Assessing Operational Impact in Enterprise Systems by Mining Usage Patterns

  • Mark Moss
  • Calton Pu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4785)

Abstract

Performing impact analysis involves determining which users are affected by system resource failures. Understanding when users are actually using certain resources allows system administrators to better assess the impact on enterprise operations. This is critical to prioritizing system repair and restoration actions, and allowing users to modify their plans proactively. We present an approach that combines traditional dependency analysis with resource usage information to improve the operational relevance of these assessments. Our approach collects data from end-user systems using common operating system commands, and uses this data to generate dependency and usage pattern information. We tested our approach in a computer lab running applications at various levels of complexity, and demonstrate how our framework can be used to assist system administrators in providing clear and concise impact assessments to executive managers.

Keywords

operational impact analysis system management data mining 

References

  1. 1.
    Kar, G., Keller, S., Calo, S.: Managing Application Services over Service Provider Networks: Architecture and Dependency Analysis. NOMS (2000)Google Scholar
  2. 2.
    Singh, A., Koropolu, M., Voruganti, K.: Zodiac: Efficient Impact Analysis for Storage Area Networks. USENIX FAST (2005)Google Scholar
  3. 3.
    Assistant Secretary of Defense, National Information Infrastructure (ASD-NII): Department of Defense Instruction (DoDI) 8580.1, Information Assurance (IA) in the Defense Acquisition System (2004)Google Scholar
  4. 4.
    Jobst, D., Preissler, G.: Mapping Clouds of SOA- and Business-related Events for an Enterprise Cockpit in a Java-based Environment. Intl. Symp. JAVA Prog. (2006)Google Scholar
  5. 5.
    Hanemann, A., Schmitz, D., Sailer, M.: A Framework for Failure Impact Analysis and Recovery with Respect to Service Level Agreements. IEEE SCC (2005)Google Scholar
  6. 6.
    EMC2|SMARTS Business Impact Manager: http://www.emc.com/products/software/smarts/bim/
  7. 7.
    IBM Tivoli Application Dependency Discovery Manager. http://www-306.ibm.com/software/tivoli/products/taddm/
  8. 8.
    Thereska, E., Narayanan, D., Ganger, G.: Towards self-predicting systems: What if you could ask ”what-if”? In: Workshop Database & Expert Systems Applications (2005) Google Scholar
  9. 9.
    Sitaraman, S., Venkatesan, S.: Forensic Analysis of File System Intrusions using Improved Backtracking. In: IEEE Workshop on IWIA (2005)Google Scholar
  10. 10.
    Brown, A., Kar, G., Keller, A.: An Active Approach to Characterizing Dynamic Dependencies for Problem Determination in a Distributed Environment. IM (2001)Google Scholar
  11. 11.
    Ensel, C.: A Scalable Approach to Automated Service Dependency Modeling in Heterogeneous Environments. IEEE EDOC (2001)Google Scholar
  12. 12.
    Aguilera, M., Mogul, J., Wiener, J., Reynolds, P., Muthitacharoen, A.: Performance Debugging for Distributed Systems of Black Boxes. SOSP (2003)Google Scholar
  13. 13.
    Mortier, R., Isaacs, R., Barham, P.: Anemone: using end-systems as a rich network management platform. Microsoft Technical Report, MSR-TR-2005-62 (2005)Google Scholar
  14. 14.
    Chen, M., Kiciman, E., Fratkin, E., Fox, A., Brewer, E.: Pinpoint: Problem Determination in Large, Dynamic Internet Services. DSN (2002)Google Scholar
  15. 15.
    Kiciman, E., Fox, A.: Detecting Application-Level Failures in Component-Based Internet Services. IEEE Trans. Neural Networks 16(5), 1027–1041 (2005)CrossRefGoogle Scholar
  16. 16.
    Hariri, S., et al.: Impact Analysis of Faults and Attacks in Large-Scale Networks. IEEE Sec. & Priv. Mag., 49–54 (September-October 2003)Google Scholar
  17. 17.
    Cohen, I., et al.: Capturing, indexing, clustering, and retrieving system history. SOSP (2005)Google Scholar
  18. 18.
    van der Aalst, W.M.P., et al.: Workflow Mining: A Survey of Issues and Approaches. ACM TKDE 47(2), 237–267 (2003)Google Scholar
  19. 19.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan-Kaufmann, San Francisco (2006)MATHGoogle Scholar
  20. 20.
    Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)MATHGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2007

Authors and Affiliations

  • Mark Moss
    • 1
  • Calton Pu
    • 1
  1. 1.CERCS, Georgia Institute of Technology 801 Atlantic Drive, Atlanta, GA 30332USA

Personalised recommendations