Traffic Engineering (TE) has been widely used by network operators to improve network performance and deliver better service quality. One major challenge for TE is providing routing strategies that can adapt to highly dynamic future traffic scenarios. Unfortunately, existing works either suffer severe performance degradation under unexpected traffic fluctuations, or sacrifice optimality to guarantee worst-case performance when traffic remains relatively stable. In this paper, we propose LARRI, a learning-based TE framework that predicts adaptive routing strategies for unknown future traffic scenarios. By integrating future demand range prediction and optimal range routing imitation into a single step, LARRI learns to generate a routing strategy that accommodates a wide range of possible future traffic matrices, thereby achieving a good trade-off between performance optimality and worst-case guarantees. Moreover, LARRI employs a scalable graph neural network architecture, which greatly facilitates both training and inference. Extensive simulations on six real-world network topologies show that LARRI achieves near-optimal load balancing in future traffic scenarios, improves worst-case performance by up to 43.3% over state-of-the-art baselines, and consistently provides the lowest end-to-end delay under dynamic traffic fluctuations.

Distributed Traffic Engineering (TE) aims to optimize network performance by generating individual routing strategies at each router without a global view of the network. A major challenge for these TE solutions is handling performance degradation caused by unexpected traffic fluctuations and unpredictable link failures. Recently, Machine Learning (ML) techniques have introduced new opportunities to enhance distributed TE. In this paper, we propose Path-Based Graph Neural Network (PathGNN), which leverages the emerging GNN architecture to quickly infer robust and resilient routing strategies in a distributed manner to accommodate unexpected network conditions. PathGNN adopts a novel path-link bipartite graph modeling approach to capture the dynamics of link resources shared by routing paths. It then performs efficient GNN message exchanges among routers to make adaptive local routing decisions for better load balancing. Additionally, PathGNN leverages Supervised Learning (SL) to directly learn from optimal routing strategies through efficient offline training. Evaluation results on four real-world network topologies demonstrate PathGNN’s strong generalization capability. Compared to state-of-the-art distributed TE solutions, PathGNN improves the load balancing performance by at least 24.4% with lower end-to-end delay under dynamic traffic scenarios, and also boosts performance by up to 35.3% under multiple link failures.

Traffic Matrix (TM), which records traffic volumes among network nodes, is important for network operation and management. Due to cost and operation issues, TMs cannot be directly measured and collected in real time. Therefore, many studies work on predicting future TMs based on historical TMs. However, existing works are usually accuracy-centric prediction solutions that mainly focus on improving predicting accuracy of flows’ sizes (i.e., values of elements in TMs) without considering the practical application of TMs. In this paper, we propose a novel TM prediction solution called Prophet for Traffic Engineering (TE), a typical application for TMs which takes TMs as input to optimize routing. We identify that the critical property (i.e., ratio among elements) in a TM plays an important role in TE’s performance. Based on this analysis, we adopt the matrix normalization to maintain the critical property in TMs and customize a TE-centric angle loss function to introduce scale invariance of TMs for capturing the overall relationship error. Different from the element-wise Mean Squared Error (MSE) loss function in accuracy-centric prediction solutions, our proposed TE-centric angle loss function has a clear geometric interpretation, which confines the angle between predicted TM and real TM to zero. Simulation results show that the predicted TMs from Prophet can improve the performance of link-level TE and path-level TE by up to 45.4% and 52.8%, respectively, compared to existing solutions.

Traditional Traffic Engineering (TE) usually balances the load on network links by formulating and solving a routing optimization problem based on measured Traffic Matrices (TMs). Given that traffic demands could change unexpectedly and significantly in realistic scenarios, routing strategies opti-mized based on currently measured TMs might not work well in future traffic scenarios. To compensate for the mismatch between stale routing decisions and future TMs, network operators may perform routing updates more frequently, which could introduce significant network disturbance and service disruption. Moreover, given the high routing computation overhead of TE optimization in today's large-scale networks, routing updates could experience severe delay and thus cannot accommodate future traffic changes in time. To address these challenges, we propose Roracle, a scalable learning-based TE that quickly predicts a good routing strategy for a long sequence of future TMs, while the learning process is guided by the optimal solutions of Linear Programming (LP) problems using Supervised Learning (SL). We design a scalable Graph Neural Network (GNN) architecture that greatly facilitates training and inference processes to accelerate TE in large networks. Extensive simulation results on real-world network topologies and traffic traces show that Roracle outperforms existing TE solutions by up to 36% in terms of worst-case performance under future unknown traffic scenarios. Additionally, Roracle achieves good scalability by providing at least$71\times$speedup over the most efficient baseline method in large-scale networks.

