Protection against Failure of Machine Learning-based QoT Prediction

Machine learning (ML)-based methods are widely explored to predict the quality of transmission (QoT) of a lightpath, which is expected to reduce optical signal to noise ratio (OSNR) margin reserved for the lightpath and therefore improve the spectrum efficiency of an optical network. However, many studies conducting this prediction are often based on synthetic datasets or datasets obtained from laboratory. As such, these datasets may not be amply representative to cover the entire status space of a real optical network, which is often exposed in harsh environment. There are risks of failure when using these ML-based QoT prediction models. It is necessary to develop a mechanism that can guarantee the reliability of a lightpath service even if the prediction models fail. For this, we propose to take advantage of the conventional network protection techniques that are popularly implemented in an optical network and reuse their protection resources to also protect against such a type of failure. Based on the two representative protection techniques, i.e., 1+1 dedicated path protection and shared backup path protection (SBPP), the performance of the proposed protection mechanism is evaluated by reserving different margins for the working and protection lightpaths. For 1+1 path protection, we find that the proposed mechanism can achieve a zero design-margin (D-margin) for a working lightpath thereby significantly improving network spectrum efficiency, while not scarifying the availability of lightpath services. For SBPP, we find that an optimal D-margin should be identified to balance the spectrum efficiency and service availability, and although not significant, the proposed mechanism can save an up to 0.5-dB D-margin for a working lightpath, while guaranteeing the service availability.

Protection against Failure of Machine Learning-based QoT Prediction

Machine learning (ML)-based methods are widely explored to predict the quality of transmission (QoT) of a lightpath, which is expected to reduce optical signal to noise ratio (OSNR) margin reserved for the lightpath and therefore improve the spectrum efficiency of an optical network. However, many studies conducting this prediction are often based on synthetic datasets or datasets obtained from laboratory. As such, these datasets may not be amply representative to cover the entire status space of a real optical network, which is often exposed in harsh environment. There are risks of failure when using these ML-based QoT prediction models. It is necessary to develop a mechanism that can guarantee the reliability of a lightpath service even if the prediction models fail. For this, we propose to take advantage of the conventional network protection techniques that are popularly implemented in an optical network and reuse their protection resources to also protect against such a type of failure. Based on the two representative protection techniques, i.e., 1+1 dedicated path protection and shared backup path protection (SBPP), the performance of the proposed protection mechanism is evaluated by reserving different margins for the working and protection lightpaths. For 1+1 path protection, we find that the proposed mechanism can achieve a zero design-margin (D-margin) for a working lightpath thereby significantly improving network spectrum efficiency, while not scarifying the availability of lightpath services. For SBPP, we find that an optimal D-margin should be identified to balance the spectrum efficiency and service availability, and although not significant, the proposed mechanism can save an up to 0.5-dB D-margin for a working lightpath, while guaranteeing the service availability.

Towards an Efficient and Exact Algorithm for Dynamic Dedicated Path Protection

We present a novel algorithm for dynamic routing with dedicated path protection which, as the presented simulation results suggest, can be efficient and exact. We present the algorithm in the setting of optical networks, but it should be applicable to other networks, where services have to be protected, and where the network resources are finite and discrete, e.g., wireless radio or networks capable of advance resource reservation. To the best of our knowledge, we are the first to propose an algorithm for this long-standing fundamental problem, which can be efficient and exact, as suggested by simulation results. The algorithm can be efficient because it can solve large problems, and it can be exact because its results are optimal, as demonstrated and corroborated by simulations. We offer a worst-case analysis to argue that the search space is polynomially upper bounded. Network operations, management, and control require efficient and exact algorithms, especially now, when greater emphasis is placed on network performance, reliability, softwarization, agility, and return on investment. The proposed algorithm uses our generic Dijkstra algorithm on a search graph generated “on-the-fly” based on the input graph. We corroborated the optimality of the results of the proposed algorithm with brute-force enumeration for networks up to 15 nodes large. We present the extensive simulation results of dedicated-path protection with signal modulation constraints for elastic optical networks of 25, 50, and 100 nodes, and with 160, 320, and 640 spectrum units. We also compare the bandwidth blocking probability with the commonly-used edge-exclusion algorithm. We had 48,600 simulation runs with about 41 million searches.

A parallel cross-connection recovery scheme for dual link failure in elastic optical networks

In this paper, we proposed a fast recovery strategy for a dual link failure (DLF) in elastic optical network (EON). The EON is a promising solution to meet the next generation higher bandwidth demand. The survivability of high speed network is very crucial. As the network size increases the probability of the DLF and node failure also increases. Here, we proposed a parallel cross connection backup recovery strategy for DLF in the network. The average bandwidth blocking probability (BBP), bandwidth provisioning ratio (BPR), and recovery time (RT) for our proposed Intermediate node cross-connect backup for shared path protection (INCB-SPP) for ARPANET are 0.38, 2.71, 4.68 ms, and for DPP 0.70, 6.02, 8.71 ms and for SPP 0.40, 2.87, and 16.33 ms respectively. The average BBP, BPR, and RT of INCB-SPP for COST239 are 0.01, 1.71, 3.79 ms and for DPP are 0.39, 3.50, 8.20 ms and SPP are 0.04, 1.75, and 12.47 ms respectively. Hence, the proposed strategy shows lower BBP, fast connection recovery, and BPR when compared with the existing shared path protection (SPP) and dedicated path protection (DPP) approaches. Simulation is performed on ARPANET and COST239 topology networks.