Network Adaptations under Cascading Failures for Mission-Oriented Networks

In the network science domain, a larger size of the giant component (i.e., the largest cluster of nodes) represents higher network resilience in terms of maximizing network availability in the presence of attacks. However, this does not necessarily represent how well the network provides promised services under attacks and/or failures. We aim to improve network resilience by introducing network adaptability (i.e., reconfiguration of a network topology), in addition to fault-tolerance. We develop a suite of strategies adopting processes from percolation theory, describing the process to percolate into a medium, for a tactical, mission-oriented network. This network is service-oriented, characterized by a number of task teams where each resource-restricted node aims to maximize resource utilization while completing multiple tasks without failure. We investigate how node failures can trigger overloads, leading to cascading failures. We consider various attack behaviors (infectious, non-infectious, random, or targeted) and analyze their effects. Through extensive simulations, we show the outperformance of the proposed adaptation strategy compared with the performance of the existing counterparts in terms of the size of the giant component, the utilization of resources, the number of alive task teams (or mission success ratio), and the adaptation cost for a large-scale, mission-oriented network under attack.