Traffic Engineering (TE) has been widely used by network operators to improve network performance and provide better service quality to users. One major challenge for TE is how to generate good routing strategies adaptive to highly dynamic future traffic scenarios. Unfortunately, existing works could either experience severe performance degradation under unexpected traffic fluctuations or sacrifice performance optimality for guaranteeing the worst-case performance when traffic is relatively stable. In this paper, we propose LARRI, a learning-based TE to predict adaptive routing strategies for future unknown traffic scenarios. By learning and predicting a routing to handle an appropriate range of future possible traffic matrices, LARRI can effectively realize a trade-off between performance optimality and worst-case performance guarantee. This is done by integrating the prediction of future demand range and the imitation of optimal range routing into one step. Moreover, LARRI employs a scalable graph neural network architecture to greatly facilitate training and inference. Extensive simulation results on six real-world network topologies and traffic traces show that LARRI achieves near-optimal load balancing performance in future traffic scenarios with up to 43.3% worst-case performance improvement over state-of-the-art baselines, and also provides the lowest end-to-end delay under dynamic traffic fluctuations.

Emerging applications pose different Quality of Service (QoS) requirements for the network, where Traffic Engineering (TE) plays an important role in QoS provisioning by carefully selecting routing paths and adjusting traffic split ratios on routing paths. To accommodate diverse QoS requirements of traffic flows under network dynamics, TE usually periodically computes an optimal routing strategy and updates a significant number of forwarding entries, which introduces considerable network operation management overhead. In this paper, we propose QoS-RL, a Reinforcement Learning (RL)-based TE solution for QoS provisioning and load balancing with low management overhead and service disruption during routing updates. Given the traffic matrices that represent the traffic demands of high and low priority flows, QoS-RL can intelligently select and update only a few destination-based forwarding entries to satisfy the QoS requirements of high priority traffic while maintaining good load balancing performance by rerouting a small portion of low priority traffic. Extensive simulation results on four real-world network topologies demonstrate that QoS-RL provides at least 95.5 % of optimal end-to-end delay performance on average for high priority flows, and also achieves above 90 % of optimal load balancing performance in most cases by updating only 10% of destination-based forwarding entries.

Traffic Engineering (TE) is an important network operation that routes/reroutes flows based on network topology and traffic demands to optimize network performance. Recently, new emerging applications pose challenges to TE with dynamic network conditions, where frequent routing updates are required to maintain good network performance with Software-Defined Networking (SDN). However, flow rerouting operations could lead to considerable Quality of Service (QoS) degradation and service disruption, which is often neglected by existing TE solutions. In this paper, we apply a new QoS metric named network disturbance to measure the negative impact of flow rerouting operations performed by TE. To achieve near-optimal load balancing performance and mitigate network disturbance together in dynamic network scenarios, we propose a flexible and disturbance-aware TE solution called FlexDATE that combines Reinforcement Learning (RL) and Linear Programming (LP). Specifically, FlexDATE leverages RL to intelligently identify flexible numbers of critical flows for each traffic matrix and reroutes these critical flows based on LP optimization to improve network performance with low disturbance. Empowered by a customized actor-critic architecture coupled with Graph Neural Networks (GNNs), FlexDATE can generalize well to unseen traffic scenarios and remain resilient to single link failures. Extensive simulations are conducted on five real-world network topologies to evaluate FlexDATE with real and synthetic traffic traces. The results show that FlexDATE can achieve the performance target (i.e., 90% of optimal performance) in 99% of network scenarios and effectively mitigate the average and maximum network disturbance by up to 9.1% and 38.6%, respectively, compared to state-of-the-art TE solutions.

