Elastic Optical Networks Research Articles

On the minimisation of resource utilisation for cost reduction in space division multiplexing based elastic optical networks

On the minimisation of resource utilisation for cost reduction in space division multiplexing based elastic optical networks

Auxiliary Graph-Based Energy-Efficient Routing Resource Allocation Algorithm in Space Division Multiplexing Elastic Optical Networks

Auxiliary Graph-Based Energy-Efficient Routing Resource Allocation Algorithm in Space Division Multiplexing Elastic Optical Networks

Impairment- and Fragmentation Aware Dynamic Routing, Modulation and Spectrum Allocation in C+L Band Elastic Optical Networks Using Q-Learning

Impairment- and Fragmentation Aware Dynamic Routing, Modulation and Spectrum Allocation in C+L Band Elastic Optical Networks Using Q-Learning

Why Optical Packet Switching failed and can Elastic Optical Networks take its place?

In this special issue devoted to the memory of Prof. Fabio Neri we would like to look back at the time of the international research projects where some of us collaborated with him. On the basis of our personal experience of the time and the current viewpoint, we will discuss why Optical Packet Switching (OPS) is a technology that never came to market in spite of the great amount of research that was devoted to it. Then we will explore how Elastic Optical Network came to the stage more recently, somewhat overcoming the OPS technical proposal both in the interest of the researchers as well as of the industry.

Machine learning-assisted nonlinear-impairment-aware proactive defragmentation forC+Lband elastic optical networks

Efficient resource allocation and management can enhance the capacity of an optical backbone network. In this context, spectrum retuning via hitless defragmentation has been presented for elastic optical networks to enhance efficient spectrum accommodation while reducing the unused fragmented spaces in the spectrum. However, the quality of service committed in a service level agreement may be affected due to spectrum retuning. In particular, for transmission beyond the conventional C band, the presence of inter-channel stimulated Raman scattering can severely degrade the quality of the signal during defragmentation. To conquer this problem, this paper proposes, for the first time to our knowledge, a signal-quality-aware proactive defragmentation scheme for theC+Lband system. The proposed scheme prioritizes the minimization of the fragmentation index and quality of transmission (QoT) maintenance for two different defragmentation algorithms, namely, nonlinear-impairment (NLI)-aware defragmentation (NAD) and NLI-unaware defragmentation (NUD). We leverage machine learning techniques for QoT estimation of ongoing lightpaths during spectrum retuning. The optical signal-to-noise ratio of a lightpath is predicted for each choice of spectrum retuning, which helps to monitor the effect of defragmentation on the quality of ongoing lightpaths (in terms of assigned modulation format). Numerical results show that, compared to a baseline algorithm (NUD), the proposed NAD algorithm provides up to 15% capacity increment for smaller networks such as BT-UK, while for larger networks such as the 24-node USA network, a capacity benefit of 23% is achieved in terms of the number of served demands at 1% blocking.

Dynamic routing, modulation, and spectrum assignment based on fuzzy logic control in elastic optical networks.

Considering the constantly changing network resources and randomly generated spectrum fragmentation problem, a dynamic routing, modulation, and spectrum assignment based on the fuzzy logic control (RMSA-FLC) algorithm is proposed in this paper. A fuzzy logic control (FLC) system based on the degree of fragmentation optimization and the degree of link selection is constructed. The path-control weight (PW) is achieved as the output of the FLC system, and the path and spectrum allocation scheme with the maximum PW is selected for the immediate reservation requests. The simulation results show that the proposed RMSA-FLC algorithm can effectively reduce the blocking probability and improve the spectrum resource utilization in a dynamic elastic optical network.

Dynamic and highly energy-efficient virtual network embedding method based on elastic optical networks

Network virtualization has been considered one of the promising paradigms for future network architecture, which can satisfy the diversified and personalized requirements of different applications by sharing physical network infrastructure. One of the major challenges in virtualization is how to map virtual networks efficiently onto the substrate network considering different constraints. We aim at solving the problem of high energy consumption of virtual network embedding in respect of the orthogonal frequency division multiplexing using elastic optical networks (EONs). First, we comprehensively consider and design an energy consumption model of network components, which denotes the key part of virtual optical network embedding (VONE). Then, a dynamic heuristic energy-efficient virtual optical network embedding (DEE-VONE) algorithm is proposed to achieve high energy efficiency and resource scalability. Simulation experiments have proven that, compared with the two benchmark algorithms, namely, the FFK-VONE and LRCK-VONE, the proposed method can maximize the network energy consumption saving at the cost of increasing the blocking probability. As the traffic load increases, the DEE-VONE method achieves higher energy-saving efficiency because optical switching energy saving plays an essential role.

