Traffic Engineering Algorithms Research Articles

A Reinforcement Learning-Based Traffic Engineering Algorithm for Enterprise Network Backbone Links

Large enterprise networks typically rely on expensive, high-speed backbone links to connect multiple campuses across diverse regions. As the volume of traffic traversing these backbone links increases, traffic engineering techniques are employed to filter or redirect traffic flows. Nevertheless, simple rerouting strategies can introduce business disruptions such as packet reordering, which significantly impact the user experience. To address this issue, we introduce an enhanced traffic scheduling algorithm named Critical Flow Rerouting with Weight- Reinforcement Learning(CFRW-RL), which builds upon the critical flow rerouting-reinforcement learning (CFR-RL) algorithm. CFRW-RL incorporates the principles of reinforcement learning, accounting for both the weights and classifications of data flows. This approach enables the algorithm to prioritize flows with lower weights for rerouting. The simulation results demonstrate that CFRW-RL significantly minimizes the rerouting of high-priority business flows and reduces business interference compared with the CFR-RL algorithm and that it maintains a similar computational complexity.

Exploring the Benefits of Carbon-Aware Routing

Carbon emissions associated with fixed networks can be significant. However, accounting for these emissions is hard, requires changes to deployed equipment, and has contentious benefits. This work sheds light on the benefits of carbon aware networks, by exploring a set of potential carbon-related metrics and their use to define link-cost in carbon-aware link-state routing algorithms. Using realistic network topologies, traffic patterns and grid carbon intensity, we identify useful metrics and limitations to carbon emissions reduction. Consequently, a new heuristic carbon-aware traffic engineering algorithm, CATE, is proposed. CATE takes advantage of carbon intensity and routers' dynamic power consumption, combined with ports power down, to minimize carbon emissions. Our results show that there is no silver bullet to significant carbon reductions, yet there are promising directions without changes to existing routers' hardware.

A novel optimized routing algorithm for QoS traffic engineering in SDN-based mobile networks

Nodes in mobile networks change position randomly and quickly so modify their attachment point to the network. A most suitable method for handoff in core of the network is MPLS. For MPLS-TE, the most critical issue is LSP configuration. The goal of the routing algorithms in MPLS-TE is resource management and satisfy QoS metrics. The promising new SDN architecture can be a useful help to MPLS-TE due to its good features. This paper introduces a novel and low complexity SDN-MPLS algorithm. The proposed algorithm advances bandwidth-restricted routing as it trade-offs between network load balancing, route length, energy-saving with low complexity in mobile networks.

An intelligent routing optimization strategy based on deep reinforcement learning

Finding the optimal strategy in network routing has always been a NP hard problem. Due to the complexity and dynamics of network traffic, the existing intelligent routing algorithms have poor generalization ability. Therefore, this paper proposes an intelligent routing strategy based on deep reinforcement learning, and with the help of SDN control can dynamically collect network traffic distribution information, can dynamically adjust the routing strategy. Compared with traffic engineering algorithms such as TCMP and DRL-TE, the end-to-end delay is optimized under different throughput.

SOSW: scalable and optimal nearsighted location selection for fog node deployment and routing in SDN-based wireless networks for IoT systems

In a fog computing (FC) architecture, cloud services migrate towards the network edge and operate via edge devices such as access points (AP), routers, and switches. These devices become part of a virtualization infrastructure and are referred to as “fog nodes.” Recently, software-defined networking (SDN) has been used in FC to improve its control and manageability. The current SDN-based FC literature has overlooked two issues: (a) fog nodes’ deployment at optimal locations and (b) SDN best path computation for data flows based on constraints (i.e., end-to-end delay and link utilization). To solve these optimization problems, this paper suggests a novel approach, called scalable and optimal near-sighted location selection for fog node deployment and routing in SDN-based wireless networks for IoT systems (SOSW). First, the SOSW model uses singular-value decomposition (SVD) and QR factorization with column pivoting linear algebra methods on the traffic matrix of the network to compute the optimal locations for fog nodes, and second, it introduces a new heuristic-based traffic engineering algorithm, called the constraint-based shortest path algorithm (CSPA), which uses ant colony optimization (ACO) to optimize the path computation process for task offloading. The results show that our proposed approach significantly reduces average latency and energy consumption in comparison with existing approaches.

