Optical Switching and Networking最新文献

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.

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.

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.

Next Generation Ethernet Passive Optical Network (NG-EPON) is considered to be future prospective access technology that could help to achieve 100Gbps data rates. Wavelength bonding is a phenomenon that can help Optical Network Units (ONU) to enhance their transmission capabilities. Using wavelength bonding, an ONU could transmit on multiple wavelength channels in parallel. The ONUs can achieve data transmission rates ranging from 25Gbps to 100Gbps. In upstream direction, ONUs share different available channels in time-sharing manner to effectively utilize the resources in NG-EPON. Dynamic Wavelength & Bandwidth Allocation (DWBA) algorithm is required for efficient allocation of wavelength and bandwidth resources in upstream direction. DWBA plays a vital role to help ONUs for transmission on multiple channels simultaneously. When an ONU transmits on multiple channels, a frame-rearrangement problem would occur at the Optical Line Terminal (OLT). OLT suffers from an extra overhead of frame-rearrangement; as the received frames at OLT are not in proper sequence. DWBA can play a vital role in avoiding frame rearrangement overhead. We proposed a DWBA algorithm to avoid/minimize frame rearrangement problem and efficient bandwidth allocation in NG-EPON. Our proposed DWBA avoids and minimizes frame-rearrangement problem and provides efficient resource allocation. We comparatively analyzed and evaluated our proposed DWBA with the existing DWBA algorithm. The simulation results show that our proposed DWBA minimizes frame-rearrangement problem as compared to existing DWBA algorithms and proves to be more efficient based on average (end-to-end) delay and completion time.

In this work the spectrum and power allocation (SPA) trade-off in elastic optical network (EON) is discussed in terms of the residual margin and residual spectrum in real-time application, both terms refer to the normalization of the sum-power and sum-spectrum in the resource allocation, respectively. Realistic scenarios have been investigated using optical performance monitoring techniques to measure the quality of transmission (QoT). The SPA-EON problem is formulated and three algorithms finding improved performance-complexity trade-offs are proposed to solve it: i) an analytical method based on combinatorial optimization, namely SPA-CO algorithm, ensuring the optimal solution but with a high computational cost; ii) a sub-optimum low-complexity method based on distance adaptive transmission (DAT), namely SPA-DAT, and iii) an SPA algorithm based on the distributed Verhulst algorithm, namely SPA-V, which achieves good solutions under acceptable computational time. A bunch of metrics, including probability of success, sum-power, and allocated spectrum are evaluated for the three SPA algorithms. The SPA-V was proved to be promising in EON operation, achieving the best performance-complexity trade-off.

The elastic optical network (EON) is the most attractive architecture for the next generation of optical networks. Dealing with high bit-rate traffic, EON faces the challenge of ensuring survivability to operate with stringent Service Level Agreements. This article proposes a Deep Neural Network model with a multi-objective Fuzzy Inference System (FIS) to solve the Routing and Spectrum Assignment problem with Shared Backup Path Protection. The algorithm aims to optimize the trade-off between blocking probability (BP) and fault restoration ratio (FRR). It uses a new spectrum-fragmentation metric to improve the FRR of affected connections. The FIS adds features of load balancing and alignment of allocation path solutions. We use figures of merit as BP of connection requests, FRR, spectrum utilization ratio, and connection downtime to evaluate the algorithm performance. The proposed algorithm organizes traffic in a less fragmented way, efficiently uses routing and protection resources, and performs well compared to similar algorithms related in the literature.

In this paper, we focus on the efficient dynamic routing in Spectrally-Spatially Flexible Optical Networks (ss-fon) realized using Single-Mode Fiber Bundles (smfbs). We study two scenarios – unprotected network (np) and network protected by dedicated path protection (dpp) against a single link failure. For these configurations, we propose a dedicated optimization approach (Enhanced Adaptive Spectral-Spatial Allocation – e-assa), which makes use of the traffic categorization and application of different allocation strategies for different traffic categories. To select beneficial categorization rules, we employ supervised learning paradigm. We show that the selection of a beneficial regression algorithm to support network optimization cannot be performed based only on standard metrics like r2 but some additional measures/experiments are necessary. Then, we carry out extensive numerical experiments in order to tune the approach and to evaluate its efficiency based on the comparison with reference methods. The results prove high efficiency of the proposed optimization framework, which provides low blocking probability and significantly shorter processing time compared to the best of reference methods.

The advent of Elastic Optical Networks (EON) has led to significant improvements in optical network spectrum utilization when compared to Wavelength Division Multiplexing Optical Networks. However, the EON brought challenges to be explored, notably the Power, Routing, Modulation Level and Spectrum Assignment (PRMLSA) problem. This article aims to explore techniques for the PRMLSA problem, being developed two strategies named Shortest and Least Allocated (SLA) Path and Route-Based Spectrum Assignment (RBSA), which, respectively, include the link power spectral density inspection dynamic for routing and a physical layer factor (distance traveled) for Spectrum Assignment. Furthermore, a simplified version of the Adaptive Power Assignment (APA) [1] algorithm is presented, in which a power value between the minimum necessary and the maximum allowed is assigned to the signal. The simultaneous use of the SLA and RBSA algorithms resulted in locks of up to 0.00132%, being more than 10 times lower than the 0.0164% of the Shortest-Path and First-Fit algorithms. While the simplification of the APA resulted in 18.38% of the execution time of its respective original version, but with an increase in the blocking probability, which went from 0.016% to 0.031%, despite still being below conventional techniques, as the Constant Power Assignment strategy.

This paper proposes a novel joint resource allocation technique for flexible-grid systems by utilizing non-dominant sort genetic algorithm (NSGA-II) in a multi-objective optimization framework. It pioneers the implementation of an evolutionary mechanism to optimize resources as means of mitigation of physical layer impairments. This investigation initially introduces a proposal in which bandwidth reduction, maximization of the minimum signal-to-noise ratio (SNR) margin, and minimization/maximization of the sum of SNR margins are studied under dual-objective Pareto analysis in the link-level scenario. Later, the technique extends existing provisioning strategies for network planning by targeting the reduction of blocking and spectral utilization of optical connections.

In this work, we present a co-evolutionary genetic (CEGA) algorithm to adapt the optical launch powers and optimize the signal-to-noise ratio (SNR) values based on maximizing the minimum SNR margin. The introduced co-evolutionary algorithm provides lower computational complexity rather than convex optimization and linear programming techniques, applicable for both static and time-critical dynamic networking. The enhanced Gaussian noise nonlinear model is exploited to take the physical-layer impairments into account, considering networks with partial spectrum utilization. To optimize the minimum SNR margin, we formulate the power allocation problem as a minimax optimization problem. To this end, a two-space genetic algorithm (GA) is proposed to reduce the computational complexity. The obtained results demonstrate that the introduced co-evolutionary algorithm outperforms the common optimization methods in terms of run time. It is shown that the computational complexity of proposed co-evolutionary algorithm is significantly lower than convex and single-space evolutionary approaches by several orders of magnitude. Moreover, the minimum SNR margin is improved by about 2.4 dB compared to a flat launch power optimization.