Balanced resource allocation for VNF service chain provisioning in inter-datacenter elastic optical networks

Network Function Virtualization (NFV) is a novel technology that enables flexible and cost-effective network services by replacing the hardware-based middleboxes with software instances named virtual network functions (VNFs). In NFV, each requested Service Function Chain (SFC) is an ordered sequence of VNFs that must be placed optimally on the geographically distributed data centers (DCs). For the popular Elastic Optical Networks (EONs) with flexible bandwidth management, a major challenge is optimal placement of the VNF-based Service Chain (VNF-SC) on data centers and steering traffic through deployed VNFs in the substrate network. In this paper, we study both the problems of VNF placement and Routing/Spectrum Allocation (RSA) for inter-DC EONs with the aim of minimizing the total network cost while considering the balanced resource utilization to reduce the blocking probability. We first formulate the problem as an Integer Linear Programming (ILP) to solve the problem exactly, and then propose a heuristic algorithm based on the resource-balancing and fragmentation-aware solution to reduce the blocking probability. The simulation results verify that the proposed algorithm outperforms an existing algorithm.

Efficiency and fairness improvement for elastic optical networks using reinforcement learning-based traffic prediction

The characteristics of traffic in core networks have changed in recent years, with the data flows among network nodes now showing large volumes but also large fluctuations during the day. In such a dynamic environment, the static allocation of network resources by making reservations at the peak rates is wasteful, both in terms of energy consumption and resource efficiency. Periodic reconfiguration of network resources is necessary for the full exploitation of an elastic optical network’s potential. In this paper, we present a resource reconfiguration scheme for an IP over elastic optical core network. The cornerstone of the proposed scheme is a novel traffic prediction mechanism based on reinforcement learning. Using the forecasting mechanism, a heuristic routing and spectrum assignment algorithm was designed to efficiently and fairly allocate network resources periodically. Simulation results show both a reduction of the bandwidth blocking probability and an increase of the fairness index regarding the network resources allocation over corresponding algorithms in the literature.

Virtual optical network embedding of time-varying traffic in elastic optical networks

Network virtualization can enhance the flexibility of Internet infrastructure resources and the scalability of the substrate elastic optical networks (EONs) through the coexistence of multiple virtual optical networks supporting different services. Traditional virtual optical network embedding mostly assumed fixed embedding, that is, once a virtual optical network (VON) is embedded, the network resources allocated to it are almost unchanged during its lifetime. However, the actual traffic demand on a VON usually changes over time. To address the problems of high bandwidth blocking probability and low spectrum utilization caused by the mismatch between the resources allocated and the required resources during the virtual optical network embedding of the time-varying traffic in the elastic optical networks (EONs), we propose a virtual optical network embedding algorithm of the time-varying traffic (TVT-VONE). Firstly, in the node embedding stage, a node weight value measurement is designed based on the attributes of virtual nodes and physical nodes. Secondly, in the virtual optical link embedding stage, a measurement formula for path spectrum resource richness is designed to decrease link embedding failure due to insufficient spectrum resources on the path. Finally, in order to adapt to the characteristics of dynamic changes in bandwidth requirements of time-varying traffic, we design a resource-aware virtual network reconfiguration strategy when the spectrum resources of traffic required do not match the resources allocated to the changed traffic. Simulation results show that the proposed algorithm can effectively reduce the bandwidth blocking probability of VON requests and improve spectrum utilization of EONs.

Physical topology optimization for highly reliable and efficient wavelength-assignable optical networks

Optical networks, such as wavelength-division multiplexing networks and elastic optical networks, require high reliability and efficient wavelength allocation. We propose a new physical topology optimization method that achieves both high reliability and efficient wavelength allocation. We compare the results of conventional algebraic connectivity optimization, which discusses physical topology optimization from the viewpoint of spectral graph theory, with those based on reliability optimization, which is important from the viewpoint of network operations. From the comparison analysis, it is shown that our proposed two-step degree bounded reliability-optimization model, which optimizes reliability with appropriate node degree constraints, leads to a physical topology that has high wavelength capacity without compromising reliability.

Latency-aware service provisioning in survivable multilayer IP-over-elastic optical networks to support multi-class of service transmission

