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Workload characterization for trend analysis
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Source ACM SIGMETRICS Performance Evaluation Review archive
Volume 10 ,  Issue 2  (Summer 1981) table of contents
Pages: 5 - 15  
Year of Publication: 1981
ISSN:0163-5999
Authors
A. Esposito  Università di Napoli Naples, Italy
A. Mazzeo  Università di Napoli Naples, Italy
P. Costa  Università di Napoli Naples, Italy
Publisher
ACM  New York, NY, USA
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ABSTRACT

The methodology of analysis proposed in this paper aims at predicting the workload of a computer. This methodology consists of applying an algorithm of clustering to the workload, its jobs being identified by a pair (X,P), where X is the resource-vector of the job and P stands for the priority given to the job by the user.The hereby obtained clusters are then associated to the ai activities developed in the system and determine the influence of each ai to the overall workload. By repeating this operation at different times, either the periodicity or the monotonic changes that may occur in each activity are determined. This makes it possible to predict the evolution of the overall workload and consequently to evaluate changes to be carried out in the system.The above methodology is applied to a specific case and is illustrated in its various phases. The results obtained have validated the method. The study is still going on, with continuous periodical observations in order to update the data.


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
Ferrari, D., Workload Characterization and Selection in Computer Performance Measurement, Computer 4 (1972), 18--24
 
2
Ferrari, D., Computer Systems Performance Evaluation (Prentice-Hall, Englewood-Cliffs, 1978).
 
3
Agrawala, A. K., Mohr, J. M. and Bryant, R. M., An Approach to the Workload Characterization Problem, Computer 6 (1976), 18--29
 
4
Hellerman, H. and Conroy, T. F., Computer System Performance (McGraw-Hill, New York, 1975)
 
5
Artis, H. P., Capacity Planning for MVS Computer Systems in Ferrari, D., (ed.), Performance of Computer Installation (North Holland, Amsterdam, 1978) 25--53
 
6
Agrawala, A. K., Mohr, J. M., Predicting the Workload of a Computer System, Nat. Comp.Conf. (1978), 465--471
 
7
Anderberg, M. R., Cluster Analysis for Applications (Academic Press, New York, 1973)
 
8
Lebart, L., Morineau, A. and Tabard, N., Techniques de la description statistique (Dunod, Paris, 1977)
 
9
Forgy, E. W., Cluster Analysis of Multivariate Data: Efficiency versus Interpretability of Classification, Biometrics 21 (1965)
 
10
De Carlini, U., Mazzeo, A. and Savy, C., Analisi del carico di un sistema EDP per applicazioni didattiche e scientifiche, Congresso AICA, 1 (1979) Bari 295--303
 
11
Esposito, A., Mazzeo, A. and Savy, C., Systems Performance Evaluation: a Simulation Model for Batch Processing, in Dekker, L., Savastano, G. and Vansteenkiste, G. C. (eds.) Simulation of Systems 79 (North-Holland, Amsterdam, 1980)
 
12
Mc Rae, D. J., MIKCA: a FORTRAN IV Iterative K-means Cluster, CTB/McGraw-Hill (1970) Monterey
 
13
 
14
Lauro, N. and Costa, P., La caracterisation de la charge d'un systeme EDP par les methodes fa-torielles et la classification automatique, Analyse des Donnees, INRIA (1980) Napoli

Collaborative Colleagues:
A. Esposito: colleagues
A. Mazzeo: colleagues
P. Costa: colleagues