Optimizing segment routing using evolutionary computation

Segment Routing (SR) combines the simplicity of Link-State routing protocols with the flexibility of Multiprotocol Label Switching (MPLS). By decomposing forwarding paths into segments, identified by labels, SR improves Traffic Engineering (TE) and enables new solutions for the optimization of network resources utilization. This work proposes an Evolutionary Computation approach that enables Path Computation Element (PCE) or Software-defined Network (SDN) controllers to optimize label switching paths for congestion avoidance while using at the most three labels to configure each label switching path.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT Fundac¸˜ao para a Ciˆencia e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Segment Routing: a Comprehensive Survey of Research Activities, Standardization Efforts and Implementation Results

Fixed and mobile telecom operators, enterprise network operators and cloud providers strive to face the challenging demands coming from the evolution of IP networks (e.g. huge bandwidth requirements, integration of billions of devices and millions of services in the cloud). Proposed in the early 2010s, Segment Routing (SR) architecture helps face these challenging demands, and it is currently being adopted and deployed. SR architecture is based on the concept of source routing and has interesting scalability properties, as it dramatically reduces the amount of state information to be configured in the core nodes to support complex services. SR architecture was first implemented with the MPLS dataplane and then, quite recently, with the IPv6 dataplane (SRv6). IPv6 SR architecture (SRv6) has been extended from the simple steering of packets across nodes to a general network programming approach, making it very suitable for use cases such as Service Function Chaining and Network Function Virtualization. In this paper we present a tutorial and a comprehensive survey on SR technology, analyzing standardization efforts, patents, research activities and implementation results. We start with an introduction on the motivations for Segment Routing and an overview of its evolution and standardization. Then, we provide a tutorial on Segment Routing technology, with a focus on the novel SRv6 solution. We discuss the standardization efforts and the patents providing details on the most important documents and mentioning other ongoing activities. We then thoroughly analyze research activities according to a taxonomy. We have identified 8 main categories during our analysis of the current state of play: Monitoring, Traffic Engineering, Failure Recovery, Centrally Controlled Architectures, Path Encoding, Network Programming, Performance Evaluation and Miscellaneous...Comment: SUBMITTED TO IEEE COMMUNICATIONS SURVEYS & TUTORIAL

Survey of Consistent Network Updates

Computer networks have become a critical infrastructure. Designing dependable computer networks however is challenging, as such networks should not only meet strict requirements in terms of correctness, availability, and performance, but they should also be flexible enough to support fast updates, e.g., due to a change in the security policy, an increasing traffic demand, or a failure. The advent of Software-Defined Networks (SDNs) promises to provide such flexiblities, allowing to update networks in a fine-grained manner, also enabling a more online traffic engineering. In this paper, we present a structured survey of mechanisms and protocols to update computer networks in a fast and consistent manner. In particular, we identify and discuss the different desirable update consistency properties a network should provide, the algorithmic techniques which are needed to meet these consistency properties, their implications on the speed and costs at which updates can be performed. We also discuss the relationship of consistent network update problems to classic algorithmic optimization problems. While our survey is mainly motivated by the advent of Software-Defined Networks (SDNs), the fundamental underlying problems are not new, and we also provide a historical perspective of the subject

Energy management in communication networks: a journey through modelling and optimization glasses

The widespread proliferation of Internet and wireless applications has produced a significant increase of ICT energy footprint. As a response, in the last five years, significant efforts have been undertaken to include energy-awareness into network management. Several green networking frameworks have been proposed by carefully managing the network routing and the power state of network devices. Even though approaches proposed differ based on network technologies and sleep modes of nodes and interfaces, they all aim at tailoring the active network resources to the varying traffic needs in order to minimize energy consumption. From a modeling point of view, this has several commonalities with classical network design and routing problems, even if with different objectives and in a dynamic context. With most researchers focused on addressing the complex and crucial technological aspects of green networking schemes, there has been so far little attention on understanding the modeling similarities and differences of proposed solutions. This paper fills the gap surveying the literature with optimization modeling glasses, following a tutorial approach that guides through the different components of the models with a unified symbolism. A detailed classification of the previous work based on the modeling issues included is also proposed

