Machine learning-based routing and wavelength assignment in software-defined optical networks

Recently, machine learning (ML) has attracted the attention of both researchers and practitioners to address several issues in the optical networking field. This trend has been mainly driven by the huge amount of available data (i.e., signal quality indicators, network alarms, etc.) and to the large number of optimization parameters which feature current optical networks (such as, modulation format, lightpath routes, transport wavelength, etc.). In this paper, we leverage the techniques from the ML discipline to efficiently accomplish the routing and wavelength assignment (RWA) for an input traffic matrix in an optical WDM network. Numerical results show that near-optimal RWA can be obtained with our approach, while reducing computational time up to 93% in comparison to a traditional optimization approach based on integer linear programming. Moreover, to further demonstrate the effectiveness of our approach, we deployed the ML classifier into an ONOS-based software defined optical network laboratory testbed, where we evaluate the performance of the overall RWA process in terms of computational time.The authors would like to acknowl-edge the support of the project TEXEO (TEC2016-80339-R), funded by Spanish MINECO and the EU-H2020 Metrohaul project (grant no. 761727)

Reinforcement Learning for Service Function Chain Reconfiguration in NFV-SDN Metro-Core Optical Networks

With the advent of 5G technology, we are witnessing the development of increasingly bandwidth-hungry network applications, such as enhanced mobile broadband, massive machine-type communications and ultra-reliable low-latency communications. Software Defined Networking (SDN), Network Function Virtualization (NFV) and Network Slicing (NS) are gaining momentum not only in research but also in IT industry representing the drivers of 5G. NS is an approach to network operations allowing the partition of a physical topology into multiple independent virtual networks, called network slices (or slices). Within a single slice, a set of Service Function Chains (SFCs) is defined and the network resources, e.g. bandwidth, can be provisioned dynamically on demand according to specific Quality of Service (QoS) and Service Level Agreement (SLA) requirements. Traditional schemes for network resources provisioning based on static policies may lead to poor resource utilization and suffer from scalability issues. In this article, we investigate the application of Reinforcement Learning (RL) for performing dynamic SFC resources allocation in NFV-SDN enabled metro-core optical networks. RL allows to build a self-learning system able to solve highly complex problems by employing RL agents to learn policies from an evolving network environment. In particular, we build an RL system able to optimize the resources allocation of SFCs in a multi-layer network (packet over flexi-grid optical layer). The RL agent decides if and when to reconfigure the SFCs, given state of the network and historical traffic traces. Numerical simulations show significant advantages of our RL-based optimization over rule-based optimization design

Multipath optical routing with compact fiber delay line-based differential delay compensation

Multipath (MP) routing is an effective technique for applications imposing stringent requirements on bandwidth, delay and availability. However, the benefits of MP routing can be impaired by the differential delay (DD) i.e., delay difference between paths of the MP connection. In presence of DD the destination of a MP connection receives a disordered version of the original packet sequence. Thus, a DD compensation (DDC) technique is needed to recover the original sequence. DDC is normally performed at destination (centralized-DDC) using high speed reconstruction buffers. For MP connections with large DD the centralized-DDC creates a bottleneck that limits the performance gain of MP routing. DDC can be distributed along the paths (distributed electronic-DDC) to reduce the reconstruction buffer requirements and minimize DD at destination. In optical networks, distributed electronic-DDC incurs in extra costly and power hungry electro/optical (E/O) conversions, that are otherwise avoided by routing all optical circuits (i.e., lightpaths). To avoid extra E/O conversions, distributed electronic-DDC can be jointly placed with optical regeneration. Nonetheless, such approach greatly reduces the candidate nodes to distribute the DDC, because optical regeneration is only needed for very long lightpaths. This work proposes, for the first time, the use of compact fiber delay lines (FDL)s to perform distributed all optical DDC (transparent-DDC). The FDLs are passive elements that overcome the problems of previous solutions: they are not restricted to optical regeneration points, and do not incur into extra E/O conversions. An integer linear programming formulation is presented for the MP routing with DD-minimization problem that combines electronic-DDC co-located with 3R (Reamplifying, Reshaping and Retiming) regeneration points to the novel transparent-DDC based on FDLs. Numerical results show the advantages of combining transparent and electronic-DDC in realistic network scenarios

