AIMS: Robustness Through Sensible Introspection
Fabián E. Bustamante, Christian Poellabauer and Karsten Schwan
As Extended Abstract In Proc. of the 10th ACM SIGOPS European Workshop, September 2002.
College of Computing
Georgia Institute of Technology
Atlanta, GA 30332, USA
Our society increasingly relies on dependable complex computing systems. To be useful, dependable systems must also be robust when facing unpredictable changes to their operating environments. Introspection has proven to be a helpful approach in the design of dynamically adaptable computing systems. We argue that, for robustness, the intro-spective component itself needs to be dynamically adaptive since (i) it is effectively impossible to predict all information needed for introspection, (ii) even if we try, no introspective system will be able to manage the amount of data necessary to select the right adaptation to an overwhelming number of possible system conditions, and (iii) the right adaptation may be situation dependent as well.
At Georgia Tech we are exploring the idea of dynamically adaptive introspective components for future systems. To this end, we are building AIMS,an Adaptive Introspective Management System through which monitoring probes (or agents) can be (un-)installed at runtime, their execution can be finely tuned dynamically, and the processing done on the collected data can be changed as needed.