Monitoring methodology using Aspect Oriented Programming in functional based system

The problem of system complexity has recently attracted much attention. The self-healing system that is considered as a novel solution is one of the autonomic computing methodologies. It automatically recognizes faulty states of the system and provides the ability of self recovery without human intervention. There have been plenty of studies on monitoring methodologies from both inside or outside of a system. However, most of those approaches have focused on sub- partitioning the system; therefore, in this paper, we need to invent another type of methodology that is capable of considering the system as a whole. To solve above mentioned problem, we adapt the AOP (Aspect Oriented Programming) technique to the proposed system that can configure scope/constraints of each function and discover even a tiny abnormal state. In addition to that, we proposed an architecture for sensing the abnormal flow of the entire system with utilizing a state diagram based on each function and a module-function relation list. Finally, we prove the proposed system by adapting it to the case study.