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Michael A. Iverson, Füsun Özgüner, Lee Potter, "Statistical Prediction of Task Execution Times through Analytic Benchmarking for Scheduling in a Heterogeneous Environment," IEEE Transactions on Computers, vol. 48, no. 12, pp. 13741379, December, 1999.  
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@article{ 10.1109/12.817403, author = {Michael A. Iverson and Füsun Özgüner and Lee Potter}, title = {Statistical Prediction of Task Execution Times through Analytic Benchmarking for Scheduling in a Heterogeneous Environment}, journal ={IEEE Transactions on Computers}, volume = {48}, number = {12}, issn = {00189340}, year = {1999}, pages = {13741379}, doi = {http://doi.ieeecomputersociety.org/10.1109/12.817403}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Computers TI  Statistical Prediction of Task Execution Times through Analytic Benchmarking for Scheduling in a Heterogeneous Environment IS  12 SN  00189340 SP1374 EP1379 EPD  13741379 A1  Michael A. Iverson, A1  Füsun Özgüner, A1  Lee Potter, PY  1999 KW  Heterogeneous distributed computing KW  execution time estimation KW  analytic benchmarking KW  nonparametric regression KW  distance matrices. VL  48 JA  IEEE Transactions on Computers ER   
Abstract—In this paper, a method for estimating task execution times is presented in order to facilitate dynamic scheduling in a heterogeneous metacomputing environment. Execution time is treated as a random variable and is statistically estimated from past observations. This method predicts the execution time as a function of several parameters of the input data and does not require any direct information about the algorithms used by the tasks or the architecture of the machines. Techniques based upon the concept of analytic benchmarking/code profiling [1] are used to characterize the performance differences between machines, allowing observations from dissimilar machines to be used when making a prediction. Experimental results are presented which use actual execution time data gathered from
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