To meet the requirements of the emerging applications, future networks must transmit multiple class of services traffic with diverse quality of service (QoS) requirements. In this paper, we formulate an integer linear programming (ILP) to design multilayer internet protocol (IP) over elastic optical networks (EONs), IP-over-EONs to support multi-class services with different latency and availability requirements. The objective of the ILP is to minimize capital expenditure (CAPEX) and spectrum usage while satisfying all QoS constraints in terms of latency and survivability mechanism. Furthermore, a heuristic algorithm is proposed to solve this problem in large-scale networks. We compare the results of the ILP formulation and the proposed heuristic algorithm in a 5-node network, where our results reveal that the performance gaps between the ILP and heuristic algorithm are on average 2.4% and 9.8% in terms of CAPEX and occupied spectrum, respectively, while the heuristic algorithm has a lower run time. Moreover, we quantitatively analyze the effects of end-to-end latency on the CAPEX of multilayer IP-over-EONs. Our results reveal that serving latency-sensitive demands increases the CAPEX of the network. This is because all-optical connections (i.e., single-hop IP connections) must be established to meet the delay constraints, which in turn leads to a higher CAPEX and occupied spectrum. Furthermore, we compare the effects of using different types of transponders in three practical topologies and evaluate the impacts of the length of the topology links on CAPEX and spectrum usage. Finally, we propose the best transponder types that could be used for each topology based on the gathered results.

Circuits/cutsets duality and theoretical foundation of a structural approach to survivable logical topology mapping in IP-over-WDM optical networks

The survivable logical topology mapping (SLTM) problem in IP-over-WDM networks is to map each link in the logical topology (IP layer) onto a lightpath in the physical topology (optical layer) such that a failure of a physical link does not cause the logical topology to become disconnected. This problem is known to be NP-complete. For this SLTM problem, two lines of investigations have been reported in the literature: the mathematical programming approach [1] and the structural approach introduced by Kurant and Thiran in [2] and pursued by Thulasiraman et al. [3,4,5]. In this paper we present an integrated treatment of the theoretical foundation of the survivable topology mapping problem presented in [3,4,5]. We believe that the algorithmic strategy developed in this paper will serve as an important phase in any strategy in the emerging area of resilient slicing of elastic optical networks. We conclude with a comparative evaluation, based on simulations, of the different algorithmic strategies developed in the paper, and also pointing to applications beyond IP-over-WDM optical networks, in particular, survivable design of inter-dependent multi-layer cyber physical systems such as smart power grids.

Investigation of modulation format conversion for optical channel deaggregation in elastic optical network

Our research investigates an optical channel deaggregation technique based on modulation format conversion that may be used in elastic optical networks (EONs) to achieve high spectral efficiency, flexibility, and high data rate. The modulation format conversion technique from 8-ary phase shift keying (8-PSK) to two simultaneous quadrature phase shift keying (QPSK) at 50 Gb / s has been simulated and investigated. 8-PSK and the converted QPSK signals are investigated and compared in terms of bit error rate (BER) and quality factor (Q-factor), and it is demonstrated that they substantially reduce the complexity of transmission and reception process, at high data rates in EON. Additionally, this approach is further exploited to tailor a protection scheme for network survivability. Channel deaggregation, which divides the channel into working and protection paths, is used to implement a dedicated path protection scheme in EON. To evaluate the effect of various network conditions such as traffic load, modulation format, and BER on EON survivability, network simulations are performed on the ARPANET network.

Nonblocking conditions for a multicast WSW architecture based on subtree scheme for elastic optical networks

Elastic optical networks (EONs) are a promising solution for future high-speed optical communication, and multicasting in EONs can efficiently support many emerging services. Different schemes, such as path, tree and subtree schemes, serve multicast services. In this paper, we consider a three-stage wavelength-space-wavelength (WSW) node architecture, which adopts wavelength switches in the first and last stages and space switches in the middle stage, and uses the path scheme to accommodate multicast requests, as proposed in an earlier work for elastic optical networks. We also enhance the WSW architecture to serve multicast requests in a more spectrum-efficient way, namely, using the subtree scheme, by making each switch support multicast capacity, and we term the resulting architecture M-WSW. To the best of our knowledge, this is the first study of the WSW architecture using the subtree scheme to support multicast capacity. We prove the sufficient and necessary conditions, in terms of the number of middle switches, of the M-WSW architecture for being strictly nonblocking (SNB) and wide-sense nonblocking (WSNB) under the two routing algorithms proposed in this paper. Our results show that the number of middle switches required for the architecture to be WSNB under each of the two proposed routing algorithms is much less than the number of middle switches required for SNB, especially when the SNB results meet the boundary condition.