Computing Delay-Constrained Least-Cost Paths for Segment Routing is Easier Than You Think

With the growth of demands for quasi-instantaneous communication services such as real-time video streaming, cloud gaming, and industry 4.0 applications, multi-constraint Traffic Engineering (TE) becomes increasingly important. While legacy TE management planes have proven laborious to deploy, Segment Routing (SR) drastically eases the deployment of TE paths and thus became the most appropriate technology for many operators. The flexibility of SR sparked demands in ways to compute more elaborate paths. In particular, there exists a clear need in computing and deploying Delay-Constrained Least-Cost paths (DCLC) for real-time applications requiring both low delay and high bandwidth routes. However, most current DCLC solutions are heuristics not specifically tailored for SR. In this work, we leverage both inherent limitations in the accuracy of delay measurements and an operational constraint added by SR. We include these characteristics in the design of BEST2COP, an exact but efficient ECMP-aware algorithm that natively solves DCLC in SR domains. Through an extensive performance evaluation, we first show that BEST2COP scales well even in large random networks. In real networks having up to thousands of destinations, our algorithm returns all DCLC solutions encoded as SR paths in way less than a second

Safe Update of Hybrid SDN Networks

The support for safe network updates, i.e., live modification of device behavior without service disruption, is a critical primitive for current and future networks. Several techniques have been proposed by previous works to implement such a primitive. Unfortunately, existing techniques are not generally applicable to any network architecture, and typically require high overhead (e.g., additional memory) to guarantee strong consistency (i.e., traversal of either initial or final paths, but never a mix of them) during the update. In this paper, we deeply study the problem of computing operational sequences to safely and quickly update arbitrary networks. We characterize cases, for which this computation is easy, and revisit previous algorithmic contributions in the new light of our theoretical findings. We also propose and thoroughly evaluate a generic sequence-computation approach, based on two new algorithms that we combine to overcome limitations of prior proposals. Our approach always finds an operational sequence that provably guarantees strong consistency throughout the update, with very limited overhead. Moreover, it can be applied to update networks running any combination of centralized and distributed control-planes, including different families of IGPs, OpenFlow or other SDN protocols, and hybrid SDN networks. Our approach therefore supports a large set of use cases, ranging from traffic engineering in IGP-only or SDN-only networks to incremental SDN roll-out and advanced requirements (e.g., per-flow path selection or dynamic network function virtualization) in partial SDN deployments

MAGNNETO: A graph neural network-based multi-agent system for traffic engineering

Current trends in networking propose the use of Machine Learning (ML) for a wide variety of network optimization tasks. As such, many efforts have been made to produce ML-based solutions for Traffic Engineering (TE), which is a fundamental problem in Internet Service Provider (ISP) networks. Nowadays, state-of-the-art TE optimizers rely on traditional optimization techniques, such as Local search, Constraint Programming, or Linear programming. In this paper, we present MAGNNETO, a distributed ML-based framework that leverages Multi-Agent Reinforcement Learning and Graph Neural Networks for distributed TE optimization. MAGNNETO deploys a set of agents across the network that learn and communicate in a distributed fashion via message exchanges between neighboring agents. Particularly, we apply this framework to optimize link weights in Open Shortest Path First (OSPF), with the goal of minimizing network congestion. In our evaluation, we compare MAGNNETO against several state-of-the-art TE optimizers in more than 75 topologies (up to 153 nodes and 354 links), including realistic traffic loads. Our experimental results show that, thanks to its distributed nature, MAGNNETO achieves comparable performance to state-of-the-art TE optimizers with significantly lower execution times. Moreover, our ML-based solution demonstrates a strong generalization capability to successfully operate in new networks unseen during training.This publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GBC21), funded by MCIN/AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA), the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia, and the European Social Fund.Peer ReviewedPostprint (author's final draft