eModel: addressing the need for a flexible modeling framework in autonomic computing

Abstract

The paper describes a novel, flexible framework, eModel, designed to address the runtime requirements of autonomic computing: on-line workload measurement, analysis, and prediction. The eModel architecture has been developed using platform independent technology (XML and Java) to allow for maximum portability while also allowing for ease-of-integration with existing measurement and system management tools. The eModel toolkit consists of a GUI based model builder tool, a data base deployment tool, a runtime tool, and an analysis tool. In addition to the toolkit, the eModel design provides a runtime architecture which can be deployed directly without using any interaction with the GUI. The architecture is flexible enough to allow for incorporation with models of various complexity, including modeling techniques that require a hierarchical approach to attain reasonable accuracy based upon on-line, measured data. We present examples that illustrate eModel as a capacity planning tool as well as an augmentation to autonomic system management in an effort to highlight the technological gaps that the eModel framework is capable of bridging.