Survivable elastic optical networks: survey and perspective (invited)

This study investigates the survivable traffic grooming problem for elastic optical networks with flexible spectrum grid employing new transmission technologies. In such networks, instead of following the traditional fixed ITU-T wavelength grid, optical transponders are capable of properly tuning their rates, and consequently their spectrum occupation, by introducing the fine-granular spectrum unit, called a frequency slot. The number of contiguous frequency slots allocated to an optical path (i.e., lightpath) is adjusted to the current network flow. In this study, we propose a novel shared protection specific to elastic networks, namely, elastic separate-protection-at-connection (ESPAC). It not only provides traditional backup sharing, but also offers a new opportunity of spectrum sharing enabled by the elasticity of the transponders: 1) if the working paths of two connections are link disjoint physically, and 2) if their backup paths traverse two lightpaths which are adjacent on a fiber link, then the two backup lightpaths can share spectrum. The new opportunity of spectrum sharing is realized by using First-Fit to assign working traffic and Last-Fit to assign backup traffic, and allowing spectrum overlap between adjacent backup wavelengths. The elasticity of the transponder enables the expansion and contraction of the lightpaths, thus when a single failure occurs in the network, lightpaths carrying backup flows can be tuned to appropriate rates in such a way that the overlap spectrum is used by only one of the adjacent lightpaths. The results show ESPAC is very spectrum efficient in elastic network setting.

An efficient spectrum defragmentation algorithm is proposed in elastic optical networks by properly rerouting existing connections after traffic terminated dynamically to improve spectrum efficiency, and shown to be resource efficient. Keywords-optical network; network optimization; Spectrum Defragmentation

The rapid escalation of Internet traffic and increasingly heterogeneous service demands impose stringent requirements on optical networks for dynamic resource scheduling, efficient spectrum utilization, and automated operation. Elastic optical networks (EONs) are regarded as a promising solution, yet their performance remains constrained by two critical challenges: routing, modulation format, and spectrum allocation (RMSA) and spectrum defragmentation (SD). Existing approaches predominantly focus on optimizing one of these tasks, which may lead to limited adaptability and suboptimal network efficiency. To address this gap, we propose an asynchronous cooperative multi-agent deep reinforcement learning framework, termed MADRL-JRASD, for the joint optimization of RMSA and proactive SD. The framework incorporates an RMSA agent with dynamic allocation capability and an SD agent with autonomous decision-making ability, coordinated through an asynchronous architecture that enables adaptive responses to environmental changes. Invalid-action masking and carefully designed reward functions are further integrated to enhance training stability and convergence. Comprehensive evaluations over three representative topologies demonstrate that MADRL-JRASD reduces the blocking probability by up to 81% compared with RMSA heuristics without SD and achieves an 85% reduction in overhead relative to heuristic algorithms combining RMSA and SD that attain similar blocking performance. Moreover, the sensitivity analysis shows that the SD agent improves spectrum utilization and that multi-agent cooperation enhances global decision coordination, while action masking and reward design jointly strengthen the convergence and efficiency of MADRL-JRASD.

The immense growth of Internet traffic calls for advanced techniques to enable the dynamic operation of optical networks, efficient use of spectral resources, and automation. In this paper, we investigate the proactive spectrum defragmentation (SD) problem in elastic optical networks and propose a novel deep reinforcement learning-based framework DeepDefrag to increase spectral usage efficiency. Unlike the conventional, often threshold-based heuristic algorithms that address a subset of the defragmentation-related tasks and have limited automation capabilities, DeepDefrag jointly addresses the three main aspects of the SD process: determining when to perform defragmentation, which connections to reconfigure, and which part of the spectrum to reallocate them to. By considering service attributes, the spectrum occupancy state expressed by several different fragmentation metrics, and the reconfiguration cost, DeepDefrag is able to consistently select appropriate reconfiguration actions over the network lifetime and adapt to changing conditions. Extensive simulation results reveal superior performance of the proposed scheme over a scenario with exhaustive defragmentation and a well-known benchmark heuristic from the literature, achieving lower blocking probability at a smaller defragmentation overhead.

