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Publicação
Model optimisation for server loading forecasting with genetic algorithms
Orientador(es)
Resumo(s)
Server load prediction has different approaches and
applications, with the general goal of predicting future load for
a period of time ahead on a given system. Depending on the
specific goal, different methodologies can be defined. In this paper, we study the use of temporal factors, along with its manual
or optimised application using genetic algorithms. The main
steps involved are data transformations, a novel pre-processing
method based on enrichment of data through the inducing of
temporal factors and a genetic algorithms wrapper that optimises all the variables in our approach. The created model was
tested on a short-term load forecasting problem, with the use
of data from single and combined months, regarding real data
from Wikipedia servers. The learning methods used for creating the different models were linear regression, neural networks, and support vector machines. A basic dataset, as well as an
enriched dataset, were the core elements for the two scenarios
studied. Results show that it is possible to tune the dataset features, e.g., granularity and time window to improve prediction
results.
Descrição
Palavras-chave
Load Forecasting Linear Regression Artificial Neu ral Networks Support Vector Machines Server Load Prediction Wikipedia
Contexto Educativo
Citação
Silva, C.A., Grilo, C., & Silva, C. (2019). Model Optimisation for Server Loading Forecasting with Genetic Algorithms.