On Deep Reinforcement Learning for Traffic Engineering in SD-WAN

The demand for reliable and efficient Wide Area Networks (WANs) from business customers is continuously increasing. Companies and enterprises use WANs to exchange critical data between headquarters, far-off business branches and cloud data centers. Many WANs solutions have been proposed over the years, such as: leased lines, Frame Relay, Multi-Protocol Label Switching (MPLS), Virtual Private Networks (VPN). Each solution positions differently in the trade-off between reliability, Quality of Service (QoS) and cost. Today, the emerging technology for WAN is Software-Defined Wide Area Networking (SD-WAN) that introduces the Software-Defined Networking (SDN) paradigm into the enterprise-network market. SD-WAN can support differentiated services over public WAN by dynamically reconfiguring in real-time network devices at the edge of the network according to network measurements and service requirements. On the one hand, SD-WAN reduces the high costs of guaranteed QoS WAN solutions (as MPLS), without giving away reliability in practical scenarios. On the other, it brings numerous technical challenges, such as the implementation of Traffic Engineering (TE) methods. TE is critically important for enterprises not only to efficiently orchestrate network traffic among the edge devices, but also to keep their services always available. In this work, we develop different kind of TE algorithms with the aim of improving the performance of an SD-WAN based network in terms of service availability. We first evaluate the performance of baseline TE algorithms. Then, we implement different deep Reinforcement Learning (deep-RL) algorithms to overcome the limitations of the baseline approaches. Specifically, we implement three kinds of deep-RL algorithms, which are: policy gradient, TD- λ and deep Q-learning. Results show that a deep-RL algorithm with a well-designed reward function is capable of increasing the overall network availability and guaranteeing network protection and restoration in SD-WAN.

Algorithms for ribosome traffic engineering and their potential in improving host cells' titer and growth rate

mRNA translation is a fundamental cellular process consuming most of the intracellular energy; thus, it is under extensive evolutionary selection for optimization, and its efficiency can affect the host's growth rate. We describe a generic approach for improving the growth rate (fitness) of any organism by introducing synonymous mutations based on comprehensive computational models. The algorithms introduce silent mutations that may improve the allocation of ribosomes in the cells via the decreasing of their traffic jams during translation respectively. As a result, resources availability in the cell changes leading to improved growth-rate. We demonstrate experimentally the implementation of the method on Saccharomyces cerevisiae: we show that by introducing a few mutations in two computationally selected genes the mutant's titer increased. Our approach can be employed for improving the growth rate of any organism providing the existence of data for inferring models, and with the relevant genomic engineering tools; thus, it is expected to be extremely useful in biotechnology, medicine, and agriculture.

A Time Granular Analysis of Software Defined Wireless Mesh Based IoT (SDWM-IoT) Network Traffic Using Supervised Learning

The ceaseless increase in the number of the wireless Internet of Things (IoT) devices has resulted in the need of different traffic engineering techniques to manage the massive network traffic. Wireless Mesh Networks (WMNs) are an important constituent part of the wireless IoT networks, and are helpful to route the IoT networks’ traffic over long distances. The WMN devices are powerful in comparison to the IoT sensor devices, and are suitable to run the traffic engineering algorithms. To further improve the performance of the WMNs, Software Defined Networking can be used. Its unique features like global visibility, agility, etc., guarantee the optimal network management. As granularity plays an important role in data analysis and none of the existing works has discussed a time granularity based network data analysis, this work tries to offer a time granular analysis of Software Defined Wireless Mesh based IoT (SDWM-IoT) network’s traffic using supervised learning approaches. A time granular analysis helps to explore the functional traits of the data at the Coarse, Medium, and Fine granularity levels. This assists in divulging and understanding the hidden characteristics and behaviour of the SDWM-IoT network’s data based on varying time granularity, respectively. Some well known supervised learning algorithms are used to offer an in-depth analysis of the traffic, and to draw the relevant conclusions. Different variants of Decision Tree, Support Vector Machine and K-Nearest Neighbour (KNN) are used to analyze the traffic and achieve a reliable accuracy rate of more than 90%. Among all the variants, fine-KNN produces the best accuracy for most of the traffic classes with a rate of more than 98%. In addition to this, a tenfold cross-validation technique is also used to prevent the the chances of over-fitting.

Termite inspired algorithm for traffic engineering in hybrid software defined networks.

