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学者姓名：郭迎亚
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Abstract ：
Traffic Engineering (TE) is an efficient technique to balance network flows and thus improves the performance of a hybrid Software Defined Network (SDN). Previous TE solutions mainly leverage heuristic algorithms to centrally optimize link weight setting or traffic splitting ratios under the static traffic demand. Note that as the network scale becomes larger and network management gains more complexity, it is notably that the centralized TE methods suffer from a high computation overhead and a long reaction time to optimize routing of flows when the network traffic demand dynamically fluctuates or network failures happen. To enable adaptive and efficient routing in distributed TE, we propose a Multi-agent Reinforcement Learning method CMRL that divides the routing optimization of a large network into multiple small-scale routing decision-making problems. To coordinate the multiple agents for achieving a global optimization goal in a hybrid SDN scenario, we construct a reasonable virtual environment to meet different routing constraints brought by legacy routers and SDN switches for training the routing agents. To train the routing agents for determining the local routing policies according to local network observations, we introduce the difference reward assignment mechanism for encouraging agents to cooperatively take optimal routing action. Extensive simulations conducted on the real traffic traces demonstrate the superiority of CMRL in improving TE performance, especially when traffic demands change or network failures happen.
Keyword ：
Distributed traffic engineering Distributed traffic engineering imitation learning imitation learning network-wide guidance network-wide guidance reinforcement learning reinforcement learning transformer transformer
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| GB/T 7714 | Guo, Yingya , Lin, Bin , Tang, Qi et al. Distributed Traffic Engineering in Hybrid Software Defined Networks: A Multi-Agent Reinforcement Learning Framework [J]. | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT , 2024 , 21 (6) : 6759-6769 . |
| MLA | Guo, Yingya et al. "Distributed Traffic Engineering in Hybrid Software Defined Networks: A Multi-Agent Reinforcement Learning Framework" . | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 21 . 6 (2024) : 6759-6769 . |
| APA | Guo, Yingya , Lin, Bin , Tang, Qi , Ma, Yulong , Luo, Huan , Tian, Han et al. Distributed Traffic Engineering in Hybrid Software Defined Networks: A Multi-Agent Reinforcement Learning Framework . | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT , 2024 , 21 (6) , 6759-6769 . |
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Network traffic prediction plays a significant role in network management. Previous network traffic prediction methods mainly focus on the temporal relationship between network traffic, and used time series models to predict network traffic, ignoring the spatial information contained in traffic data. Therefore, the prediction accuracy is limited, especially in long-term prediction. To improve the prediction accuracy of the dynamic network traffic in the long term, we propose an Attention -based Spatial-Temporal Graph Network (ASTGN) model for network traffic prediction to better capture both the temporal and spatial relations between the network traffic. Specifically, in ASTGN, we exploit an encoder-decoder architecture, where the encoder encodes the input network traffic and the decoder outputs the predicted network traffic sequences, integrating the temporal and spatial information of the network traffic data through the Spatio-Temporal Embedding module. The experimental results demonstrate the superiority of our proposed method ASTGN in long-term prediction.
