SDN Architecture and Southbound APIs for IPv6 Segment Routing Enabled Wide Area Networks

The SRv6 architecture (Segment Routing based on IPv6 data plane) is a promising solution to support services like Traffic Engineering, Service Function Chaining and Virtual Private Networks in IPv6 backbones and datacenters. The SRv6 architecture has interesting scalability properties as it reduces the amount of state information that needs to be configured in the nodes to support the network services. In this paper, we describe the advantages of complementing the SRv6 technology with an SDN based approach in backbone networks. We discuss the architecture of a SRv6 enabled network based on Linux nodes. In addition, we present the design and implementation of the Southbound API between the SDN controller and the SRv6 device. We have defined a data-model and four different implementations of the API, respectively based on gRPC, REST, NETCONF and remote Command Line Interface (CLI). Since it is important to support both the development and testing aspects we have realized an Intent based emulation system to build realistic and reproducible experiments. This collection of tools automate most of the configuration aspects relieving the experimenter from a significant effort. Finally, we have realized an evaluation of some performance aspects of our architecture and of the different variants of the Southbound APIs and we have analyzed the effects of the configuration updates in the SRv6 enabled nodes

Service Function Chaining to Support Ultra-Low Latency Communication in NFV

Network function virtualization (NFV) has the potential to fundamentally transform conventional network architecture through the decoupling of software from dedicated hardware. The convergence of virtualization and cloud computing technologies has revolutionized the networking landscape, offering a wide range of advantages, including improved flexibility, manageability, and scalability. The importance of network capability in enabling ultra-low latency applications has been greatly amplified in the current era due to the increased demand for emerging services such as autonomous driving, teleoperated driving, virtual reality, and remote surgery. This paper presents a novel and efficient methodology for service function chaining (SFC) in an NFV-enabled network that aims to minimize latency and optimize the utilization of physical network resources, with a specific focus on ultra-low latency applications. In our proposed methodology, we offer flow prioritization and an adjustable priority coefficient factor (µ) to reserve a portion of physical network resources exclusively for ultra-low latency applications in order to optimize the deployment paths of these applications further. We formulate the SFC deployment problem as an integer linear programming (ILP) optimization model. Furthermore, we propose a set of heuristic algorithms that yield near-optimal solutions with minimal optimality gaps and execution times, making them practical for large-scale network topologies. Performance evaluations demonstrate the effectiveness of our proposed methodology in enabling ultra-low latency applications in an NFV-enabled network. Compared to existing algorithms, our proposed methodology achieves notable enhancements in terms of the end-to-end delay (up to 22 percent), bandwidth utilization (up to 28 percent), and SFC acceptance rate (up to 13 percent)

Joint QoS and congestion control based on traffic prediction in SDN

Due to the various network requirements of applications, quality of service (QoS)-aware routing plays an important role in the networks. Recently proposed resource allocation algorithms focus on the current traffic matrix, which is not applicable for dynamic networks. In this paper, we exploit an estimation of flow matrix that gives our scheme the ability to sufficiently reduce the total packet loss and simultaneously raise the network throughput. In this way, we mathematically formulate the QoS-aware resource reallocation in software-defined networking (SDN) networks based on the traffic prediction. To solve this optimization problem, two schemes are proposed: (i) exact solution; and (ii) fast suboptimal one. The proposed schemes are compared with the accuracy perspective. Moreover, the impact of prediction on resource reallocation is discussed. In this regard, it is shown that, compared with the conventional scheme, the proposed scheme decreases the packet loss and increases the throughput significantly

CECT: Computationally Efficient Congestion-avoidance and Traffic Engineering in Software-defined Cloud Data Centers

The proliferation of cloud data center applications and network functionvirtualization (NFV) boosts dynamic and QoS dependent traffic into the datacenters network. Currently, lots of network routing protocols are requirementagnostic, while other QoS-aware protocols are computationally complex andinefficient for small flows. In this paper, a computationally efficientcongestion avoidance scheme, called CECT, for software-defined cloud datacenters is proposed. The proposed algorithm, CECT, not only minimizes networkcongestion but also reallocates the resources based on the flow requirements.To this end, we use a routing architecture to reconfigure the network resourcestriggered by two events: 1) the elapsing of a predefined time interval, or, 2)the occurrence of congestion. Moreover, a forwarding table entries compressiontechnique is used to reduce the computational complexity of CECT. In this way,we mathematically formulate an optimization problem and define a geneticalgorithm to solve the proposed optimization problem. We test the proposedalgorithm on real-world network traffic. Our results show that CECT iscomputationally fast and the solution is feasible in all cases. In order toevaluate our algorithm in term of throughput, CECT is compared with ECMP (wherethe shortest path algorithm is used as the cost function). Simulation resultsconfirm that the throughput obtained by running CECT is improved up to 3xcompared to ECMP while packet loss is decreased up to 2x