In the era of Internet of Things and 5G networks, handling real time network traffic with the required Quality of Services and optimal utilization of network resources is a challenging task. Traffic Engineering provides mechanisms to guide network traffic to improve utilization of network resources and meet requirements of the network Quality of Service (QoS). Traditional networks use IP based and Multi-Protocol Label Switching (MPLS) based Traffic Engineering mechanisms. Software Defined Networking (SDN) have characteristics useful for solving traffic scheduling and management. Currently the traditional networks are not going to be replaced fully by SDN enabled resources and hence traffic engineering solutions for Hybrid IP/SDN setups have to be explored. In this paper we propose a new Termite Inspired Optimization algorithm for dynamic path allocation and better utilization of network links using hybrid SDN setup. The proposed bioinspired algorithm based on Termite behaviour implemented in the SDN Controller supports elastic bandwidth demands from applications, by avoiding congestion, handling traffic priority and link availability. Testing in both simulated and physical test bed demonstrate the performance of the algorithm with the support of SDN. In cases of link failures, the algorithm in the SDN Controller performs failure recovery gracefully. The algorithm also performs very well in congestion avoidance. The SDN based algorithm can be implemented in the existing traditional WAN as a hybrid setup and is a less complex, better alternative to the traditional MPLS Traffic Engineering setup.

Traffic Engineering in Partially Deployed Segment Routing Over IPv6 Network With Deep Reinforcement Learning

Segment Routing (SR) is a source routing paradigm which is widely used in Traffic Engineering (TE). By using SR, a node steers a packet through an ordered list of instructions called segments. By some extensions of interior gateway protocol, SR can be applied to IP/MPLS or IPv6 network without signal protocol. SR over IPv6 (SRv6) is attracting wide attention because of its interoperation ability with IPv6. However, upgrading the existing IPv6 network directly to a full SRv6 one can be difficult, because large-scale equipment replacement or software upgrade may cause economic and technical problems. TE in partially deployed SR network is becoming a hot research topic. In this paper, we propose the TE algorithm Weight Adjustment-SRTE (WA-SRTE) in partially deployed SRv6 network, in which SRv6 capable nodes are dispersedly deployed. Our objective is to minimize the network’s maximum link utilization. WA-SRTE converts the TE problem into a Deep Reinforcement Learning problem and optimizes the OSPF weight, SRv6 node deployment and traffic paths simultaneously. Besides, traffic variation is also considered and we use a representative Traffic Matrix (TM) to epitomize the traffic characteristics over a period of time. Experiments demonstrate that with 20% to 40% of the SRv6 nodes deployed, we can achieve TE performance as good as in a full SR network for the experiment topologies. The results with WA remarkably outperform the results without it. Our algorithm also gets near-optimal results with changing traffic.

Energy-aware traffic engineering in IP networks using non-dominated sorting genetic II algorithm

Wide spreading of computer networks along with increasing traffic demand throughout the Internet caused a dramatic increase in energy consumption by networking devices and Internet infrastructure. Energy-aware traffic engineering is a promising approach towards green networking to achieve a trade-off between energy saving and network utilisation in backbone networks. In this paper, we propose to use non-dominated sorting genetic algorithm (NSGA-II) for energy-aware intra-domain traffic engineering. This algorithm tries to make a trade-off between maximum link utilisation (MLU) and energy reservation. For each pair of network topology and traffic matrix, NSGA-II computes the optimal set of links to put to sleep so that the resulting topology would be able to carry the traffic demand. We developed a simulator to evaluate the performance of our mechanism. The results of comprehensive evaluations show that our energy-aware TE approach increases the network performance in terms of energy conservation by 50% at the cost of slight increase in maximum link utilisation.

Toward a Scalable, Robust, and QoS-Aware Virtual-Link Provisioning in SDN-Based ISP Networks

In this paper, we propose a control framework for virtual-link provisioning in software defined networking (SDN)-based Internet service provider (ISP) networks. Within the problem's scope, we have analyzed the traffic engineering (TE), quality of service (QoS) provisioning, network stability, and failure recovery issues. The proposed solution aims to optimize the network throughput subject to multiple QoS constraints and link-failure robustness constraints that are defined in service level agreements (SLAs), while minimizing the time-average rate of routing updates according to ISP preferences. In contrast to the conventional TE mechanisms which periodically optimize all the routing rules, our solution is based on a bi-objective optimization model which jointly optimizes the routing and the number of flow-table updates. In this way, the reconfiguration overhead of TE is reduced, thus the SDN controller could perform TE more frequently, in order to quickly and efficiently respond to traffic and topology changes. To generate a set of Pareto optimal solutions for the proposed optimization model we use the augmented $\epsilon $ -constrained method. The most efficient trade-off solution from the Pareto set is chosen by the Lypunov drift-plus-penalty algorithm. In order to make the proposed approach scalable enough for large network topologies, we also propose a heuristic TE algorithm that runs in polynomial time. The proposed solution is validated via simulations and experimentally, and it is shown that it outperforms conventional periodic TE techniques. The advantages of our proposal over a state-of-the-art TE technique in terms of the capability to adjust the reconfiguration rate based on the threshold set by ISP is demonstrated. In the analysis, a special attention is paid to the impact of proactive failure protection mechanisms on the network service capacity.

