A Shared Path Protection Mechanism Based on Delay Constraint in the Optical Network

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.

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Protection against Failure of Machine Learning-based QoT Prediction

10.36227/techrxiv.17097440.v1 ◽

2021 ◽

Author(s):

Ningning Guo ◽

Longfei Li ◽

Biswanath Mukherjee ◽

Gangxiang Shen

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Signal To Noise Ratio ◽

Optical Network ◽

Optical Signal ◽

Spectrum Efficiency ◽

Service Availability ◽

Protection Mechanism ◽

Backup Path ◽

Path Protection

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.

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Algorithm for shared path protection in elastic optical network based on spectrum partition

2017 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC) ◽

10.1109/imoc.2017.8121061 ◽

2017 ◽

Author(s):

Andre Luiz Ferraz Lourenco ◽

Amilcar Careli Cesar

Keyword(s):

Optical Network ◽

Elastic Optical Network ◽

Path Protection ◽

Shared Path Protection

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The Traffic Grooming Problem in Optical Networks with Respect to ADMs and OADMs: Complexity and Approximation

Algorithms ◽

10.3390/a14050151 ◽

2021 ◽

Vol 14 (5) ◽

pp. 151

Author(s):

Michele Flammini ◽

Gianpiero Monaco ◽

Luca Moscardelli ◽

Mordechai Shalom ◽

Shmuel Zaks

Keyword(s):

Cost Function ◽

Optical Networks ◽

Cost Minimization ◽

Convex Combination ◽

Optical Network ◽

Switching Cost ◽

Wavelength Assignment ◽

Intermediate Node ◽

Polynomial Time Approximation Algorithm ◽

Path Topology

All-optical networks transmit messages along lightpaths in which the signal is transmitted using the same wavelength in all the relevant links. We consider the problem of switching cost minimization in these networks. Specifically, the input to the problem under consideration is an optical network modeled by a graph G, a set of lightpaths modeled by paths on G, and an integer g termed the grooming factor. One has to assign a wavelength (modeled by a color) to every lightpath, so that every edge of the graph is used by at most g paths of the same color. A lightpath operating at some wavelength λ uses one Add/Drop multiplexer (ADM) at both endpoints and one Optical Add/Drop multiplexer (OADM) at every intermediate node, all operating at a wavelength of λ. Two lightpaths, both operating at the same wavelength λ, share the ADMs and OADMs in their common nodes. Therefore, the total switching cost due to the usage of ADMs and OADMs depends on the wavelength assignment. We consider networks of ring and path topology and a cost function that is a convex combination α·|OADMs|+(1−α)|ADMs| of the number of ADMs and the number of OADMs deployed in the network. We showed that the problem of minimizing this cost function is NP-complete for every convex combination, even in a path topology network with g=2. On the positive side, we present a polynomial-time approximation algorithm for the problem.

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