# Disaster-Resilient Optical Network Survivability: A Comprehensive Survey
## 4 Disaster-Aware Network Survivability  
Increase in disaster threats as indicated by Reference [77; 78] and the rising risks of disasters around the world demand the design of disaster-resilient network against disaster risk constraints [79]. Disaster risk constraints may be reflected as service disruptions caused by natural disasters or intentional attacks. Tools for network planning and management [80] and network optimization [81] are required to mitigate the impact of disaster based failures, and more generally of spatially correlated failures in optical networks. A Geographic Information System (GIS) based fiber tool provides risk-aware new fiber network planning and the seamless management of intact infrastructures [82]. During the past couple of years, the research community has studied, with diverse perspectives, to address the network disaster vulnerabilities, as discussed below. Deployed protection schemes cannot cope with disaster-based failures. The risk profile of the network helps to identify all possible network vulnerabilities and determines the networks capability to survive geographically correlated failures. Natural disasters or physical attacks can disrupt multiple network components to function instantaneously as those have a geographic regional impact. The region of a failure can be of a circular geometry of a radius \(r\) centered at point \(O\) in the network layout. Network elements enclosed within this region can malfunction, as illustrated in Figure 4. [PERSON] in Reference [83] proposed two algorithms: (1) Failure recovery after region failure and (2) network augmentation in the case of a single region failure for providing continuous service against geographically correlated regional failures. He proposed a greedy approximation based on the approximation ratio. Several parameters have been showing up in the literature to enumerate the effects of these failures. For instance, weighted spectrum formulated in Reference [84] as \(WS(G,N)=\sum\limits_{i}(1-\lambda_{i})^{N}\) based on the Eigenvalues of the normalized Laplacian of a graph. [PERSON] et al. [85] used this parameter to assess the network survivability in the event of geographically correlated failures. Additionally, they solve an optimization problem to determine the most vulnerable geographic nodes and cuts, and conduct a comparative analysis using other common survivability measures with weighted spectrum. [PERSON] et al. [86] considered the regionally correlated failures and attacks to be a threat to optical network communication and proposed a model to identify the critical region. The model described its effectiveness in finding most vulnerable regions at fiber-level network using both weighted and unweighted topologies. The obtained results are used to enhance the performance of routing using GeoDivRP. As networks are installed geographically, having areas posed by different disasters, it leads to complications in protecting the network. [PERSON] et al. [87] proposed a model to identify affected areas for several disasters with different shapes and sizes characterized by temporal distribution. They also introduced two metrics: (1) Region Betweenness-Centrality to represent the region importance in terms of all-nodes pairs shortest paths passing through that region and (2) Risk Impact to enumerate the threat posed by disasters within a region and analyzed the effect of link existence and disaster area size on these metrics. Network geography describes the impact of failure events on a network's services and capacity. [PERSON] et al. [88] explained the geographic vulnerabilities as a geographic region that is based on the radius of effected proximity such that when nodes and their incident links fail, then the network will  
Figure 4: Critical region failures embedded in xy-plane. become disconnected. Geographic region depending on the radius of threat will reduce the search space. Similarly, [PERSON] et al. (2018) formulated a polynomial-time algorithm to decline the impact of regional failures on the region-aware network.