MICRAST: Micro-Forecasting Approach for Cloud User Consumption Pattern Based on RNN

One vital key for effective management of cloud resources is the ability to predict their users’ consumption’s patterns in granular level. It can provide more insightful analysis to guide these users towards more resource-effective habits. Such prediction requires pre-processing the users’ traces from these cloud resources for granular prediction (micro-prediction). However, the methodology followed by many forecasting based cloud studies was designed to deal with these traces as over-all trends (macro-prediction). We propose a (MICRAST) that generates segments of granular patterns. Then, it carries out parallel tasks of pre-processing and training that lead to separate trained network for each of these segments. To select a model for our approach, we compared methods from two forecasting categories: statistical and artificial neural network (ANN)-based. The results lead us to recurrent neural networks (RNN). We evaluated the MICRAST through a comparison with related work methodologies (macro-prediction approach) for both univariate and multi-variate forecasting. Then, we measured its confidence for forecasting up to 20% of the training time steps. The results showed that our approach can forecast the preferences of each cloud user with a confidence level of between (95% to 98%) surpassing related works by more than 70%.

@article{Ali2025,
  title     = {MICRAST: Micro-Forecasting Approach for Cloud User Consumption Pattern Based on RNN},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Shallaw Mohammed Ali and Gabor Kecskemeti},
  doi       = {10.14569/IJACSA.2025.0160584},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160584}
}