Using Adaptive Neural Networks in Self-Healing Systems

Self-healing in systems is one of the main characteristics of Autonomic Computing (AC). In this regard the challenge is how to implement self-healing systems in real time, since online learning is required so that the running system is tuned and adapted automatically, based on the current changes of the system's behavior. In this paper, to overcome the challenges associated with self-healing comprising monitoring, interpretation, resolution, and adaptation (MIRA); a novel technique is implemented using pipelined recursive neural networks (PRNN) with a modification of the original algorithm. This method enables us to deal with several independent signals instead of one input; also on the fly learning is achieved in order to address the problems of providing system self-healing. For example instigating continuous learning rather that supervised learning which is not suitable for real systems. Traditional Predefined methods were used, but in this paper an external approach of self-adaptation is implemented to suit the changes of real time systems.