GAP: A General Approach to Quantitative Diagnosis of Performance Problems

Quantitative performance diagnosis (QPD) provides explanations that quantify the impact of problem causes. An example of such an explanation is it Increased web server traffic accounts for 90% of the increase in LAN utilization, which in turn accounts for 20% of the increase in web response times. This paper describes GAP, a general approach to quantitative performance diagnosis. GAP has two parts: (1) an algorithm for computing quantitative performance diagnoses; and (2) a framework for constructing diagnostic techniques that provides the basis for quantifications produced by the algorithm. The GAP algorithm makes use of a measurement navigation graph, a directed acyclic graph whose nodes are measurement variables and whose arcs have weights that quantify the effect of child variables (e.g., LAN utilization) on parent variables (e.g., response time). The framework for developing diagnostic techniques consists of (a) the choice of statistic (e.g., mean, variance) to aggregate problem values, and (b) the estimator of the statistic.

[1]  K. Frohlich,et al.  Model-aided diagnosis: an inexpensive combination of model-based and case-based condition assessment , 2001 .

[2]  Behrokh Samadi TUNEX: A Knowledge-Based System for Performance Tuning of the UNIX Operating System , 1989, IEEE Trans. Software Eng..

[3]  Raymond Reiter,et al.  A Theory of Diagnosis from First Principles , 1986, Artif. Intell..

[4]  Magid Igbaria,et al.  A knowledge based decision support system for computer performance management , 1992, Decis. Support Syst..

[5]  T Bowen,et al.  Expert Systems for Performance Review , 1987, The Journal of the Operational Research Society.

[6]  J. L. Hellerstein,et al.  A unified approach to interpreting measurement data in performance management applications , 1993, Proceedings of 1993 IEEE 1st International Workshop on Systems Management.

[7]  D. J. Smith Monitoring/diagnostic systems enhance plant asset management , 1992 .

[8]  M. F. Abu Ei-Yazeed Minimum measurements at minimum set of test nodes for analog circuit fault diagnosis , 2002, MELECON 2002.

[9]  Krishna R. Pattipati,et al.  A hidden Markov model-based algorithm for fault diagnosis with partial and imperfect tests , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[10]  Bernard Domanski A PROLOG-based expert system for tuning MVS/XA , 1989, PERV.

[11]  Subhash C. Agrawal Metamodeling: A Study of Approximations in Queueing Models , 1984 .

[12]  David R. Irvin Monitoring the performance of commercial T1-rate transmission service , 1991, IBM J. Res. Dev..

[13]  Ben P. Wise,et al.  Self-Explanatory Financial Planning Models , 1984, AAAI.

[14]  Raymond Reiter,et al.  Characterizing Diagnoses and Systems , 1992, Artif. Intell..

[15]  Rolf Isermann Process fault diagnosis based on process model knowledge , 1988 .

[16]  Harvey J. Greenberg,et al.  Rule-based intelligence to support linear programming analysis , 1993, Decis. Support Syst..

[17]  Chantal Robach,et al.  Performance Evaluation of Distributed Diagnosis Algorithms in Parallel Systems , 1998, Parallel Comput..

[18]  Andrzej J. Strojwas,et al.  Path delay fault diagnosis and coverage-a metric and an estimationtechnique , 2001, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[19]  Joseph L. Hellerstein What's-Different Analysis and its Application to Performance Management in VM SP/HPO , 1988, Int. CMG Conference.