# Instantaneous Networking Service Availability for Disaster Recovery
## 6 Numerical Examination  
Numerical examination is conducted to assess the performance of the proposed scheme in comparison with the conventional scheme. The aim is to identify the effectiveness of I-NSA in measuring networking service restoration that includes partial and/or temporal restoration phases on the way to the complete restoration. We then verify the I-NSA improvement by utilizing the restoration method based on LI-Cloud. We attempted to allow the evaluation to involve the following three aspects. First, potential users are widely distributed in the disaster region, which comprises multiple local areas. A user in a local area may communicate with other users who are inside the local area, in the different local areas, or outside of the disaster region. Second, probability of communication occurrence among users depends on the distance between the users [23]--that is, a user communicates more with people in the nearby places rather than the people faraway. Third, partial and temporal restoration of a networking service can support the communication among users nearby. Based on the aspects above, we set up the following evaluation scenarios. The disaster region consists of three local areas denoted by A, B, and C. A user in the disaster region tries to communicate with other users either in the same local area or outside the areas. We assume the probability of a user communication occurrence as shown in Table 2, in which the probabilities were derived based on the gravity model that a user has a higher probability to communicate with other users nearby than with users who are far away [23]. More specifically, we suppose that the probability of communication between two users is in reverse proportion to the distance between them. Let the probability of the emergence of communication between two users in A be 0.2. Suppose that the average distance between two users in A and B is about 2.8 times of the distance between two users in A. Then, we set the probability of communication between two users in A and B as 0.025. Although there can be various parameter configurations in evaluation, we employ the setup in Table 2 to represent the basic features that are involved in the evaluation aspects. In what follows, we adopt notations of Ta, Tb, and Tc to denote the average launching time of LI-Clouds A, B, and C, respectively. \(T_{AG}\), \(T_{BG}\), and \(T_{CG}\) denote the average launching time of global network connections at LI-Clouds A, B, and C.  
We consider two types of scenarios regarding the deployment of local and global networking services, as shown in Figure 3. The first setup mimics the independent deployment of networking services, allowing the average launching times of LI-Clouds A, B, and C to be independent and similar to each other. We call this deployment scenario the parallel deployment of networking services. The second scenario setup adopts sequential deployment of LI-Clouds and allows their launching time to be one after another with a certain time interval between each other. We call this deployment scenario the sequential deployment of networking services. The choice of the restoration methods depends on the disaster situation and the available resources, which include a number of available operators and vehicles to deliver and operate LI-Clouds. When there are enough resources, the parallel distribution of LI-Clouds for launching local networking services is possible. In the case of insufficient resources, however, the distribution of LI-Clouds has to be conducted in a sequential manner that takes much more time than the parallel distribution. In the evaluation, two types of launching time distributions are employed, including Poisson and Uniform distributions.