Programmable switches have been proposed in today’s network to enable flexible reconfiguration of devices and reduce time-to-deployment. Buffer sizing, an important factor for network performance, however, has not received enough attention in programmable network. The state-of-the-art buffer sizing solutions usually employ either fixed buffer size or adjust the buffer size heuristically. Without programmability, they suffer from either massive packet drops or large queueing delay in dynamic environment. In this paper, we propose Adaptive Buffer Sizing (ABS), a low-cost and deploy-friendly framework compatible with programmable network. By decoupling the data plane and control plane, ABS-capable switches only need to react to the actions from controller, optimizing network performance in run-time under dynamic traffic. Meanwhile, actions can be programmed by particular Machine Learning (ML) models in the controller to meet different network requirements. In this paper, we address two specific ML models for different scenarios, a reinforcement learning model for relatively stable network with user specific quality requirements, and a supervised learning model for highly dynamic network condition. We implement the ABS framework by integrating the prevalent network simulator NS-2 with ML module. The experiment shows that ABS outperforms state-of-the-art buffer sizing solutions by up to 38.23x under various network environments.

Traffic Engineering (TE) is a widely-adopted network operation to optimize network performance and resource utilization. Destination-based routing is supported by legacy routers and more readily deployed than flow-based routing, where the forwarding entries could be frequently updated by TE to accommodate traffic dynamics. However, as the network size grows, destination-based TE could render high time complexity when generating and updating many forwarding entries, which may limit the responsiveness of TE and degrade network performance. In this paper, we propose a novel destination-based TE solution called FlexEntry, which leverages emerging Reinforcement Learning (RL) to reduce the time complexity and routing update overhead while achieving good network performance simultaneously. For each traffic matrix, FlexEntry only updates a few forwarding entries calledcritical entriesfor redistributing a small portion of the total traffic to improve network performance. These critical entries are intelligently selected by RL with traffic split ratios optimized by Linear Programming (LP). We find out that the combination of RL and LP is very effective. Our simulation results on six real-world network topologies show that FlexEntry reduces up to 99.3% entry updates on average and generalizes well to unseen traffic matrices with near-optimal load balancing performance.

Emerging Software-Defined Networking (SDN) technique brings new opportunities to improve network performance. Some SDN-enabled programmable switches are deployed in legacy networks, and thus legacy and programmable switches could coexist, generating hybrid SDNs. In this paper, we study the node upgrade for layer-2 hybrid SDN and propose Shortcutter to accelerate the transmission. Preliminary results show that the proposed Shortcutter can reduce the forwarding path’s length 7% on average, compared with baseline solutions.

Network operators usually adopt Traffic Engineering (TE) to configure the routing in their networks to achieve good load balancing performance and high resource utilization. While centralized TE can effectively improve network performance with a global view of the network, distributed TE has been considered as an alternative to manage large-scale networks that are usually partitioned into multiple regions. However, it is challenging for distributed TE to reach a global optimal performance since each region can make its local routing decisions only based on partially observed network states. In this paper, we propose a novel distributed TE scheme called FedTe, which leverages supervised learning coupled with a collaborative approach to improve the overall load balancing performance for multi-region networks. FedTe learns from the global optimal routing strategy in a centralized offline manner and predicts the optimal distribution of cross-region traffic among different regions through distributed deployment in real time. The predicted cross-region traffic distribution is integrated with measured local traffic to construct each region’s optimal regional traffic matrix, which is used to perform intra-region TE optimization. FedTe can also handle dynamic traffic variation and link failures with a 2-layer hierarchical graph neural network architecture. To validate the effectiveness of the proposed scheme, we evaluate FedTe with two real-world network topologies and a large-scale synthetic topology. Extensive evaluation results show that FedTe can achieve near-optimal load balancing performance and outperform state-of-the-art distributed TE approaches by up to 28.9% on average.

