Data Mining Based Diagnosis in Resource Management

Mathias Beck, Jorge Marx Gómez



There are different solutions to resource allocation problems in Resource Management Systems (RMS). One of the most sophisticated ways to solve these problems is an adjustment to Quality-of-Service (QoS) settings during run-time. These settings affect the trade-off between the resource usage and the quality of the services the executed tasks create. But, to be able to determine the optimal reactive changes to current QoS settings in an acceptable time, knowledge of the resource allocation problem’s cause is necessary. This is especially significant in an environment with real-time constraints. Without this knowledge other solutions could be initiated, still an improvement to the current resource allocation, but the optimal compromise between resource requirements and QoS is likely to be missed. A resource management system (RMS) with the ability to adjust QoS settings can solve more resource allocation problems than one providing reallocation measures only. But problem-depending only optimal changes to QoS settings can solve the problem within timing constraints and thus prevent expensive system failures. Depending on the environment a RMS is used in, the failures could be a huge financial loss or even a threat to human lives. But the knowledge of a problem’s cause does not only help to solve the problem within existing timing constraints and to guarantee feasibility of the executed tasks, but helps to maximize the quality of the generated services as well. To detect upcoming problems in time, forecasting mechanisms can be integrated into the RMS. They can predict a problem in the near future, early enough for the system to react. For diagnosis of resource management problems, data mining can be applied to determine the cause of an allocation problem. The techniques implemented in this work are the k-nearest neighbor analysis and decision trees. Both techniques will make their predictions based on prior created resource allocation snapshots referring to problem cases with known cause.


  1. BERSON, A., SMITH, S. and THEARLING, K. (1999): Building Data Mining Applications for CRM. New York, NY, U.S.A.
  2. BESTAVROS, A. (1996): Middleware Support for Data Mining and Knowledge Discovery in Large-scale Distributed Information Systems. Proceedings of ACM SIGMOD'96 Data Mining Workshop
  3. FLEEMAN, D. and WELCH, L. et al. (w/o year): Quality-based Adaptive Resource Management Architecture (QARMA): A CORBA Resource Management Service. Ohio University, Athens, OH, U.S.A.
  4. IMBERMAN, S. (2002): The KDD process and data mining for performance professionals. Journal of Computer Resource Management, issue 107, p. 68-77
  5. KDNUGGETS (2003): Poll: Data Mining techniques mining techniques.htm 08/21/04

Paper Citation

in Harvard Style

Beck M. and Marx Gómez J. (2005). Data Mining Based Diagnosis in Resource Management . In Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2005) ISBN 972-8865-28-7, pages 177-185. DOI: 10.5220/0002534801770185

in Bibtex Style

author={Mathias Beck and Jorge Marx Gómez},
title={Data Mining Based Diagnosis in Resource Management},
booktitle={Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2005)},

in EndNote Style

JO - Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2005)
TI - Data Mining Based Diagnosis in Resource Management
SN - 972-8865-28-7
AU - Beck M.
AU - Marx Gómez J.
PY - 2005
SP - 177
EP - 185
DO - 10.5220/0002534801770185