An Approach for Performance Modelling and Analysis of Multi-tiers Autonomic Systems

The need of a modern system to be more autonomous with regard to its environment make autonomic systems very attractive, providing them with self-management capabilities. Usually, an autonomic system reconfigures itself to overcome some problem or optimize itself. However, these reconfigurations may result in a loss of performance or even a service degradation. To avoid such situations, this paper proposes a performance prediction approach for autonomic systems, based on formal modelling. Our goal is to forecast the most appropriate configuration for an autonomic system, particularly, a multi-tier system, in order to get an efficient use of resources. The main idea is to model resource allocation triggered by the autonomic loop for achieving the best configuration. Then, we show how to analyse the whole system and evaluate the autonomic loop impact on system performances. The method is based on Stochastic Petri Nets (SPN) modelling. The resource availability, the workload intensity and the autonomic features are targeted. A typical example of a multi-ers system with one server is presented, analysed with the GreatSPN tool, to show the effectiveness of the proposed approach.