Traffic Engineering (TE) has been applied to optimize network performance by routing/rerouting flows based on traffic loads and network topologies. To cope with network dynamics from emerging applications, it is essential to reroute flows more frequently than today’s TE to maintain network performance. However, existing TE solutions may introduce considerable Quality of Service (QoS) degradation and service disruption since they do not take the potential negative impact of flow rerouting into account. In this paper, we apply a new QoS metric named network disturbance to gauge the impact of flow rerouting while optimizing network load balancing in backbone networks. To employ this metric in TE design, we propose a disturbance-aware TE called DATE, which uses Reinforcement Learning (RL) to intelligently select some critical flows between nodes for each traffic matrix and reroute them using Linear Programming (LP) to jointly optimize network performance and disturbance. DATE is equipped with a customized actor-critic architecture and Graph Neural Networks (GNNs) to handle dynamic traffic and single link failures. Extensive evaluations show that DATE can outperform state-of-the-art TE methods with close-to-optimal load balancing performance while effectively mitigating the 99th percentile network disturbance by up to 31.6%.

Although many distributed denial of service (DDoS) attacks detection algorithms have been proposed and even some of them have claimed high detection accuracy, DDoS attacks are still a major problem for network security. The latent and inherent problems of these detection algorithms are 1) Requirement of both normal and attack data for building detection models, and 2) Almost inability to detect novel and unknown DDoS attacks. To conquer the problems, this paper proposes an AutoEncoder based DDoS attacks Detection Framework (AE-D3F), which only uses normal traffic to build the detection model and is able to update itself automatically as time goes. Experimental results on synthetic and public traffic show that our AE-D3F can not only achieve 82.00% detection rate (DR) with 0 false positive rate (FPR), better than classical anomaly detection approaches, but also detect novel and unknown attacks.

Traditional Traffic Engineering (TE) solutions can achieve the optimal or near-optimal performance by rerouting as many flows as possible. However, they do not usually consider the negative impact, such as packet out of order, when frequently rerouting flows in the network. To mitigate the impact of network disturbance, one promising TE solution is forwarding the majority of traffic flows using Equal-Cost Multi-Path (ECMP) and selectively rerouting a few critical flows using Software-Defined Networking (SDN) to balance link utilization of the network. However, critical flow rerouting is not trivial because the solution space for critical flow selection is enormous. Moreover, it is impossible to design a heuristic algorithm for this problem based on fixed and simple rules, since rule-based heuristics are unable to adapt to the changes of the traffic matrix and network dynamics. In this paper, we propose CFR-RL (Critical Flow Rerouting-Reinforcement Learning), a Reinforcement Learning-based scheme that learns a policy to select critical flows for each given traffic matrix automatically. CFR-RL then reroutes these selected critical flows to balance link utilization of the network by formulating and solving a simple Linear Programming (LP) problem. Extensive evaluations show that CFR-RL achieves near-optimal performance by rerouting only 10%-21.3% of total traffic.

Intradomain traffic engineering (TE) has become an indispensable tool for Internet service providers (ISPs) to optimize network performance and utilize network resources efficiently. Various explicit routing TE methods were recently proposed and have been able to achieve high network performance. However, explicit routing has high complexity and requires large ternary content addressable memories (TCAMs) in the routers. Moreover, it is costly to deploy explicit routing in IP networks. In this paper, we present an approach, called generalized destination-based multipath routing (GDMR), to achieve the same high performance as explicit routing. The main contribution of this paper is that we prove that an arbitrary explicit routing can be converted to a loop-free destination-based routing without any performance penalty for a given traffic matrix. We present a systematic approach including a heuristic algorithm to realize GDMR. Extensive evaluation demonstrates the effectiveness and robustness of GDMR.

