Mind the paths you choose: Speeding up segment routing-based traffic engineering with path preprocessing

The diversity of methodologies and applications in the literature for the traffic engineering, performance modelling and analysis of convergent multiservice heterogeneous networks attests to the breath and richness of recent research and developments towards the design and dimensioning of the next and future generation Internets. Heterogeneous Networks: Traffic Engineering, Performance Evaluation Studies and Tools describes recent advances in networks of diverse technology reflecting the state-of-the-art technology and research achievements in traffic engineering, performance evaluation studies and tools worldwide. Technical topics presented in the book include: Traffic Modelling and Characterisation Queueing and Interconnection Networks Performance Evaluation Studies TCP Performance Analysis Congestion Control Application Layer Multicast Numerical and Software Tools; This book contains recently extended research papers, which have their roots in the series of the HET-NETs International Working Conferences focusing on the Performance Modelling and Evaluation of Heterogeneous Networks under the auspices of the EU Networks of Excellence Euro-NGI and Euro-FGI. Heterogeneous Networks: Traffic Engineering, Performance Evaluation Studies and Tools is ideal for personnel in computer/communication industries as well as academic staff and master/research students in computer science, operational research, electrical engineering and telecommunication systems and the Internet. CACHE_VSTAMP

Traffic Engineering (TE) is important and necessary in order to fully utilize the existing network resources and reduce capital expenditure. Given its importance, many algorithms have been proposed in the literature. Unfortunately, there is still not a consistent methodology to evaluate these algorithms. Worse yet, some variants of the traffic engineering problem are known to be NP-hard. Thus, given a particular TE problem, in general, it is not possible to know the optimal solution; hence, it is difficult to assess how a particular heuristic algorithm performs. Even though several heuristic algorithms could be used to validate each other, the best solution from these algorithms could still be very far away from the optimal solution. We propose a novel methodology to evaluate TE algorithms. In this methodology, we construct TE problems with known optimal solutions and we then use these TE problem instances to test the performance of TE algorithms. We found that some TE algorithms perform poorly, and the result deviates from the optimum further as the problem size gets bigger. Our results suggest that there is large room for algorithm improvements and further research is required. Even though we only demonstrate the power of the methodology in the context of traffic engineering algorithms, the methodology is general enough that it could be applied in many other areas as well.

Inter-AS outbound Traffic Engineering (TE) aims to control the flow of traffic exiting an AS so as to optimize inter-AS TE objectives such as load balancing among multiple downstream ASes. The inter-AS traffic matrix is fundamental input for outbound TE and its accuracy is essential for the target performance to be achieved. However, deriving an accurate traffic matrix is far from trivial. This paper proposes a robust approach for outbound TE to manage traffic demand uncertainty through Scenario-based Robust Optimization. The objective of this robust outbound TE is to minimize the worst-case maximum inter-AS link utilization across a set of inter-AS traffic matrices while minimizing the performance deviation from the optimal solutions. Simulation results reveal that the robust outbound TE is capable of achieving reasonably good performance under all the given traffic matrices while non-robust approaches are not.

This study aims to analyze the influencing factors and mechanisms of investment and financing risks in transportation projects so that regions do not restrict the transportation investment and financing risk models in all areas to achieve intelligent transportation financial risk assessment. Firstly, the investment and financing modes are studied and analyzed. According to the analysis of intellectual investment and the financing report of traffic engineering infrastructure, a traffic engineering investment and a financing model based on intelligent computing is established, which is based on artificial intelligence (AI) big data analysis technology. Secondly, the investment and the financing risk model of traffic engineering is established based on multimodal learning. Finally, the urban traffic engineering of Xi'an is taken as the research object. Based on its investment and financing data in the construction of urban roads, the risk assessment is carried out. Combined with risk influencing factors, the accuracy of the intelligent calculation in the risk assessment model is calculated. Different grades of urban transportation projects have different risks in the investment and financing of transportation projects. The results show that different levels of urban transport projects have different risks in the investment and financing (IAF) performance of transport projects. Among them, the risk index of the first-class project is the highest, reaching 0.55. The risk index of the second-class project is 0.49. The results before and after using the flow engineering IAF risk model are compared. In the test results of traffic engineering risk, all target risks did not increase after the AI-based traffic engineering IAF is tested. The model test results for credit risk and financial risk are the highest at 70 and 60, respectively. Combined with the actual urban development situation, this study can provide investment and financing risk models for urban transportation projects in different regions and provide a reference for the resource control of transportation projects. This study uses AI to learn and analyze traffic engineering investment and financing data and more accurately provide data references for traffic engineering investment and financing risk models.