Keyword ：
Attention mechanism Attention mechanism Encoder-decoder Encoder-decoder Graph neural network Graph neural network Network traffic prediction Network traffic prediction Temporal and spatial Temporal and spatial
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| GB/T 7714 | Peng, Yufei , Guo, Yingya , Hao, Run et al. Network traffic prediction with Attention-based Spatial-Temporal Graph Network [J]. | COMPUTER NETWORKS , 2024 , 243 . |
| MLA | Peng, Yufei et al. "Network traffic prediction with Attention-based Spatial-Temporal Graph Network" . | COMPUTER NETWORKS 243 (2024) . |
| APA | Peng, Yufei , Guo, Yingya , Hao, Run , Xu, Chengzhe . Network traffic prediction with Attention-based Spatial-Temporal Graph Network . | COMPUTER NETWORKS , 2024 , 243 . |
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Network traffic classifiers of mobile devices are widely learned with federated learning(FL) for privacy preservation. Noisy labels commonly occur in each device and deteriorate the accuracy of the learned network traffic classifier. Existing noise elimination approaches attempt to solve this by detecting and removing noisy labeled data before training. However, they may lead to poor performance of the learned classifier, as the remaining traffic data in each device is few after noise removal. Motivated by the observation that the data feature of the noisy labeled traffic data is clean and the underlying true distribution of the noisy labeled data is statistically close to the clean traffic data, we propose to utilize the noisy labeled data by normalizing it to be close to the clean traffic data distribution. Specifically, we first formulate a distributionally robust federated network traffic classifier learning problem (DR-NTC) to jointly take the normalized traffic data and clean data into training. Then we specify the normalization function under Wasserstein distance to transform the noisy labeled traffic data into a certified robust region around the clean data distribution, and we reformulate the DR-NTC problem into an equivalent DR-NTC-W problem. Finally, we design a robust federated network traffic classifier learning algorithm, RFNTC, to solve the DR-NTC-W problem. Theoretical analysis shows the robustness guarantee of RFNTC. We evaluate the algorithm by training classifiers on a real-world dataset. Our experimental results show that RFNTC significantly improves the accuracy of the learned classifier by up to 1.05 times.
Keyword ：
Data models Data models distributionally robust optimization distributionally robust optimization federated learning federated learning Mobile handsets Mobile handsets Network traffic classification Network traffic classification Noise measurement Noise measurement Servers Servers Telecommunication traffic Telecommunication traffic Training Training Uncertainty Uncertainty
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| GB/T 7714 | Shi, Siping , Guo, Yingya , Wang, Dan et al. Distributionally Robust Federated Learning for Network Traffic Classification With Noisy Labels [J]. | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2024 , 23 (5) : 6212-6226 . |
| MLA | Shi, Siping et al. "Distributionally Robust Federated Learning for Network Traffic Classification With Noisy Labels" . | IEEE TRANSACTIONS ON MOBILE COMPUTING 23 . 5 (2024) : 6212-6226 . |
| APA | Shi, Siping , Guo, Yingya , Wang, Dan , Zhu, Yifei , Han, Zhu . Distributionally Robust Federated Learning for Network Traffic Classification With Noisy Labels . | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2024 , 23 (5) , 6212-6226 . |
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Route optimization is a key core technology to optimize network traffic distribution, achieve network load balancing, and improve network performance. Traditional distributed networks widely run shortest-path based routing protocols, and the path of traffic is determined by the link weights of the distributed network, so routing optimization methods are usually optimized around the link weights of the network. Heuristics-based link weight optimization methods are widely used. However, heuristic methods rely on manually set rules, which are poorly generalized and cannot adapt well to dynamically changing traffic demands. Compared to heuristics, deep reinforcement learning (DRL) has the advantage of extracting more accurate feature representations in addition to its ability to handle high-dimensional state and action spaces. We propose a network link weight optimization method based on anti-symmetric deep graph networks (A-DGN) and reinforcement learning using a novel GNN framework anti-symmetric deep graph networks, where link weights are adjusted to reduce the network link utilization with the optimization objective of minimizing the maximum link utilization in the network. Experimental results show that the proposed method achieves significant performance improvements in the link weight optimization problem in four real-world network topology scenarios. © 2024 IEEE.