Classical traffic engineering (TE) methods calculate the optimal routing based on a single traffic matrix. However, they are unable to handle unexpected traffic changes. Thus, it is of interest to find a good routing configuration to accommodate multiple possible traffic scenarios. There are two major approaches to achieve load balancing for multiple traffic matrices: destination-based routing and explicit routing. It has been shown that explicit routing performs better than destination-based routing for multiple traffic matrices. However, explicit routing has high complexity and requires large Ternary Content Addressable Memory (TCAM) in the routers. Thus, it is power hungry and unscalable. This paper presents an approach called hybrid routing to achieve load balancing for multiple traffic matrices with low complexity and good scalability. Our basic idea is to complement destination-based routing with a small number of explicit routing forwarding entries to take advantage of both two routing approaches. Hybrid routing greatly reduces the number of forwarding entries compared with pure explicit routing. This has great value for practice in that the scheme requires very small TCAM to implement. Hybrid routing is very suitable for implementation using SDN. A heuristic algorithm is developed to obtain the near-optimal hybrid routing configuration. Extensive evaluation demonstrates the effectiveness of hybrid routing. The results show that hybrid routing achieves near-optimal load balancing compared with pure explicit routing. In particular, hybrid routing saves at least 84.6% TCAM resources in all practical networks used in our evaluation.

Classical TE methods calculate the optimal routing based on a known traffic matrix. However, they are unable to handle unexpected traffic changes. Thus, various methods were proposed in recent years, such as online dynamic TE and robust static routing TE. However, online dynamic TE requires additional overhead on routers for information dissemination and suffers from the transient disruptions during routing protocol convergence, while using one robust static routing to accommodate a wide range of traffic scenarios is unable to ensure near optimality of performance for each individual traffic scenario. This paper presents an approach called dynamic hybrid routing (DHR) to achieve load balancing for a wide range of traffic scenarios. Our basic idea is to configure several routing policies in advance and then dynamically rebalance traffic by applying different preconfigured routing policy to react to traffic fluctuations. Each routing policy composes of a common basic destination-based routing and a few complementary explicit routing forwarding entries for a small set of selected ingress/egress node pairs. We design a method to find the near-optimal dynamic hybrid routing configuration. Extensive evaluation demonstrates the effectiveness of DHR. We show that DHR achieves nearoptimal load balancing and thus obtain about at least 96% throughput compared to optimal routing for each individual traffic scenario with very low overhead.

Equal-Cost Multipath (ECMP) routing has been widely adopted to perform load balancing. With ECMP, a router can maintain multiple next hops for a destination IP prefix. The most common method used by such routers is to split traffic with per-flow basis evenly among those next hops. This approach, although simple, cannot achieve optimal load balancing. In this paper we study the optimal configuration of weighted ECMP, where traffic splitting among the available paths is based on a set of pre-determined ratios. The contribution of this paper is two-fold. First, we develop a model to obtain the split ratios such that the overall network end-to-end delay is optimized. This is important because better delay performance is a result of better bandwidth allocation and has a direct impact on application, while most existing work tries to minimize the traffic load on the most utilized link. Second, we prove that the problem can be first solved by using a simple flow-based routing model and then converting the results to apply to IP networks, where destination-based forwarding is used. We present a heuristic algorithm to find the near-optimal weight configurations and demonstrate the effectiveness of the algorithm using computer simulations.

Hotspot congestion control is one of the most challenging issues when designing a high-throughput low-latency network on the chip (NOC). When a destination node is overloaded, it starts pushing back the packets destined for it, which in turns blocks the packets destined for other nodes. How to detect the occurrence(s) of hotspot and notify all source nodes to regulate their traffic to the hotspot node(s) can be quite complex because of potentially high volume of information to be collected and the non-negligible latency between the detection point of congestion and the source nodes. In this paper, we propose an effective end-to-end flow control scheme, called HOPE (HOtspot PrEvention), to resolve the hotspot congestion problem for the Clos network on the chip (CNOC). Specifically, HOPE regulates the injected traffic rate proactively by estimating the number of packets inside the switch network destined for each destination and applying a simple stop-and-go protocol to prevent hotspot traffic from jamming the internal links of the network. We evaluate HOPE's overall performance and the required hardware. Extensive simulation results based on both static and dynamic hotspot traffic patterns confirm that HOPE can effectively regulate hotspot flows and improve system performance. Our hardware analysis shows that HOPE has very small logic overhead.