RMSA algorithms resilient to multiple node failures in dynamic EONs

In Elastic Optical Networks (EONs), the way different service demands are supported in the network is ruled by the Routing, Modulation and Spectrum Assignment (RMSA) algorithm, which decides how the spectrum resources of the optical network are assigned to each service demand. In a dynamic EON, demand requests arrive randomly one at a time and the accepted demands last in the network for a random time duration. So, one important goal of the RMSA algorithm is the efficient use of the spectrum resources to maximize the acceptance probability of future demand requests. On the other hand, multiple failure events are becoming a concern to network operators as such events are becoming more frequent in time. In this work, we consider the case of multiple node failure events caused by malicious attacks against network nodes. In order to obtain RMSA algorithms resilient to such events, a path disaster availability metric was recently proposed which takes into account the probability of each path not being disrupted by an attack. This metric was proposed in the offline variant of the RMSA problem where all demands are assumed to be known at the beginning. Here, we exploit the use of the path disaster availability metric in the RMSA of dynamic EONs. In particular, we propose RMSA algorithms combining the path disaster availability metric with spectrum usage metrics in a dynamic way based on the network load level. The aim is that the efficient use of the resources is relaxed for improved resilience to multiple node failures when the EON is lightly loaded, while it becomes the most important goal when the EON becomes heavily loaded. We present simulation results considering a mix of unicast and anycast services in 3 well-known topologies. The results show that the RMSA algorithms combining the path disaster availability metric with spectrum usage metrics are the best trade-off between spectrum usage efficiency and resilience to multiple node failures.publishe

On the Efficient Flow Restoration in Spectrally-Spatially Flexible Optical Networks

We focus on the efficient modeling and optimization of the flow restoration in the spectrally-spatially flexible optical networks (SS-FONs) realized using a single mode fiber bundle. To this end, we study a two-phase optimization problem, which consists of: (i) the network planning with respect to the spectrum usage and (ii) the flow restoration after a failure aiming at maximizing the restored bit-rate. Both subproblems we formulate using the integer linear programming with two modeling approaches—the node-link and the link-path. We perform simulations divided into: (i) a comparison of the proposed approaches, (ii) an efficient flow restoration in SS-FONs—case study. The case study focuses on the verification whether the spectral-spatial allocation may improve the restoration process (compared to the spectral allocation) and on the determination of the full restoration cost (the amount of additional resources required to restore whole traffic) in two network topologies. The results show that the spectral-spatial allocation allows us to restore up to 4% more traffic compared to the restoration with only the spectral channels. They also reveal that the cost of the full traffic restoration depends on plenty of factors, including the overall traffic volume and the network size, while the spectral-spatial allocation allows us to reduce its value about 30%

A column generation technique for routing and spectrum allocation in cloud–ready survivable elastic optical networks

Driven by increasing user requirements and expectations, the fast development of telecommunications networks brings new challenging optimization problems. One of them is routing and spectrum allocation (RSA) of three types of network flows (unicast, anycast, multicast) in elastic optical networks (EONs) implementing dedicated path protection (DPP). In the paper, we model this problem as integer linear programming (ILP) and we introduce two new optimization approaches—a dedicated heuristic algorithm and a column generation (CG)-based method. Then, relying on extensive simulations, we compare algorithm performance with reference methods and evaluate CG efficiency in detail. The results show that the proposed CG method significantly outperforms reference algorithms and achieves results very close to optimal ones (the average distance to optimal results was at most 2.1%)

Fragmentation-Aware Traffic Grooming with Lane Changes in Spectrally–Spatially Flexible Optical Networks

Traffic in current networks is constantly increasing due to the growing popularity of various network services. The currently deployed backbone optical networks apply wavelength division multiplexing (WDM) techniques in single-core single-mode fibers (SMFs) to transmit the light. However, the capacity of SMFs is limited due to physical constraints, and new technologies are required in the near future. Spectrally–spatially-flexible optical networks (SS-FONs) are proposed to provide a substantial capacity increase by exploring the spatial dimension. However, before this technology will reach maturity, various aspects need to be addressed. In particular, during traffic grooming, multiple small requests are aggregated into large-capacity optical corridors in an optical layer to increase the spectral efficiency. As the summary traffic volume is dynamically changing, it may be required to set up and tear down optical channels, which results in network fragmentation. As a consequence, in a congested network, part of the requests can be blocked due to the lack of spectrum resources. Thus, the grooming of traffic and the creation of lightpaths should be carefully designed to minimize network fragmentation. In this study, we present several fragmentation metrics and develop a fragmentation-aware traffic grooming algorithm that reduces the bandwidth blocking probability

Modeling and Prediction of Daily Traffic Patterns—WASK and SIX Case Study

The paper studies efficient modeling and prediction of daily traffic patterns in transport telecommunication networks. The investigation is carried out using two historical datasets, namely WASK and SIX, which collect flows from edge nodes of two networks of different size. WASK is a novel dataset introduced and analyzed for the first time in this paper, while SIX is a well-known source of network flows. For the considered datasets, the paper proposes traffic modeling and prediction methods. For traffic modeling, the Fourier Transform is applied. For traffic prediction, two approaches are proposed—modeling-based (the forecasting model is generated based on historical traffic models) and machine learning-based (network traffic is handled as a data stream where chunk-based regression methods are applied for forecasting). Then, extensive simulations are performed to verify efficiency of the approaches and their comparison. The proposed modeling method revealed high efficiency especially for the SIX dataset, where the average error was lower than 0.1%. The efficiency of two forecasting approaches differs with datasets–modeling-based methods achieved lower errors for SIX while machine learning-based for WASK. The average prediction error for SIX reached 3.36% while forecasting for WASK turned out extremely challenging