Traffic engineering in a software-defined network based on the decision-making method

One of the main control tasks in a computer network is to organize an effective system of information delivery; this task is of particular relevance in the software defined network. Conventional routing tools do not meet the requirements to service quality and the requirements for equitable distribution of congestion along communication channels. Routing in conventional networks is performed by the shortest path search based on a specified parameter, but these tools do not provide sufficient agility when changing routes in the network. Another drawback is the need to transmit regular updates of routing information by passing the service traffic, thereby dramatically increasing the congestion and reducing the throughput. At present, the most effective way to ensure the assigned quality of service parameters, as well as a promising solution to organize efficient routing under conditions of uncertainty, is a software defined network. This new networking paradigm makes it possible to simplify the process of managing the network, to significantly enhance the use of network resources, and to reduce operating costs. One of the main advantages of such a network is control at the upper levels of the reference model, which makes it possible to simplify both the process of network management and the process to manage traffic in corporate networks and data center networks. A new approach to traffic design in a software defined network has been proposed that employs the making-decision theory oriented towards routing exactly in such networks. If there is a «problematic area» and there is the need to overcome it, the decision-making theory under conditions of uncertainty is used, since the probability of selecting the best way to circumvent it accounts for the patterns in transmitted traffic. Such a method makes it possible to reduce the loss of inelastic traffic that is an important component of the overall amount of transmitted information. From a practical point of view, the algorithm constructed in this work, when compared to known algorithms for traffic engineering, improves the quality of service in software defined networks

A Constrained Shortest Path Scheme for Virtual Network Service Management

Virtual network services that span multiple data centers are important to support emerging data-intensive applications in fields such as bioinformatics and retail analytics. Successful virtual network service composition and maintenance requires flexible and scalable “constrained shortest path management” both in the management plane for virtual network embedding (VNE) or network function virtualization service chaining (NFV-SC), as well as in the data plane for traffic engineering (TE). In this paper, we show analytically and empirically that leveraging constrained shortest paths within recent VNE, NFV-SC and TE algorithms can lead to network utilization gains (of up to 50%) and higher energy efficiency. The management of complex VNE, NFV-SC and TE algorithms can be, however, intractable for large scale substrate networks due to the NP-hardness of the constrained shortest path problem. To address such scalability challenges, we propose a novel, exact constrained shortest path algorithm viz., neighborhoods method (NM). Our NM uses novel search space reduction techniques and has a theoretical quadratic speed-up making it practically faster (by an order of magnitude) than recent branch-and-bound exhaustive search solutions. Finally, we detail our NM-based SDN controller implementation in a real-world testbed to further validate practical NM benefits for virtual network services.

Energy-aware traffic engineering in IP networks using non-dominated sorting genetic II algorithm

Wide spreading of computer networks along with increasing traffic demand throughout the Internet caused a dramatic increase in energy consumption by networking devices and Internet infrastructure. Energy-aware traffic engineering is a promising approach towards green networking to achieve a trade-off between energy saving and network utilisation in backbone networks. In this paper, we propose to use non-dominated sorting genetic algorithm (NSGA-II) for energy-aware intra-domain traffic engineering. This algorithm tries to make a trade-off between maximum link utilisation (MLU) and energy reservation. For each pair of network topology and traffic matrix, NSGA-II computes the optimal set of links to put to sleep so that the resulting topology would be able to carry the traffic demand. We developed a simulator to evaluate the performance of our mechanism. The results of comprehensive evaluations show that our energy-aware TE approach increases the network performance in terms of energy conservation by 50% at the cost of slight increase in maximum link utilisation.

Dragon: Scalable, Flexible, and Efficient Traffic Engineering in Software Defined ISP Networks

To optimize network cost, performance, and reliability, SDN advocates for centralized traffic engineering (TE) that enables more efficient path and egress point selection as well as bandwidth allocation. In this paper, we argue SDN-based TE for ISP networks can be very challenging. First, ISP networks often are very large in size, imposing significant scalability challenges to the centralized TE. Second, ISP networks usually have diverse types of links, switches, and cost models, leading to a complex combination of optimizations. Third, ISP networks not only have many choices of internal paths but also include rich selections of egress points and interdomain routes, unlike cloud/enterprise networks. To overcome these challenges, we present a novel TE application framework, called Dragon, for existing SDN control planes. To address the scalability challenge, Dragon consists of hierarchical and recursive TE algorithms and mechanisms that divide flow optimization problems into subtasks and execute them in parallel. Further, Dragon allows ISPs to express diverse objectives for different parts of their network. Finally, we extend Dragon to jointly optimize the selection of intradomain and interdomain paths. Using extensive evaluation on real topologies and prototyping with SDN controller and switches, we demonstrate that Dragon outperforms existing TE methods both in speed and optimality.

