IPMI-based Efficient Notification Framework for Large Scale Cluster Computing

  • Authors:

  • Chokchia Leangsuksun;Tirumala Rao;Anand Tikotekar;Stephen L. Scott;Richard Libby;Jeffrey S. Vetter;Yung-Chin Fang;Hong Ong

  • Affiliations:

  • Louisiana Tech University, USA;Louisiana Tech University, USA;Louisiana Tech University, USA;Oak Ridge National Laboratory, USA;Intel Corporation, USA;Intel Corporation, USA;Dell, Inc., USA;Oak Ridge National Laboratory, USA

  • Venue:
  • CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
  • Year:
  • 2006

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Abstract

The demand for an efficient Jhult 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 afects the scalability and peijbrmance of monitoring and management tools. In this paper, we propose a problem notiJication framework that directly addresses the issue of monitor scalability. We first present the design and inzpIementation of our step-by-step approach to analyzing, filtering, and clas,slfiing 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.