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.

Tuning parameters in Data Center Networks (DCN) has long been a nuisance and one of the reasons service providers are reluctant to deploy new mechanisms in their production environments. Despite the excessive time and resources devoted to finding better configurations, a "one-size-fits-all" solution remains elusive. Neither manual configuration by experts nor black-box optimization can address the challenges of network heterogeneity and dynamics. One essential factor impeding efficient and stable parameter optimization is the need to explore in real environments, which has a long convergence time alongside the risk of performance degradation. To address this problem, we build a twin performance model of the physical DCN that approximates the mapping from parameters to Quality of Service (QoS) metrics for fast and safe performance inference and present a DCN configuration framework called Iphicles. Leveraging gradients provided by differentiable performance models built with Graph Neural Networks (GNN), Iphicles can automatically recommend better parameters efficiently and stably. Experimental results based on extensive simulation demonstrate that in complex scenarios with mixed and dynamic traffic, Iphicles can deliver parameters that lead to evident improvements in flow completion time (FCT) for both mice and elephant flows simultaneously, with minimum convergence time while maintaining performance stability during the optimization process.

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.

Today’s network switches often use on-chip shared memory to improve buffer efficiency and absorb bursty traffic. Current buffer management practices usually rely on simple heuristics and have unrealistic assumptions about the traffic pattern, since developing a buffer management policy suited for every scenario is infeasible. We show that modern machine learning techniques can be of essential help to learn efficient policies automatically.In this paper, we propose Neural Dynamic Threshold (NDT) that uses deep reinforcement learning (RL) to learn buffer management policies without human instructions except for a high-level objective. To tackle the high complexity and scale of the buffer management problem, we develop two domain-specific techniques upon off-the-shelf deep RL solutions. First, we design a scalable RL model by leveraging the permutation symmetry of the switch ports. Second, we use a two-level control mechanism to achieve efficient training and decision-making. The buffer allocation is directly controlled by a low-level heuristic during the decision interval, while the RL agent only decides the high-level control factor according to the traffic density. Testbed and simulation experiments demonstrate that NDT generalizes well and outperforms hand-tuned heuristic policies even on workloads for which it was not explicitly trained.

Switch buffer serves an important role in the modern internet. To achieve efficiency, today’s switches often use on-chip shared memory. Shared memory switches rely on buffer management policies to allocate buffer among ports. To avoid waste of buffer resources or excessive buffer occupation by a few ports, existing policies tend to maximize overall buffer utilization and pursue queue length fairness. However, blind pursuit of utilization and misleading fairness definition based on queue length lead to buffer occupation with no benefit to throughput but extends queuing delay and undermines burst absorption of other ports. With analysis of current dynamic threshold policies, we demonstrate that meaningless buffer occupation can potentially impair the absorption capability of shared buffer, whereas none of the existing policies have addressed this problem. We contend that a buffer management policy should proactively detect port traffic and adjust buffer allocation accordingly. In this paper, we propose Traffic-aware Dynamic Threshold (TDT) policy. On the basis of the classic dynamic threshold policy, TDT proactively raises or lowers port threshold to absorb burst traffic or evacuate meaningless buffer occupation. We present detailed designs of port control state transition and state decision module that detect real-time traffic and change port thresholds accordingly. Simulation and DPDK-based real testbed demonstrate that TDT simultaneously optimizes for throughput, loss and delay, and reduces up to 50% flow completion time.

Switch buffer serves an important role in modern internet. To achieve efficiency, today's switches often use on-chip shared memory. Shared memory switches rely on buffer management policies to allocate buffer among ports. To avoid waste of buffer resources or a few ports occupy too much buffer, existing policies tend to maximize overall buffer utilization and pursue queue length fairness. However, blind pursuit of utilization and misleading fairness definition based on queue length leads to buffer occupation with no benefit to throughput but extends queuing delay and undermines burst absorption of other ports. We contend that a buffer management policy should proactively detect port traffic and adjust buffer allocation accordingly. In this paper, we propose Traffic-aware Dynamic Threshold (TDT) policy. On the basis of classic dynamic threshold policy, TDT proactively raise or lower port threshold to absorb burst traffic or evacuate meaningless buffer occupation. We present detailed designs of port control state transition and state decision module that detect real time traffic and change port thresholds accordingly. Simulation and DPDK-based real testbed demonstrate that TDT simultaneously optimizes for throughput, loss and delay, and reduces up to 50% flow completion time.