Keyword ：
Deep reinforcement learning Deep reinforcement learning Reinforcement learning Reinforcement learning Routing algorithms Routing algorithms
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| GB/T 7714 | Chen, Yuhan , Guo, Yingya . Network link weight optimization based on antisymmetric deep graph networks and reinforcement learning [C] . 2024 : 96-99 . |
| MLA | Chen, Yuhan et al. "Network link weight optimization based on antisymmetric deep graph networks and reinforcement learning" . (2024) : 96-99 . |
| APA | Chen, Yuhan , Guo, Yingya . Network link weight optimization based on antisymmetric deep graph networks and reinforcement learning . (2024) : 96-99 . |
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With the rapid development of Internet technology and the continuous explosive growth of network traffic, Traffic Engineering (TE), as a viable method for optimizing network traffic distribution and improving network performance, attracts widespread attention from both industry and academia. Software Defined Networks (SDN), which decouples the data plane and the control plane, realizes a flexible routing and improves the TE performance. Existing TE approaches in SDN mainly utilize Reinforcement Learning (RL) methods to learn the mapping relationship between network traffic and routing policies. However, due to the continuous expansion of network size and dynamic changes in traffic, the enlargement of traffic state space hinders RL from converging to the optimal routing policy, leading to a decline in network performance. To address these issues, this paper presents a TE method based on unsupervised contrastive representation and RL. This method first shrinks the original traffic state space by efficiently extracting traffic features through Contrastive Learning (CL), aiding quick convergence of RL. It then uses RL to directly learn the mapping from traffic features to traffic splitting policies. Finally, through numerous experiments on real network traffic and topology, it demonstrates that the proposed TE method can effectively achieve load balancing of network traffic under complex and volatile dynamic traffic demands, thereby enhancing network performance. © 2024 IEEE.
Keyword ：
Mapping Mapping Network routing Network routing Reinforcement learning Reinforcement learning Traffic control Traffic control
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| GB/T 7714 | Yang, Ruiyu , Tang, Qi , Guo, Yingya . An SDN Traffic Engineering Approach Based on Traffic Unsupervised Contrastive Representation and Reinforcement Learning [C] . 2024 : 239-242 . |
| MLA | Yang, Ruiyu et al. "An SDN Traffic Engineering Approach Based on Traffic Unsupervised Contrastive Representation and Reinforcement Learning" . (2024) : 239-242 . |
| APA | Yang, Ruiyu , Tang, Qi , Guo, Yingya . An SDN Traffic Engineering Approach Based on Traffic Unsupervised Contrastive Representation and Reinforcement Learning . (2024) : 239-242 . |
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Hybrid Software Defined Networks (Hybrid SDNs), which combines the robustness of distributed network and the flexibility of centralized network, is now a prevailing network architecture. Previous hybrid SDN Traffic Engineering (TE) solutions search an optimal link weight setting or compute the splitting ratios of traffic leveraging heuristic algorithms. However, these methods cannot react timely to the fluctuating traffic demands in dynamic environments and suffer a hefty performance degradation when traffic demands change or network failures happen, especially when network scale is large. To cope with this, we propose a Multi- Agent reinforcement learning based TE method MATE that timely determines the route selection for network flows in dynamic hybrid SDNs. Through dividing the large-scale routing optimization problem into small-scale problem, MATE can better learn the mapping between the traffic demands and routing policy, and efficiently make online routing inference with dynamic traffic demands. To collaborate multiple agents and speed up the convergence in the training process, we innovatively design the actor network and introduce previous actions of all agents in the training of each agent. Extensive experiments conducted on different network topologies demonstrate our proposed method MATE has superior TE performance with dynamic traffic demands and is robust to network failures.
