ParaDisE: parallel discovery engine for enterprise datacenters

  • Authors:
  • Sandip Agarwala;Luis A. Bathen;Divyesh Jadav;Ramani Routray

  • Affiliations:
  • IBM Almaden Research Center, San Jose, CA, USA;University of California, Irvine, Irvine, CA, USA;IBM Almaden Research Center, San Jose, CA, USA;IBM Almaden Research Center, San Jose, CA, USA

  • Venue:
  • ICAC '09 Proceedings of the 6th international conference on Autonomic computing
  • Year:
  • 2009

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Abstract

Automatic discovery and monitoring of IT resources is a critical part of enterprise systems management. In addition to ascertaining internal device configurations, this discovery process may also need to capture the capabilities, usage, connectivity, availability, and other information related to various IT components. Systems resource management (SRM) tools typically implement this discovery process using device specific APIs, custom agents and/or some standard-based solution (like WBEM and CIM). The discovery actions need to be systematically planned; an inefficient implementation or scheduling may easily take from a few minutes to several hours to complete in a large heterogeneous enterprise datacenter. This paper discusses the various challenges associated in discovering a datacenter environment and presents an autonomic monitoring framework called 'ParaDisE' that builds upon well-known industry standards and reduces the overall discovery time by more than 50%.