Traffic engineering in IP/WDM networks can be pursued in two fashions: overlay and integrated. With overlay traffic engineering, operations in one layer can be independent from those in the other layer. Traffic engineering solutions developed for either IP networks or WDM networks can be directly applied to each layer, respectively. At the present stage, an anticipated first step towards IP over WDM is to hook up IP router interfaces to optical add/drop multiplexer client add/drop ports. This forms an overlay network in which WDM serves as the server layer and IP behaves like the client layer. This overlay client-server network is a natural match to the notion of overlay traffic engineering. With integrated traffic engineering, optimization with respect to selected objectives is pursued coordinately across both the IP and WDM layers. It is made possible by mechanisms such as multi-protocol lambda switching that allows a unified control plane across both layers. With the emergence of more sophisticated hardware that integrates functionality of both IP and WDM at each network element, integrated traffic engineering can be performed more naturally and efficiently.

This paper proposes a Traffic Engineering (TE) scheme for routing in a target network topology that supports OpenFlow using a metric called congestion contribution and Software-Defined Networking (SDN). According to the network traffic volume has been increasing dramatically, it is clear that the congestion, which is caused by the imbalance of the traffic flow, can be occurred. Furthermore, an Interior Gateway Protocol (IGP) that is used for an Autonomous System (AS) also can cause congestion because it uses snapshot information of network topology to make the shortest path-based routing table. Due to the reasons mentioned above, much research on TE has been conducting to enhance the network performance. However, as an example of TE, the Multi-Protocol Label Switching (MPLS) using Resource Reservation Protocol (RSVP) is not flexible in reconfiguring the labeled path, and it has resource overhead that cannot be ignored. Thus, we propose a TE scheme using SDN to overcome the limitations in the target network. In order to show the feasibility of the proposed scheme, TE application predicts the bandwidth utilization for network devices in SDN networks. It also determines the near-optimized routing path for the maximization of the average bandwidth utilization with avoiding the congestion nodes. Our scheme can configure network devices (e.g., SDN switches) along the paths of traffic flows so that the SDN network can have maximum bandwidth utilization. Through experiments, it is shown that our proposed scheme can utilize the average bandwidth of the whole topology up to 66%.

In the Internet today, traffic engineering is performed assuming that the offered traffic is inelastic. In reality, end hosts adapt their sending rates to network congestion, and network operators adapt the routing to the measured traffic. This raises the question of whether the joint system of congestion control (transport layer) and routing (network layer) is stable and optimal. Using the established optimization models for TCP and traffic engineering as a basis, we find the joint system can be stablized and often maximizes aggregate user utility. We prove that both stability and optimality of the joint system can be guaranteed for sufficiently elastic traffic simply by tuning the cost function used for traffic engineering. Then, we present a new algorithm that adapts on a smaller timescale to changes in traffic distribution and is more robust to large traffic bursts. Uniting the network and transport layers in a multi-layer approach, this algorithm, Distributed Adaptive Traffic Engineering (DATE), jointly optimizes the goals of end users and network operators and reacts quickly to avoid bottlenecks. Simulations demonstrate that DATE converges quickly.

This paper reviews the research on Traffic Engineering (TE) based on Software-Defined Networking (SDN). Firstly, the shortcomings of traditional network architectures in managing complex traffic are introduced, and it is pointed out that SDN provides a new solution for traffic engineering through the features of separation of control and data planes, centralized control, and programmability, but at the same time, it faces many challenges. Then the three-layer architecture of SDN and its role in traffic engineering are described, and the goals, research directions and application scenarios of traffic engineering are outlined. The application of machine learning (ML) methods in SDN traffic engineering is then discussed in detail, including the advantages of deep learning (DL) in traffic prediction and classification, and the potential of reinforcement learning (RL) in routing optimization and resource scheduling. In addition, four mechanisms for optimization algorithms to merge with ML are analyzed. The integration of SDN with new architectures such as edge computing and network slicing, as well as the prospects for the application of intent-driven networking are then explored. Finally, the opportunities and challenges of SDN traffic engineering are summarized, including the advantages of flexible traffic management and cross-domain optimization, as well as the problems of network scale, security, real-time and multi-tenant management, which provide directions for future research.