DSP power consumption and capacity projections for WDM-OFDMA-PON

Wavelength Division Multiplexing- Orthogonal Frequency Division Multiple Access-based Passive Optical Network (WDM-OFDMA-PON) is accepted as one of the most suitable architectures to tackle the requirements for Next Generation Optical Access Networks (NG-OAN). Digital Signal Processing (DSP) is the key technology for the development of this architecture. However, current DSP devices’ processing capacity is at least one order of magnitude bellow the required for the implementation of WDM-OFDMA-PON. Additionally, due to the access network’s massiveness, the DSP’s energy consumption has become a major concern. In order to describe how long it will take for the DSP’s devices to cope the requirements for WDM-OFDMA-PON, and how much energy they will consume, in this paper the projections of DSP’s capacity and energy consumption were developed, considering different rates of annual improvement of the technology. Based on our projections, we have demonstrated that the required capacity will be achieved within two to five years from now, and such device will consume approximately twice the amount of power of current processors if the annual rates of improvement of capacity and energy efficiency are maintained

Machine-Learning-Based Prediction and Optimization of Mobile Metro-Core Networks

We propose a methodology to optimize the decisions of mobile metro-core network orchestration systems. We use machine-learning-based traffic prediction to dynamically provision resources in advance. Resource allocation and reconfigurations are calculated through a heuristic that combines reinforcement learning and mixed integer linear programming

Dynamic programming of network slices in software-defined metro-core optical networks

Nowadays networks are the basis of our communication providing a great number of services. As a consequence, the traffic is increasing and there is a growing demand for new services that require stringent constraints on capacity, latency and jitter to provide an appropriate Quality of Service (QoS) to end users. In order to cope with these requirements, network infrastructure needs to evolve from a static and closed architecture towards a more scalable, dynamic and agile one. Software-Defined Networking and Network Function Virtualization allow to provide different services, each one with its own QoS constraints, independent and secure, thanks to the network slicing concept, the main subject of this work. Network slicing allows to segment the underlying physical network into different logical networks to provide data transport customized to specific services. In this paper, we propose two mathematical models able to dynamically provision network slices on the physical network, complying with their QoS requirements for their instantiation and routing of traffic. The proposed models aim at minimizing a linear combination of probability of blocking traffic requests, energy consumption of physical network devices and interruption of service due to the reconfiguration of the slices. Taking advantage of traffic signatures from a city's mobile network, the goal is to predict how and when to reconfigure slices already deployed in the network with the aim to optimize the resource allocation in the underlying physical network

Differential delay constrained multipath routing for SDN and optical networks

In multipath routing, maximization of the cardinality K of the disjoint-path set for a given source and destination assuming an upper bound on the differential delay D is one of the key factors enabling its practical applications. In the paper we study such an optimization problem for multipath routing involving maximization of K under the D constraint as the primary objective, and then minimization of the average end-to-end transfer delay for the fixed (maximum) K under the same D constraint. The optimization approach is iterative, based on solving an inner mixed-integer programming subproblem to minimize the delay for a given value of K and D. In order to increase the solution space, we consider the strategy of allowing controlled routing loops. Such a technique is implementable in software defined networks and optical networks. We present numerical results illustrating the gain achieved by using controlled loops in comparison with the traditional loop-free approach

On the Network Slicing for Enterprise Services with Hybrid SDN

Nowadays, companies strongly rely on Virtual Private Networks (VPNs) to deliver services between geographically distributed branch offices. Internet Service Providers (ISPs) must therefore offer a reliable and cost-effective connectivity solution. VPNs are commonly based on static bandwidth allocation over MPLS tunnels, which cause over-provisioning and underutilization of network resources. Software Defined Networking (SDN) appears as a solution to provide agile enterprise networking while reducing operators cost. This paper presents the design and implementation of a Hybrid SDN-based network application to provide dynamic services. Such an architecture enables combining centralized and distributed control with traditional VPN protocols to provision services through network slices. The application performs a flexible policy-based routing, selecting the access technology according to the Quality of Service (QoS) requirements and the network conditions. The simulations executed over an VIRL emulated environment by Cisco show that the proposed network control enables the services to be efficiently provisioned without the need of over-provisioning the resources. Furthermore, a customized network slicing over legacy equipment guarantees the service requirements