TY  - JOUR
AU  - Akter, Thabo
AU  - Alam, Imran
AU  - Sato, Ayesha
AU  - Park, Reza
TI  - Reinforcement Learning for Cloud Resource Scheduling
JO  - Open Access Journal of Engineering Applications
PY  - 2019
VL  - 43
IS  - 2
SP  - 611
EP  - 625
AB  - 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.
KW  - cloud resource scheduling
KW  - X-ray diffraction characterisation
KW  - induction motor
KW  - composite material
KW  - blockchain
SN  - 3105-7004
DO  - 10.55555/oajea.2019.4302.030
UR  - https://journal.bsaps.com/article/9647
ER  -