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Table 9 Relative mean standard error (RRSE) values for a different set of workloads and models

From: Runtime prediction of big data jobs: performance comparison of machine learning algorithms and analytical models

Workload f(S)

Wordcount linear

SVM quadratic

Pagerank linear

Kmeans linear

Graph (NWeight) quadratic

Amdhal equation (1)

0.085 ± 0.005

0.282 ± 0.009

0.113 ± 0.000

0.140 ± 0.019

0.252 ± 0.009

Gustafson equation (2)

0.091 ± 0.004

0.311 ± 0.003

0.120 ± 0.000

0.134 ± 0.009

0.254 ± 0.008

ERNEST equation (3)

0.100 ± 0.006

0.366 ± 0.001

0.127 ± 0.000

0.142 ± 0.017

0.231 ± 0.009

2D plate equation (4)

0.086 ± 0.005

0.272 ± 0.009

0.113 ± 0.000

0.141 ± 0.019

0.226 ± 0.007

Connected graph equation (5)

0.091 ± 0.009

0.268 ± 0.009

0.116 ± 0.000

0.141 ± 0.018

0.244 ± 0.008

Con. graph \(c=1\) equation (6)

0.091 ± 0.009

0.273 ± 0.008

0.118 ± 0.000

0.140 ± 0.018

0.243 ± 0.008

Kernel ridge regression

0.203 ± 0.009

0.234 ± 0.002

0.173 ± 0.001

0.207 ± 0.024

0.178 ± 0.003

Gradient boost regression

0.058 ± 0.003

0.064 ± 0.010

0.033 ± 0.002

0.062 ± 0.038

0.087 ± 0.004

  1. The bold data in each column indicates the smallest RRSE value in the corresponding column