AUTONOMIC COMPUTING SYSTEM WITH MESO TO SUPPORTONLINE DECISION MAKING

Abstract

When we work hard, our hearts beat becomes faster. When we are hot, we sweat. The internal
functions within our body regulate themselves. Correspondingly, won’t we like to have
systems that can heal themselves? The Autonomic computing systems must be able to detect
and respond to changing conditions with little or no human intervention. The goal of autonomic
computing is to create systems that can run themselves, capable of high-level functioning while
keeping the system's complexity invisible to the user. So decision making is a critical issue in
such systems, which must learn how and when to invoke corrective actions based on past
experience. Successful autonomic systems will need to be self-configuring, self-optimizing,
self-protecting, and self-healing. So we describes the design, implementation and Evaluation of
MESO, a pattern classifier designed to support online, incremental learning and decision
making in autonomic system. A novel feature of MESO is its use of small agglomerative
clusters, called Like spheres, that aggregate similar training samples. Like spheres are
partitioned into sets during the construction of a memory efficient hierarchical data structure.
This structure facilitates data compression, which is important to many autonomic systems.
MESO achieves high accuracy while enabling rapid incremental training and classification.