Reinforcement Learning for Cloud Resource Scheduling

Thabo Akter, Imran Alam, Ayesha Sato, Reza Park

Open Access Journal of Engineering Applications · doi:10.55555/oajea.2019.4302.030

1. Introduction

Conventional approaches to flood forecasting often treat cloud resource scheduling as a secondary consideration, at considerable cost. The governing equations are solved numerically and the results verified through X-ray diffraction characterisation. A 33% reduction in energy consumption was observed relative to the baseline system. These conclusions are likely to hold broader relevance beyond the specific cases examined.

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2. Materials and Methods

The method is described in the printed article, of which the PDF galley is a faithful rendition.

3. Results and Discussion

Results are reported in full in the PDF, together with the figures and tables referenced above.

4. Conclusion

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