new perspectives in autonomic design patterns for stream-classification-systems
Nowadays, systems are growing in size and are becoming more and more complex. Such a complexity suggests a new need for mechanisms that enable the system to self-manage, freeing administrators of low-level task management whilst delivering an optimized system. Autonomic systems sense their operating environment and automatically take action to change the environment or their own behavior. They are able to achieve it with a minimum of human effort. This is because they are: self-configuring, self-healing, self-optimizing and self-protecting. Current autonomic systems are ad hoc solutions: each system is designed and implemented from scratch i.e., there are not standard (or well-established) methodologies that autonomic system designers and/or programmers can exploit to drive their work. In this paper, we propose a design pattern that can be easily exploited by the stream-classification-systems designer to achieve autonomicity with a minimal effort. The pattern is described using a java-like notation for the classes and interfaces. A simple UML class diagram is depicted.
autonomicity adaptive strategies behavioral design pattern autonomic computing