Growing concerns regarding the scalability of current optical networks as well as IP-based networks are driving two important trends. One trend is a shift from the current rigid optical networks to spectrally efficient elastic optical networks with a flexible bandwidth and adaptive channel spacing. The other trend is IP traffic offloading to a lower layer yielding benefits that are potentially cost-effective and power-efficient. This article presents a novel multiflow optical transponder (OTP) that enables more efficient IP optical networking. A multiflow OTP allows client data flows that arrive from a single client interface to be mapped to multiple optical flows. In cooperation with the emerging spectrally efficient elastic optical path networking technology, multiflow OTPs can provide multiple optical connections from a single OTP to multiple OTPs. IP traffic offloading to an elastic optical path layer architecture and the effect of introducing multiflow OTPs (i.e., the potential reduction in the number of router interfaces and considerable potential for cost savings) are discussed. These benefits are brought about by increasing the number of directly connected router pairs while keeping router-to-optical-node interconnections simple. A novel optical virtual private line service based on multiflow OTPs that supports multiple optical connections from a single customer site to multiple customer sites with capacity adjustment is also discussed.

Service differentiation-based spectrum defragmentation in mixed grid optical networks

Over the past decade, we have seen a substantial amount of research on elastic optical networks (EONs), showing great promise towards dramatically improving transmission capacity compared to traditional optical networks. The main concern is that the achievable transmission capacity for a given fiber will reach the physical limit soon in the case of single-core fibers. Multi-core fiber (MCF) has been proposed as an innovative alternative which is based on the concept of space-division multiplexing (SDM). We endeavour to work on a Crosstalk Elimination and Spectrum Defragmentation based Routing and Spectrum Allocation (CESD-RSA) algorithm has been proposed which is based on a predefined metric, which measures the spectrum status in the SDM-EONs. Past research has indicated the dependence of crosstalk effect in MCF on the assignment of signals in the context of the spectrum and the core. This specifically entails a study of the Routing, Core and Spectrum allocation in a flexi-grid multicore network. In the end we shall endeavour to evaluate the proposed method using computer simulations.KeywordsWDMSDMEONMCFRSASF

O consumo energético nas redes de núcleo atualmente é impactado pelas aplicações de Bulk Data Transfer (BDT) entre os principais Centros de Dados (CDs) da Internet. Isso ocorre porque essas aplicações consomem a maior parte dos recursos disponíveis e o fazem por longos períodos de tempo. Devido ao aumento do tráfego na rede, que ocorre a cada ano, um dos principais desafios para os provedores de serviço em nuvem é a eficiência energética. Para servir à crescente demanda, o paradigma das Redes Ópticas Elásticas (EON) têm sido proposto com o objetivo de atender requisições com maior flexibilidade, aproveitando melhor o espectro de frequência. Este artigo propõe a solução Energy Efficient Aware-BDT in EON (EEABE), um algoritmo de Roteamento e Alocação de Espectro (RSA) ciente de eficiência energética para realizar BDT inter-CD (ICD), que é capaz de escalonar requisições BDT e implementar a técnica de sleep mode para reduzir o consumo energético dos elementos comutadores na rede óptica.

Spectrum Expansion/Contraction and Survivable Routing and Spectrum Assignment problems on EONs with time-varying traffic

The emergence of Elastic Optical Networks (EON) has brought new conceptions in the operations of optical networks, improving its flexibility and efficiency. The routing and spectrum assignment (RSA) is one of the key problems in EON which deals with resource allocation. In order to reduce the bandwidth blocking ratio on the network, recently, Auxiliary-Graph-Model approaches propose spectrum reservation schemes to reduce the network exhaustion probability. However, these approaches do not take into account the energy expenditure caused by these schemes. In this work, we use an auxiliary graph model to design a new RSA heuristic algorithm to improve the network energy consumption without losses in bandwidth blocking rate. Numerical results show that our proposal may provide a reduction of up to 54% in the bandwidth blocking ratio and achieve a gain of up to 51% in energy efficiency compared to literature.

