Survivable MPLS Over Optical Transport Networks: Cost and Resource Usage Analysis

TL;DR

This study compares single and multilayer survivability strategies in MPLS over Optical Transport Networks, analyzing their resource usage and costs, and demonstrates potential savings through optimized spare capacity mapping.

Contribution

It provides a comprehensive cost and resource usage analysis of survivability deployment options in MPLS over OTN, including new methods for spare capacity allocation and integrated network optimization.

Findings

Multilayer survivability is more cost-effective for high bandwidth LSPs.

Sparse networks increase wavelength usage and configuration costs.

Optimized spare capacity mapping can save up to 22% in total costs.

Abstract

In this paper we study different options for the survivability implementation in MPLS over Optical Transport Networks (OTN) in terms of network resource usage and configuration cost. We investigate two approaches to the survivability deployment: single layer and multilayer survivability and present various methods for spare capacity allocation (SCA) to reroute disrupted traffic. The comparative analysis shows the influence of the offered traffic granularity and the physical network structure on the survivability cost: for high bandwidth LSPs, close to the optical channel capacity, the multilayer survivability outperforms the single layer one, whereas for low bandwidth LSPs the single layer survivability is more cost-efficient. On the other hand, sparse networks of low connectivity parameter use more wavelengths for optical path routing and increase the configuration cost, as compared with dense networks. We demonstrate that by mapping efficiently the spare capacity of the MPLS layer onto the resources of the optical layer one can achieve up to 22% savings in the total configuration cost and up to 37% in the optical layer cost. Further savings (up to 9 %) in the wavelength use can be obtained with the integrated approach to network configuration over the sequential one, however, at the increase in the optimization problem complexity. These results are based on a cost model with different cost variations, and were obtained for networks targeted to a nationwide coverage.