Optical Switching and Networking最新文献

As optical fibre reaches deeper into passive optical network (PON) in fibre-to-the-x (FTTx) networks, maintaining the integrity of these networks is indeed imperative. Essentially, best practices have been established throughout the installation and verification of the fibre access network. Equally significant is monitoring the optical fibre performance degradation in-service to ascertain efficient service delivery, as well as to guarantee service level agreement. Nevertheless, a minor failure in PON may lead to massive loss of data stemming from inherent passivity of network elements in optical distribution network, thus resulting in customer dissatisfaction and escalated operational expenses borne by network operators. Unfortunately, testing and monitoring optical layer have become rather intricate due to the rapid evolution of PON technology. Hence, an efficient optical layer monitoring system equipped with complete monitoring capabilities may substantially enhance the quality of service. Having that said, this study presents a review pertaining to the progress on the performance of available approaches for optical link monitoring system in PON. Recent advances in improving the performance of the optical link monitoring system, such as measurement accuracy, measurement speed, signal-to-noise (SNR) ratio, spatial resolution, and dynamic range, were investigated. Additionally, the performances of the existing approaches based on optical monitoring specifications were compared to identify an ideal monitoring framework. Finally, this paper discusses several challenges and prospects for future endeavours in formulating an effective optical link monitoring system in PON.

Based on software defined optical network and machine learning, the flexible resource management mechanism (ML-FRM) is proposed, which meets the resource requirements of different services in the optical data center networks. The machine learning is integrated to the SDON controller, which accomplishes the resource allocation algorithms according to the classification and clustering results. ML-FRM firstly utilizes unsupervised learning K-means algorithm to cluster traffic flows, and uses supervised learning support vector machine (SVM) algorithm to realize hierarchical classification of channel qualities. Fragmentation-Function-Fit algorithm is proposed to reduce the blocking probability, the results show that it has the lower blocking probability than First-Fit and Exact-First-Fit algorithms. ML-FRM allocates the required resources through different algorithms based on different traffic flow clustering results, and uses different modulation methods for different channel qualities. The analysis results show that ML-FRM has lower blocking probability, acceptable complexity level, and higher spectrum resource utilization efficiency than other algorithms under different offered load level.

Elastic Optical Networks (EONs) allow the channel spacing and the spectral width of an optical signal to be dynamically adjusted and hence have become an important paradigm in managing the heterogeneous bandwidth demands of optical backbone networks. The entire available optical spectrum is divided into some spectrum slots which define the smallest granularity of bandwidth and optical signals with variable bandwidths can occupy different number of such slots. The constraints imposed by the physical layer of an EON require that the slots occupied by an optical signal from source to destination have to be consecutive and contiguous in terms of their relative position in the optical spectrum. Furthermore, the same spectrum slots need to be reserved throughout the entire optical signal's path from its source to destination. The above constraints make the routing and spectrum allocation (RSA) in EONs very challenging because unavailability of enough spectrum slots that together equals the spectral width of the optical signal associated with an end-to-end request, will result in blocking of the request. Recent developments in the physical layer technologies have made all-optical ‘slicing’ of a request possible and make the request to be ‘fit’ into multiple non-consecutive spectral slots in an EON. But these all-optical ‘slicers’ employ complex technologies and can be very costly to employ. In this paper, we propose a spectrum allocation scheme for an EON node architecture with these ‘slicers’ and we also formulate a modified RSA scheme for EONs employing slicers, both as a mixed-integer linear programming (MILP) model and a heuristic algorithm. Our main aim is to analyze the tradeoff between the number of slicers that can be used per node versus the spectrum utilization and bandwidth blocking rate. The numerical results show that the proposed scheme with slicers can significantly improve bandwidth blocking rate, compared to the conventional scheme without slicer.

Elastic Optical Networks (EON) emerge as a viable solution to support the current growing demand for bandwidth. With the application of multi-core fibers (MCF) in EON links, it is possible to increase the availability of spectral resources and explore the spatial dimension. An EON network with MCF enables Space-Division Multiplexing (SDM), allowing the use of more resources in fibers and increasing the capacity of attending circuit requests. However, the use of SDM brings some problems of interference between the circuits in fiber, with greater emphasis on inter-core crosstalk interference. In this survey, some important concepts around EON are presented, along with the characterization of SDM supporting equipment. The impact of crosstalk interference between fiber cores is discussed, with the elements responsible for its occurrence. The Routing, Modulation, Spectrum, and Core Allocation (RMSCA) problem is also characterized, and some solutions currently found in the literature are evaluated. The goal is to show the performance of different allocation techniques, in terms of the circuit blocking ratio. This survey is concluded with an evaluation of the state-of-art, and a presentation of the main challenges found from a systematic review of the related literature.