Keyword ：
Dynamic environment Dynamic environment Hybrid Software Defined Networks Hybrid Software Defined Networks Multi-agent reinforcement learning Multi-agent reinforcement learning Traffic Engineering Traffic Engineering
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| GB/T 7714 | Guo, Yingya , Ding, Mingjie , Zhou, Weihong et al. MATE: A multi-agent reinforcement learning approach for Traffic Engineering in Hybrid Software Defined Networks [J]. | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2024 , 231 . |
| MLA | Guo, Yingya et al. "MATE: A multi-agent reinforcement learning approach for Traffic Engineering in Hybrid Software Defined Networks" . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 231 (2024) . |
| APA | Guo, Yingya , Ding, Mingjie , Zhou, Weihong , Lin, Bin , Chen, Cen , Luo, Huan . MATE: A multi-agent reinforcement learning approach for Traffic Engineering in Hybrid Software Defined Networks . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2024 , 231 . |
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Hybrid Software Defined Networks (Hybrid SDNs), with a partial upgrade of legacy routers to SDN switches in traditional distributed networks, currently stand as a prevailing network architecture. Traffic Engineering (TE) in hybrid SDN requires the efficient and timely acquisition of a routing policy to adapt to dynamically changing traffic demands, which has recently become a hot topic. Ignoring the hidden relations of consecutive states and the compact representation of the network environment, previous Deep Reinforcement Learning (DRL)-based studies suffer from the convergence problem by only establishing the direct relationship between individual Traffic Matrix (TM) and routing policy. Therefore, to enhance TE performance in hybrid SDNs under dynamically changing traffic demands, we propose to integrate the Transformer model with DRL to establish the relationship between consecutive states and routing policies. The temporal characteristic among consecutive states can effectively assist DRL in solving the convergence problem. To obtain a compact and accurate description of the network environment, we propose to jointly consider TM, routing action, and reward in designing the state of the network environment. To better capture the temporal relations among consecutive states of the network environment, we design a multi-feature embedding module and achieve positional encodings in the Transformer model. The extensive experiments demonstrate that once the convergence problem is solved, the proposed Transformer-based DRL method can efficiently generate routing policies that adapt well to dynamic network traffic.
Keyword ：
Deep Reinforcement Learning Deep Reinforcement Learning Dynamic traffic Dynamic traffic Hybrid SDN Hybrid SDN Traffic Engineering Traffic Engineering Transformer Transformer
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| GB/T 7714 | Lin, Bin , Guo, Yingya , Luo, Huan et al. TITE: A transformer-based deep reinforcement learning approach for traffic engineering in hybrid SDN with dynamic traffic [J]. | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE , 2024 , 161 : 95-105 . |
| MLA | Lin, Bin et al. "TITE: A transformer-based deep reinforcement learning approach for traffic engineering in hybrid SDN with dynamic traffic" . | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 161 (2024) : 95-105 . |
| APA | Lin, Bin , Guo, Yingya , Luo, Huan , Ding, Mingjie . TITE: A transformer-based deep reinforcement learning approach for traffic engineering in hybrid SDN with dynamic traffic . | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE , 2024 , 161 , 95-105 . |
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Network failures, especially link failures, happen frequently in Internet Service Provider (ISP) networks. When link failures occur, the routing policies need to be re-computed and failure recovery usually takes a few minutes, which degrades the network performance to a great extent. Therefore, a proper failure recovery scheme that can realize a fast and timely routing policy computation needs to be designed. In this paper, we propose FRRL, a Reinforcement Learning (RL) approach to intelligently perceive network failures and timely compute the routing policy for improving the network performance when link failure happens. Specifically, to perceive the link failures, we design a Topology Difference Vector (TDV) encoder module in FRRL for encoding the topology structure with link failures. To efficiently compute the routing policy when link failures happen, we integrate the TDV in the agent training for learning the map between the encoded failure topology structure and routing policies. To evaluate the performance of our proposed method, we conduct experiments on three network topologies and the experimental results demonstrate that our proposed method has superior performance when link failures happen compared to other methods.
