IPMI-based Efficient Notification Framework for Large Scale Cluster Computing

Abstract

The demand for an efficient faith tolerance system has led to the development of complex monitoring infrastructure, which in turn has created an overwhelming task of data and event management. The increasing level of details at the hardware and software layer clearly affects the scalability and performance of monitoring and management tools. In this paper, we propose a problem notification framework that directly addresses the issue of monitor scalability. We first present the design and implementation of our step-by-step approach to analyzing, filtering, and classifying the plethora of node statistics. Then, we present experimental results to show that our approach only needs minimal system resource and thus has low overhead. Finally, we introduce our Web-based cluster management system that provides hardware controls at both cluster and nodal levels