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Therefore, this paper considers cloud services with the periodic, growing, and unpredictable workload patterns.
On the client side, a customized script is used along with the TPC-W workload generator to produce the growing, the periodic, and the unpredictable workload patterns.
Meanwhile, typical existing resource scaling management approaches often rest on single monitor category statistics and are driven by certain threshold algorithms, they usually fail to function effectively in case of dealing with complicated and unpredictable workload patterns.
The goal of this experiment is to compare the accuracy of the MLP, the MLPWD, and the SVM algorithms for predicting the periodic, the growing, and the unpredictable workload patterns.
The main goal of the experiment presented in this section is to verify the empirical and the structural risk minimization principles behaviors in the environments with the periodic, growing, and unpredictable workload patterns.
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Contrariwise, the unpredictable workload pattern does not follow a repeatable trend.
The same reasoning applies to the environments with the unpredictable workload pattern.
Hypothesis 1b: The empirical risk minimization principle performs better in the environments with the unpredictable workload pattern.
To predict the future workload in an environment with the unpredictable workload pattern it is better to use MLP algorithm with a large sliding window size.
On the other hand, for the environments with the unpredictable workload pattern, it is better to use the ERM principle with the bigger window sizes.
The result is that in the environments with the unpredictable workload pattern the MLP algorithm outperforms the MLPWD and the SVM algorithms.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com