Modeling traffic demand at the flow level is essential for accurate network performance evaluation and optimization. However, despite its prevalence, the common practice is oversimplified and relies on unsubstantiated assumptions of traffic homogeneity and independent arrivals. In this paper, we analyze real-world traffic data collected from production environments to challenge these assumptions. Our findings reveal notable fidelity issues in the common practice, compromising the reliability of network performance evaluation and optimization. To address these limitations, we introduce Encore, a flow-level traffic demand modeling framework that captures key traffic characteristics and generates high-fidelity synthetic traces. Encore adopts a divide-and-conquer strategy, employing tailored machine learning models for distributional and sequential modeling, along with problem-specific enhancements. Systematic evaluations demonstrate that Encore outperforms existing traffic modeling methods in terms of accuracy and coverage in distribution modeling, and fidelity in sequential modeling. In addition to accurately restoring key characteristics of real traffic, Encore improves simulation performance consistency by a factor of 4 to 17 over the common practice. Moreover, Encore achieves a ~0.88 correlation in parameter ranking compared to the ground truth, showcasing its practical utility for network optimization.

Network traffic understanding is crucial to providing high-quality network services and protecting network security. However, due to the growing complexity of networks and the rising proportion of encrypted traffic, existing methods for network traffic understanding face severe challenges. Traditional approaches rely on manually designed features or require a large amount of labeled data, while pre-trained models offer new possibilities. Nevertheless, existing pre-trained models have the following limitations: (1) Their inputs only contain features from the packet content, neglecting temporal information about network dynamics. (2) Their pre-training targets only focus on static characteristics of the data stream without understanding the process of the network transmission. This paper presents the Pre-trained model for network Traffic Understanding (PTU), an innovative model that employs self-supervised pre-training to address the challenges of network traffic understanding. In PTU, we design a traffic representation scheme that integrates static packet content and network dynamics into a unified input space. Furthermore, we propose a pre-training method that includes four tailored pre-training targets. This approach enables PTU to capture both static and dynamic characteristics of network traffic from massive amounts of unlabeled data, thereby achieving enhanced performance in downstream tasks through fine-tuning. Extensive experiments confirm PTU's state-of-the-art (SOTA) performance. In traffic classification tasks, PTU achieves an F1 score of over$\mathbf{0. 9 9}$and secures a more than$\mathbf{1 0 \%}$improvement in accuracy in the most challenging task of encrypted application classification.

Today’s network is notorious for its complexity and uncertainty. Network operators often rely on network models for efficient network planning, operation, and optimization. The network model is responsible for understanding the complex relationships between network performance metrics (e.g., delay and jitter) and network characteristics (e.g., traffic and configuration). However, we still lack a systematic approach to developing accurate and lightweight network models that are aware of the impact of network configurations (i.e., expressiveness) and provide fine-grained flow-level temporal predictions (i.e., granularity). In this paper, we propose xNet, a data-driven network modeling framework based on graph neural networks (GNN). It is worth noting that xNet is not a dedicated network model designed for a specific network scenario with constraint considerations. On the contrary, xNet provides a general approach to modeling the network characteristics of concern with relation graph representations and configurable GNN blocks. xNet learns the state transition functions between time steps and rolls them out to obtain the full fine-grained prediction trajectory. We implement and instantiate xNet with three use cases. The experimental results show that xNet can accurately predict different performance metrics (i.e. temporal and steady-state QoS) in different scenarios, with performance comparable to state-of-the-art domain-specific models. Compared with traditional packet-level simulators, xNet achieves a speed improvement of more than two orders of magnitude, demonstrating its promising application in real-time optimization of network configurations.

Traffic modeling of Datacenter Network (DCN) today is over-simplified, deviating from the ground truth. Adopted by numerous researchers, the common practice relies on the assumptions of traffic homogeneity and independence for ease of use. Based on our investigation of a real-world traffic dataset, we disprove these assumptions and point out the severe fidelity issue of the common practice that could invalidate many motivations and conclusions from influential research works. In this paper, we present Encore, a DCN traic modeling framework for ine-grained traic modeling and high-fidelity synthetic traffic generation. Leveraging machine learning techniques, Encore effectively extracts and preserves essential distribution and sequential features from raw traic. Preliminary experiments demonstrate that the traic generated by Encore not only restores the key features of real traffic but also achieves high consistency when used to evaluate network performance. We envision further expanding Encore to full-process traffic modeling and generation, and expect these critical improvements in traffic models can facilitate the DCN performance evaluation and optimization.