Multi-Agent and Cooperative Deep Reinforcement Learning for Scalable Network Automation in Multi-Domain SD-EONs

The service provisioning in multi-domain software-defined elastic optical networks (SD-EONs) is an interesting but difficult problem to tackle, because the basic problem of lightpath provisioning, i.e., the routing and spectrum assignment (RSA), is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {NP}$ </tex-math></inline-formula> -hard, and each domain is owned and operated by a different carrier. Therefore, even though numerous RSA heuristics have been proposed, there does not exist a universal winner that can always achieve the lowest blocking probability in all the scenarios of a multi-domain SD-EON. This motivates us to revisit the inter-domain provisioning problem in this paper by leveraging deep reinforcement learning (DRL). Specifically, we propose DeepCoop, which is an inter-domain service framework that uses multiple cooperative DRL agents to achieve scalable network automation in a multi-domain SD-EON. DeepCoop employs a DRL agent in each domain to optimize intra-domain service provisioning, while a domain-level path computation element (PCE) is introduced to obtain the sequence of the domains to go through for each lightpath request. By sharing a restricted amount of information among each other, the DRL agents can make their decisions distributedly. To ensure scalability and universality, we design the action space of each DRL agent based on well-known RSA heuristics, and architect the agents based on the soft actor-critic (SAC) scenario. We run extensive simulations to evaluate DeepCoop, and the results show that DeepCoop can adapt to the dynamic environment in a multi-domain SD-EON to always select the best RSA heuristic for minimizing blocking probability, and it outperforms the existing algorithms on inter-domain provisioning in various scenarios. Moreover, we verify that the distributed training implemented in DeepCoop ensures its universality and scalability (i.e., its training and operation do not depend on the topology of the SD-EON).

A Research on Routing and Spectrum Allocation Algorithm for Elastic Optical Networks Based on Deep Learning

Dynamic allocation request and spectrum release will lead to spectrum fragmentation, which will affect the allocation of subsequent services and spectrum resource utilization of elastic optical network. This paper proposes a new routing and spectrum allocation algorithm based on deep learning, which will find the best routing and spectrum allocation method for a specific network, so as to improve the overall network performance. Simulation results show that compared with the traditional resource allocation strategy, the neural network model used in this paper can improve the degree of spectrum fragmentation and reduce the network blocking probability.

Demonstration of latency-aware 5G network slicing on optical metro networks

The H2020 METRO-HAUL European project has architected a latency-aware, cost-effective, agile, and programmable optical metro network. This includes the design of semidisaggregated metro nodes with compute and storage capabilities, which interface effectively with both 5G access and multi-Tbit/s elastic optical networks in the core. In this paper, we report the automated deployment of 5G services, in particular, a public safety video surveillance use case employing low-latency object detection and tracking using on-camera and on-the-edge analytics. The demonstration features flexible deployment of network slice instances, implemented in terms of European Telecommunications Standards Institute (ETSI) network function virtualization network services. We summarize the key findings in a detailed analysis of end-to-end quality of service, service setup time, and soft-failure detection time. The results show that the round-trip time over an 80 km link is under 800 µs and the service deployment time is under 180 s.

Optical networks management and control: A review and recent challenges

In the last twenty years, optical networks have witnessed recurrent changes in their management and control architecture. In this paper, we present a historical timeline and a future perspective of the evolution of optical network management and control deployed for Wavelength Switched Optical Networks (WSON), Elastic Optical Networks (EON) and (multilayer) Data Center Networks.Early implementations of WSON envisaged a static and centralized provisioning approach supported by the Management Plane only. Gradually, the requirement of accommodating more network dynamicity in WSON, and later in EON, pushed the adoption of a distributed control, mostly supported by vendor-dependent implementations of the Generalized MultiProtocol Label Switching (GMPLS) protocol suite. The drawbacks of the fully distributed GMPLS-based control, such as resource contention, suboptimal resource usage, and complex computations (e.g., to account for physical layer constraints) showed the necessity to bring back some of the routing/provisioning functions to a centralized Path Computation Element (PCE) capable of accounting for e.g. physical impairments and interworking with GMPLS.The centralized control then gained its momentum and brought a radical change in network control, through the separation of data and control plane introduced by the paradigm of Software Defined Networking (SDN). Such an approach has been gradually extended to optical network control.The paper, eventually, presents the most advanced control techniques, namely the intent-based networking, the observe/decide/act state-based approach providing for autonomic optical network and the (closed-loop) zero-touch service management approach. Advanced traffic conditioning techniques are also detailed, namely the in-band telemetry and the exploitation of Programming Protocol-Independent Packet Processors (P4) language capabilities as well as solutions tailored for data center networks: all of them are still in a research stage and to be integrated within future optical network architectures.

Cognitive Control-Loop for Elastic Optical Networks with Space-Division Multiplexing

This paper introduces a new solution to improve network performance by decreasing spectrum fragmentation, crosstalk interference, blocking of virtual networks, cost, and link load imbalance. These problems degrade the performance of Elastic Optical Networks with Space-Division Multiplexing. The proposed solution, called Cognitive control loop (CO-OP), is capable of identifying a set of problems and creating plans to mitigate these problems. The CO-OP comprises four functions that employ learning algorithms to identify problems and plan a series of actions to reduce or eliminate them. The results show that the CO-OP can effectively decrease up to 30% the blocking of requests and up to 50% the crosstalk occurrence compared to existing algorithms.