Correlating node centrality metrics with node resilience in self-healing systems with limited neighbourhood information

Resilient systems must self-heal their components and connections to maintain their topology and function when failures occur. This ability becomes essential to many networked and distributed systems, e.g., virtualisation platforms, cloud services, microservice architectures and decentralised algorithms. This paper builds upon a self-healing approach where failed nodes are recreated and reconnected automatically based on topology information, which is maintained within each node’s neighbourhood. The paper proposes two novel contributions. First, it offers a generic method for establishing the minimum size of a network neighbourhood to be known by each node in order to recover the system’s component interconnection topology under a certain probability of node failure. This improves the previous proposal by reducing resource consumption, as only local information is communication and stored. Second, it adopts analysis techniques from complex networks theory to correlate a node’s recovery probability with its closeness centrality within the self-healing system. This allows strengthening a system’s resilience by analysing its topological characteristics and rewiring weakly-connected nodes. These contributions are supported by extensive simulation experiments on different systems with various topological characteristics. Obtained results confirm that nodes which propagate their topology information to more neighbours are more likely to be recovered; while requiring more resources. The proposed contributions can help practitioners to: identify the most fragile nodes in their distributed systems; consider corrective measures by increasing each node’s connectivity; and, establish a suitable compromise between system resilience and costs. • Studies a self-healing approach that recreates and reconnects failed nodes based on network topology information. • Proposes a generic method to determine the minimum amount of topology information that is necessary to recover various networks, depending on the network’s topological features. • Correlates node centrality metrics (e.g. betweenness centrality, closeness centrality, degree, eccentricity, eigenvector centrality and page rank centrality) with the probability of a node being lost after failure, because of the limited amount of topological information. • Establishes that a node’s closeness centrality metric is most essential to the node’s recovery probability, based on experiments with several selected networks. This implies that a node that disseminates its local topology information to more neighbouring nodes is more likely to be recovered.