Keyword ：
Link failure recovery Link failure recovery Reinforcement learning Reinforcement learning Routing optimization Routing optimization Traffic engineering Traffic engineering
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| GB/T 7714 | Ma, Yulong , Guo, Yingya , Yang, Ruiyu et al. FRRL: A reinforcement learning approach for link failure recovery in a hybrid SDN [J]. | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2024 , 234 . |
| MLA | Ma, Yulong et al. "FRRL: A reinforcement learning approach for link failure recovery in a hybrid SDN" . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 234 (2024) . |
| APA | Ma, Yulong , Guo, Yingya , Yang, Ruiyu , Luo, Huan . FRRL: A reinforcement learning approach for link failure recovery in a hybrid SDN . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2024 , 234 . |
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Routing optimization, as a significant part of Traffic Engineering (TE), plays an important role in balancing network traffic and improving quality of service. With the application of Machine Learning (ML) in various fields, many neural network-based routing optimization solutions have been proposed. However, most existing ML-based methods need to retrain the model when confronted with a network unseen during training, which incurs significant time overhead and response delay. To improve the generalization ability of the routing model, in this paper, we innovatively propose a routing optimization method GROM which combines Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN), to directly generate routing policies under different and unseen network topologies without retraining. Specifically, for handling different network topologies, we transform the traffic-splitting ratio into element -level output of GNN model. To make the DRL agent easier to converge and well generalize to unseen topologies, we discretize the huge continuous trafficsplitting action space. Extensive simulation results on five real-world network topologies demonstrate that GROM can rapidly generate routing policies under different network topologies and has superior generalization ability.
Keyword ：
Graph neural networks Graph neural networks Reinforcement learning Reinforcement learning Software-defined networks Software-defined networks Traffic engineering Traffic engineering
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| GB/T 7714 | Ding, Mingjie , Guo, Yingya , Huang, Zebo et al. GROM: A generalized routing optimization method with graph neural network and deep reinforcement learning [J]. | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2024 , 229 . |
| MLA | Ding, Mingjie et al. "GROM: A generalized routing optimization method with graph neural network and deep reinforcement learning" . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 229 (2024) . |
| APA | Ding, Mingjie , Guo, Yingya , Huang, Zebo , Lin, Bin , Luo, Huan . GROM: A generalized routing optimization method with graph neural network and deep reinforcement learning . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2024 , 229 . |
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Network traffic classification is the foundation for many network security and network management applications. Recently, to preserve the privacy of the data which are generated in the mobile ends, federated learning (FL)-based classification methods are being proposed. Unfortunately, the performance of FL-based methods can seriously degrade when the client data have skewness. This is particularly true for mobile network traffic classification where the environments in the mobile ends are highly heterogeneous. In this article, we first conduct a measurement study on traffic classification accuracy through FL using real-world network traffic trace and we observe serious accuracy degradation due to heterogeneous environments. We propose a novel federated analytics (FA) approach, FEAT, to improve the accuracy. Note that FL emphasizes on model training, yet our FA performs local analytic tasks that can estimate traffic data skewness and select appropriate clients for FL model training. Our analytics tasks are performed locally and in a federated manner; thus, we preserve privacy as well. Our approach has strong theoretical properties where we exploit Hoeffding inequality to infer traffic data skewness and we leverage the Thompson Sampling for client selection. We evaluate our approach through extensive experiments using real-world traffic data sets QUIC and ISCX. The extensive experiments demonstrate that FEAT can improve traffic classification accuracy in heterogeneous environments.
Keyword ：
Federated analytics (FA) Federated analytics (FA) federated learning (FL) federated learning (FL) heterogeneous environments heterogeneous environments network traffic classification network traffic classification
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| GB/T 7714 | Guo, Yingya , Wang, Dan . FEAT: A Federated Approach for Privacy-Preserving Network Traffic Classification in Heterogeneous Environments [J]. | IEEE INTERNET OF THINGS JOURNAL , 2023 , 10 (2) : 1274-1285 . |
| MLA | Guo, Yingya et al. "FEAT: A Federated Approach for Privacy-Preserving Network Traffic Classification in Heterogeneous Environments" . | IEEE INTERNET OF THINGS JOURNAL 10 . 2 (2023) : 1274-1285 . |
| APA | Guo, Yingya , Wang, Dan . FEAT: A Federated Approach for Privacy-Preserving Network Traffic Classification in Heterogeneous Environments . | IEEE INTERNET OF THINGS JOURNAL , 2023 , 10 (2) , 1274-1285 . |
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