Percolation-based Network Adaptability Under Correlated Failures

Abstract

Percolation theory has been studied to investigate network resilience by identifying a critical value of node occupation probability where a giant component (i.e., a largest component in a network) represents high network resilience. However, the concept of network resilience studied in percolation theory has been limited to measuring network fault-tolerance in the presence of failures/attacks. In this work, we take a step to extend the concept of network resilience beyond fault-tolerance by introducing network adaptability. We consider a tactical network where a node is executing multiple tasks by belonging to multiple task groups. In this type of tactical networks, a node is limited with its resource while aiming to maximize its resource utilization and task execution without being overloaded. We investigate network resilience and resource utilization of the tactical network where the network is attacked by either infectious or non-infectious attacks. To mitigate the impact of the failed/attacked nodes in the network, we propose a suite of the proposed adaptation strategies to deal with correlated, cascading failures caused by overloaded nodes due to increased workload introduced by other failed nodes. We conduct a comparative performance analysis of the set of proposed adaptation strategies and a baseline scheme with no adaptation based on metrics including a size of a giant component, node resource utilization, a number of active tasks in execution, and adaptation cost. Our simulation results show that a large size of a giant component does not necessarily ensure high resource utilization of nodes and task performance in the given tactical network.