Topology-Preserving Traffic Engineering for Hierarchical Multi-Domain SDN

Decoupling of control and data plane in Software Define Networks (SDN) creates significant flexibility in network management. As networks are evolving into a complex multi-domain multi-layer architecture, traffic engineering across multiple domains and layers entails significant challenges for the control plane, especially when each separate administrative domain is not willing to disclose its network topology and resource information. In this paper, we present the first traffic engineering scheme for a multi-domain SDN by using a hierarchical control plane, in which each domain is managed by a local controller, and they are further controlled by a centralized root controller. To preserve the local topology of each domain, we extend the Network Information Base (NIB) into a hierarchical architecture as well, and the root controller maintains only an abstracted view of the lower-layer networks in its NIB. Then, we devise a communication protocol, which enables controllers at different layers and domains to work collaboratively on bandwidth allocation by reading to the hierarchical NIB. We also present an improved traffic engineering algorithm by considering bandwidth and delay simultaneously, to maximize the network utilization while respecting max-min fairness. Experiments on a 719-switches 5-domain network topology demonstrate that our proposal could drive the link utilization ratio to more than 85%.

Efficient algorithm for traffic engineering in Cloud-of-Things and edge computing

We propose a scheme for Cloud-of-Things and Edge Computing (CoTEC) traffic management in multi-domain networks. To direct the traffic flow through the service nodes, we assign a critical egress point for each traffic flow in the CoTEC network using multiple egress routers to optimize the traffic flow; this is known as Egress-Topology (ET). Therefore, the proposed ET incorporates traditional multi-topology routing in the CoTEC network to address the inconsistencies between service overlay routing and the border gateway protocol policies. Furthermore, the proposed ET introduces a number of programmable nodes that can be configured to ease the ongoing traffic on the network and re-align services among the other nodes in multi-domain networks. The results show that our algorithm has a lower execution time and better quality of service than that obtained without using our algorithm, thus allowing us to satisfy the flexibility and efficiency demands of multi-domain networks.

Optimization of Probe Train Size for Available Bandwidth Estimation in High-speed Networks

Available bandwidth parameter is a crucial characteristic in terms of networking and data transmission. The beforehand knowledge of its value and use of this parameter in various traffic engineering algorithms and QoS calculations is a key for high-efficient multigigabit data transport in nowadays networks. The challenge in available bandwidth estimations is not only in its accuracy and processing speed but also in the reduction of the amount of probe traffic injected into the network by keeping an adequate level of estimation accuracy. In this paper we extend existing active probing measurement algorithms for end-to-end available bandwidth estimation along with methods to reduce estimation times and amount of injected traffic while keeping measurement accuracy constant and even reducing the uncertainty of estimations. The main goal of this research was to detect a sufficient ratio of MTU, packet train size with the link capacity and available bandwidth (AvB) in up to 10 Gbps networks. In order to explore measurement accuracy under different conditions, a new tool for the AvB estimation named Kite2 has been developed and is presented in the paper. Comparative performance of AvB estimations using Kite2, Kite and Yaz is presented. Finally we calculate with statistical means dependency between the estimation error probability, measurement probing overhead and the measurement time.

Simultaneous routing and flow rate optimization in energy–aware computer networks

Abstract The issue of energy-aware traffic engineering has become prominent in telecommunications industry in the last years. This paper presents a two-criteria network optimization problem, in which routing and bandwidth allocation are determined jointly, so as to minimize the amount of energy consumed by a telecommunication infrastructure and to satisfy given demands represented by a traffic matrix. A scalarization of the criteria is proposed and the choice of model parameters is discussed in detail. The model of power dissipation as a function of carried traffic in a typical software router is introduced. Then the problem is expressed in a form suitable for the mixed integer quadratic programming (MIQP) solver. The paper is concluded with a set of small, illustrative computational examples. Computed solutions are implemented in a testbed to validate the accuracy of energy consumption models and the correctness of the proposed traffic engineering algorithm.