Synthesis of application-level utility functions for autonomic self-assessment

Abstract

We present a non-analytic approach to self-assessment for Autonomic Computing. Our approach leverages utility functions, at the level of an autonomic application, or even a single task or feature performed by that application. This paper describes the fundamental steps of our approach: instrumentation of the application; collection of exhaustive samples of runtime data about relevant quality attributes of the application, as well as characteristics of its runtime environment; synthesis of a utility function through statistical correlation over the collected data points; and embedding of code corresponding to the equation of the synthesized utility function within the application, which enables the computation of utility values at run time. We employ a number of case studies, with their results and implications, to motivate and discuss the significance of application-level utility, illustrate our statistical synthesis method, and describe our framework for instrumentation, monitoring, and utility function embedding/evaluation.