Traffic engineering can be viewed as the mechanisms that are used to reoptimise network performance on traffic variation time scales. The traffic engineering process involves measurements, detection of performance problems, and the adjustment of routes and various quality of service mechanisms in order to restore the desired performance. Traffic engineering models are essential in this feedback loop. In this paper we provide an overview of our recent contributions to analytical models for traffic engineering of elastic traffic in internets, and discuss the role that such models could play in automated or intelligent traffic engineering.

Chapter 8 - GMPLS and Traffic Engineering

Internet traffic engineering using multi-protocol label switching (MPLS)

The 5G technology brings new services such as the faster speeds of enhanced mobile broadband, mass deployment of machine-type communications, and ultra-reliable low-latency services. Consequently, it comes to a critical need to design a network infrastructure that can easily and efficiently support various service. Service Level Agreement (SLA) based services were traditionally established in the Internet Protocol / Multiprotocol Label Switching (IP/MPLS) network by implementing traffic engineering using protocols such as Resource Reservation Protocol - Traffic Engineering (RSVP-TE). However, it is not easy for some network operators to implement the current 5G development as mentioned earlier. Segment Routing (SR) presents as a technology to minimize network complexity and provide a fundamental for supporting SLA services, flexibility, reliability, and overall performance in the core of a network. In this study, we simulate and implement SR into MPLS Layer 3 Virtual Private Network (L3VPN) intra-AS domain to show that (i) reducing any transport protocol will not affect the service performance; instead, it simplifies service provisioning and problem handling (ii) when a network failure happens in the network, using Topology Independent Loop-Free Alternate (TI-LFA) and microloops avoidance can provide better failover time than conventional Fast Re-Route (FRR) (iii) SLA path differentiation for services within the same Virtual Routing and Forwarding (VRF) can be applied using Segment Routing - Traffic Engineering (SR-TE). It will be an essential foundation for network slicing in the future.

Programmable networks have transformed network management, particularly within Traffic Engineering (TE), which aims to optimize data flow across the network. By offering flexibility and efficiency, programmable networks facilitate advanced traffic and resource management capabilities. Key technologies in this area, such as Software-Defined Networking (SDN) and Programmable Data Planes (PDPs), enable real-time, dynamic routing and network control. These frameworks further allow the integration of Artificial Intelligence (AI) mechanisms to automate and refine network management processes. Specifically, Reinforcement Learning (RL) is a promising approach for TE applications due to its adaptability to evolving network conditions. We provide a survey with an in-depth review of TE solutions that leverage RL and programmable networks to improve network performance, including works from 2018 to 2025. The proposed survey includes a timely update on the state-of-the-art and presents a taxonomy that categorizes existing solutions based on RL principles and specific TE objectives. Our analysis highlights key findings and insights, contributing valuable knowledge for implementing TE mechanisms within programmable networks.

Recently several investigations in the field of multilayer traffic engineering (TE) mainly focusing on random traffic demands have been carried out. In this paper, we propose different TE strategies for EXC/OXC networks including electrical traffic grooming and lightpath rerouting. Unlike existing investigations that consider only random demands for TE, we consider simultaneously scheduled and random traffic demands. Scheduled demands correspond to guaranteed services whereas random demands correspond to best-effort services. In order to outline the impact of traffic predictability on TE efficiency, we introduce the new concept of semi-random traffic demand that corresponds to random demands with predictable holding time. For a given network and a given traffic scenario, numerical simulations outline the huge gap between the rejection ratio of random demands and that of semi-random demands. In order to improve the rejection ratio of random demands, we propose to proceed for such demands to a time limited resource reservation (TLRR). The choice of the value of reservation window Delta strongly depends on the arrival and life duration processes of random demands. We show that it is possible to achieve comparable rejection ratios for both random and semi-random demands

Chapter 23 - Routing and Traffic Engineering using MPLS