Large-scale software systems are in general difficult to manage and monitor. In many cases, these systems display unexpected behavior, especially after being updated or when changes occur in their environment (operating system upgrades or hardware migrations, to name a few). Therefore, to handle a changing environment, it is desirable to base fault detection and performance monitoring on self-adaptive techniques. Several studies have been carried out in the past which, inspired on the immune system, aim at solving complex technological problems. Among them, anomaly detection, pattern recognition, system security and data mining are problems that have been addressed in this framework. There are similarities between the software fault detection problem and the identification of the pathogens that are found in natural immune systems. Being inspired by vaccination and negative and clonal selection observed in these systems, we developed an effective self-adaptive model to monitor software applications analyzing the metrics of system resources. 
Monitoring applications: An immune inspired algorithm for software-fault detection