Artificial registration of network stress to self-Monitor an autonomic computing system

The objective of this research is to associate artificial registrations of unauthorized network activity with stress knowledge representations to self-Monitor an autonomic computing system, which is a self-Managing system. Utilization of the danger theory perspective in artificial immune systems (AIS) is employed to illustrate how an AIS classification method contributes to four properties of autonomic computation: self-Configuration, self-Optimization, self-Healing, and self-Protection— known as the self-C.H.O.P. properties. When the stress knowledge representation is detected via self-Monitoring, then an autonomic response to self-C.H.O.P. is executed. The AIS senses its environment by monitoring system activity (components and performance) to detect activity that signals a self-C.H.O.P. property synonymous to the collaborative efforts performed by the natural immune system (NIS) and the autonomic nervous system (ANS), where involuntary bodily function regulation is achieved. The AIS is an embedded sensoring system component for the autonomic system that assists the autonomic system with signaling the registration of C.H.O.P. properties when approaching a stress threshold classified as dangerous.