ACM Home Page
Please provide us with feedback. Feedback
Digital Library logoTake a look at the new version of this page: [ beta version ]. Tell us what you think.
Modeling file-system input traces via a two-level arrival process
Full text PdfPdf (764 KB)
Source Winter Simulation Conference archive
Proceedings of the 28th conference on Winter simulation table of contents
Coronado, California, United States
Pages: 1230 - 1237  
Year of Publication: 1996
ISBN:0-7803-3383-7
Authors
Peter P. Ware  Department of Computer and Information Science, The Ohio State University, Columbus, OH
Thomas W. Page, Jr.  Department of Computer and Information Science, The Ohio State University, Columbus, OH
Barry L. Nelson  Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL
Sponsors
INFORMS/CS : Computer Science TC
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
SCS : Society for Computer Simulation
ASA : American Statistical Association
NIST : National Institue of Standards & Technology
IEEE-CS : Computer Society
IEEE-SMCS : Systems, Man & Cybernetics Society
ACM: Association for Computing Machinery
Publisher
IEEE Computer Society  Washington, DC, USA
Bibliometrics
Downloads (6 Weeks): 0,   Downloads (12 Months): 4,   Citation Count: 0
Additional Information:

abstract   references   collaborative colleagues  

Tools and Actions: Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/256562.256937
What is a DOI?

ABSTRACT

A method for analyzing, modeling and simulating a two-level arrival--counting process is presented. This method is particularly appropriate when the number of independent processes is large. The initial motivation for this method was the need to analyze and represent computer file system trace data that involves activity on some 8,000 files. The method is also applicable to network trace data characterizing communication patterns between pairs of computers. Cluster analysis with a novel stopping rule is used to decompose the arrival process into groups. The resulting clusters can be characterized using the time between clusters, the time between arrivals within clusters, and the size of each cluster. Each of these three components is then analyzed as a univariate problem. The effectiveness of this method is measured by comparing the output of a simulation driven by the original trace data to the output of the same simulation driven by the input model.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Devroye, L. (1986), Non-Uniform Random Variate Generation, Springer-Verlag, New York, NY.
 
2
Guy, R. G., Heidemann, J. S., Mak, W., Page, Jr., T. W., Popek, G. J. & Rothmeier, D. (1990), Implementation of the Ficus replicated file system, in 'USENIX Conference Proceedings', USENIX, pp. 63-71.
 
3
Hisgen, A. (1990), Dec firefly trace data, Data obtained from author on 8mm tape.
 
4
Jain, R. (1991), The Art of Computer Systems Performance Analysis: Techniques for Experiment Design, Measurement, Simulation and Modeling, John Wiley & Sons, Inc.
 
5
Jain, R. & Routhier, S. A. (1986), 'Packet trainsmeasurements and a new model for computer network traffic', IEEE Journal on Selected Areas in Communications SAC-4(6), 986-995.
 
6
Johnson, N. L. (1949), 'Systems of frequency curves generated by methods of translation', Biometrika 36, 149-176.
 
7
Kaufman, L. & Rousseeuw, J. (1990), Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons, Inc, New York, NY.
 
8
Milligan, G. W. & Cooper, M. C. (1985), 'An examination of procedures for determining the number of dusters in a data set', Psychometrika 50(2), 159-179.
 
9
Ord, j. K. (1972), Families of Frequency Distributions, Griffin, London.
 
10
 
11
Swain, J. J., Venkatraman, S. & Wilson, J. R. (1988), 'Least-squares estimation of distribution functions in Johnson's translation system', Journal of Statistical Computation and Simulation 29, 271-297.
12
Collaborative Colleagues:
Peter P. Ware: colleagues
Thomas W. Page, Jr.: colleagues
Barry L. Nelson: colleagues