizing an Additive-Increase Additive-Decrease (AIAD) mechanism to adapt to flow distribution. We perform extensive evaluations on our physical testbed and large-scale simulations. The results demonstrate that$\textsf {PRED}$can keep up with the real-time network dynamics generated by realistic workloads. For instance, compared with the static-threshold-based methods,$\textsf {PRED}$keeps 66% shorter switch queue length and obtains up to 80% lower Flow Completion Time (FCT). Compared with the state-of-the-art learning-based method,$\textsf {PRED}$reduces the tail FCT by 34%.

When workers are heterogeneous in computing and transmission capabilities, the global efficiency of federated learning suffers from the straggler issue, i.e., the slowest worker drags down the overall training process. We propose a novel and efficient federated learning framework named FedPAGE, where workers perform distributed pruning adaptively towards global efficiency, i.e., fast training and high accuracy. For fast training, we develop a pruning rate learning approach generating an adaptive pruning rate for each worker, making the overall update time approximate to the fastest worker’s update time, i.e., no stragglers. For high accuracy, we find that structural similarity between sub-models is essential to global model accuracy in the distributed pruning, and thus propose the CIG_X pruning scheme to ensure maximum similarity. Meanwhile, we adopt the sparse training and design model aggregating of different size sub-models to cope with distributed pruning. We prove the convergence of FedPAGE and demonstrate the effectiveness of FedPAGE on image classification and natural language inference tasks. Compared with the state-of-the-art, FedPAGE achieves higher accuracy with the same speedup ratio.