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.

As the services in elastic optical networks (EONs) are bandwidth-intensive, unpredictable and dynamic, increasing network factors are emerging to affect the performance of network survivable planning and operation from network capacity to efficiency. Most of the traditional protection and restoration approaches may become before long inefficient due to the improvement of a particular network performance metric always at the expense of others. We argue that it would be more beneficial for comprehensive optimization of network performance to consider main network metrics jointly. Moreover, the highly dynamic features of EONs call for the new generation of machine learning-based solutions that are flexible and adaptable to cope with the dynamic nature of services to perform analytics. In this paper, we investigate the problem of global optimization of network performance under survivable EON environment. Specifically, a criterion, named the whole network cost-effectiveness value with survivability (WCES), is defined to measure the overall network performance by balancing the interaction among main network metrics. Then we propose a deep reinforcement learning (DRL) -based heuristic with the objective of improving overall network performance, in which two agents are utilized to provide working and protection schemes converging toward better survivable routing, modulation level and spectrum assignment (S-RMLSA) policies. Numerical results show that the proposed criterion can efficiently measure the overall network performance, and the double-agent DRL-based heuristic can greatly improve WCES while ensuring the network survivability and paying the acceptable extra consumption of request blocking probability.

Elastic optical networking is attracting much attention as a promising solution to achieve spectrum-efficient transport of higher data rates at 100 Gbits/s and beyond. If we draw an analogy to virtualization in cloud computing, it can be seen as network level resource virtualization of optical networks where spectrum resources in optical links are segmented as shareable resources and adaptively aggregated to create a wide variety of optical channels (OChs). In this paper, we discuss the benefits of introducing virtualization into the optical domain from the viewpoints of the network level and the hardware level. In elastic optical networks, a frequency slot through which an OCh is transported and the OCh itself are explicitly decoupled. While the adaptability in the frequency slot is brought about by bandwidth variable wavelength-selective switches, the adaptability in an OCh is yielded by digital coherent technology that is employed in transponders and regenerators. It is emphasized that in order to achieve transponders and regenerators that accommodate heterogeneous traffic demands in an economical manner, simply being adaptive is not enough, and being shareable is essential. We refer to this concept as hardware level virtualization. As examples, we describe a multiflow transponder and an elastic regenerator with results that show proof of concept. Based on the hardware virtualization concept, we propose an elastic optical transport system (EOTS) architecture that enables cost- and energy-efficient IP traffic offloading to the optical domain and improves programmability and automation of optical networks.

To fully utilize the existing optical network resource, the dynamic traffic grooming technology, which could efficiently multiplex many low-speed services arriving dynamically onto high-capacity optical channels, has been studied extensively and used widely. In this article, the problem of dynamic traffic grooming in joint optimization of IP over elastic optical network (EON) is studied. This problem consists of three sub-problems at two layers: the routing problem at the IP-layer (IPR), the routing, modulation level (RML), and the spectrum allocation (SA) problems at the optical layers. We proposed a novel topology-integration and adaptive-modulation based in cross-layer routing spectrum allocation (CL-RSA) algorithm, which follows a multi-cost approach that solves the IPR, the RML, and the SA problems jointly. The most important features of the proposed algorithm are merging physical topology with virtual topology and using the minimum-weight preferred method for route selection. Meanwhile, the effect of physical-layer impairments (PLI) on signal transmission is considered. In addition, the adaptive modulation scheme is used to allocate the spectrum resource. The simulation results show that the algorithm can effectively improve the spectrum resource utilization and reduce the network blocking probability compared to other algorithms in various network conditions, especially in the high load condition effectively.

Bounds for two static optimization problems on routing and spectrum allocation of anycasting