In this paper, we focus on dynamic lightpath provisioning in translucent spectrally spatially flexible optical networks (SS-FONs) created with a combination of both spectral (EON) and spatial (SDM) flexibility. We utilize the distance-adaptive transmission (DAT) in order to select a modulation for request and routing path in a flexible manner. Moreover, we assume that the signal regeneration is realized with the application of optical transceivers operating in a back-to-back (B2B) configuration. Finally, we propose a novel routing framework for SS-FONs, which involves performing Store-and-Forward (SnF) using Assistive Storage (AS), to reduce peak traffic demand and to better the utilization of network bandwidth. We design different test scenarios, varying in Assistive Storage availability, with and without modulation conversion during the B2B regeneration process and different real-life network topologies. In order to be able to compare the results of test scenarios, we propose a dynamic routing algorithm named adaptive routing with B2B regeneration and Assistive Storage (ARB2BAS) that solves routing, space and spectrum allocation (RSSA) optimization problem. We study how different flexibility aspect affects the network blocking performance, namely the Bandwidth Blocking Probability (BBP) metric. The obtained results show that the proper selection of an algorithm (scenario) has a significant impact on network performance in terms of the blocking probability. Moreover, introducing Assistive Storage enables the network to serve more requests for all of the studied B2B scenarios and network topologies.

Application-centric networking proposes a novel approach to provision connectivity services in transport networks based on application-specific requirements. By exploiting this paradigm, an application can request a service with several requirements to a generic control and management plane, which can leverage this information to differentiate a service on the overall network layers. However, an application-centric provisioning can lead to higher blocking probability when satisfying multiple requirements at the same time, thus impacting negatively both network operators and applications. This paper proposes a framework for the negotiation of connectivity services between a transport network and the applications running on top. In particular, an application can communicate its requirements to it and, in case of resource scarcity (either in term of bandwidth, latency, availability, etc.), the network can offer alternative solutions with a degraded service quality. The effectiveness of the proposed approach is proved by the lower blocking probability experienced by the service requests against a standard approach lacking negotiation capabilities, without in turn causing significant service degradation to the applications. In addition, the paper describes the software architecture behind the negotiation framework and its implementation on top of the ONOS SDN controller.

In this paper, we concentrate on the comparison of different switching policies, as well as their corresponding spatially-spectrally flexible super-channel transmission technologies, in static SDM-EONs. These switching policies are different due to their different spatial switching granularities and/or whether the space lane change (SLC) is supported. According to our simulation results, the switching policy with a finer switching granularity can achieve better network performance in terms of required frequency slices in the networks but meanwhile results in higher device cost. Moreover, we find that the application of SLC can only provide negligible improvements (0.1% ~ 3.1%) on network performance. SLC is encouraged only when the spectrum resource is highly valued by network operators compared to the additional device cost it brings. Moreover, in some previous works, the application of SLC can achieve a 7% ~ 14% improvement on the network throughput in dynamic network scenarios, especially when a finer spatial switching granularity is applied. Consequently, the results in this paper suggest that such an improvement is likely to be achieved by the means of network planning, such as the design of an efficient algorithm considering spectrum defragmentation, because if the re-routing/re-assignment of spectrum is allowed, the dynamic scenario can be treated as a sequence of static scenarios that change with time.

Today's optical transport networks are complex already and the support of the new arising services will further increase such complexity. Traditional deterministic network procedures will need to be revisited, especially their operations. Network Operators will require more dynamic approaches to get the best out of their infrastructure. In this context, cognition and machine learning techniques can provide innovative management solutions for traditional telecom operators. In this paper, we explore a dynamic cognitive approach to improve the adaption of Network Operators' operational processes to the new digital age. We propose a dynamic strategy considering the Case-Base Reasoning (CBR) technique for helping to reduce overall costs by optimizing operation margins. In this way, highly competitive exploitation methods to support new services can be deployed. The proposed dynamic algorithms can achieve higher transmitted power efficiency, up to 20% versus previously proposed static solutions, prolonging the transceivers' lifetime and thus addressing telco operator costs reduction.

Elastic optical network has recently been deemed as a promising infrastructure to support the ever-increasing bandwidth demand of emerging applications, due to its high transmission rate, fine-grained and flexible spectrum allocation. With the explosive growth of traffic, optimizing energy and spectrum efficiency has become a critical issue for green elastic optical networks, which is closely related to the sustainable development of cloud services. This paper focuses on joint optimization of energy and spectrum efficiency for virtual optical network embedding in elastic optical networks. A heuristic algorithm, termed ESE, is presented to improve energy and spectrum efficiency while keeping a high acceptance rate. With consideration of factors influencing energy and spectrum efficiency, a feasible shortest path is preferred; meanwhile, an appropriate modulation format is dynamically selected according to transmission distance and the trade-off between energy and spectrum consumption. To improve the acceptance rate of virtual network requests, a dual mapping is employed to reinforce the embedding process by multi-dimensional resources integrated mapping. The simulation results show that the proposed algorithm can achieve a joint energy and spectrum efficiency with a much lower blocking probability compared with two baseline algorithms.

Underwater Optical Wireless Communication (UOWC) is a high-speed data transmission technology promising to support the concept of the Internet of Underwater Things (IoUT). The performance of UOWC-based IoUT networks, especially medium access control (MAC), could be affected significantly by physical factors in the underwater environment. However, there is a lack of studies on the MAC performance analysis and improvement in the literature. We study in this paper a PHY/MAC cross-layer analysis that theoretically investigates the effect of UOWC PHY transmission errors on the performance of Slotted ALOHA-based MAC. To improve the performance, we propose to enable frame retransmission in the operation of Slotted ALOHA, where the optimal number of retransmissions is studied. As confirmed in the numerical results, frame retransmission could considerably improve the MAC performance. Also, the Monte Carlo simulation verifies the accuracy of our theoretical derivation for the PHY/MAC cross-layer analysis.