Towards Reliability and Performance Prediction of Autonomic Systems with Self-Healing and Protection

Autonomic systems providing self-healing and self-protection capabilities have been proposed to efficiently automate rectification of system faults and recovery from malicious attacks. In fact, it becomes more and more difficult, labor-intensive, expensive and error-prone to conduct such recoveries. Self-healing techniques and security mechanisms are resource intensive and may affect system performances and even its full operability. Therefore, balancing security and performance in these systems is needed and self-management strategies should guarantee a minimal level of functionality. In our work, we are interested to provide methodologies and tools to predict the behaviour and efficiency of autonomic strategies relating self-healing and self-protection, before applying some healing solutions. The idea is to forecast the most appropriate configuration and ensure the effectiveness of the autonomic manager after application of a solution. So, we propose, in this paper, a general modelling methodology of an autonomic system implementing self-healing and protection, based on stochastic Petri nets. We consider in our modelling an autonomic diagnostic and recovery of fault-tolerant multi-tier systems, directed by the workload intensity, possible attacks and failure frequencies. We illustrate the effectiveness of our approach through a set of experimental analysis results.