Implementing machine learning for optimized resource allocation in cloud computing environments

doi:10.47974/jios-2270

Implementing machine learning for optimized resource allocation in cloud computing environments

2026-05-01

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Chaudhari, Leena Bharat

DOI：10.47974/jios-2270

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Self-driving cars need to learn how to drive in places that change all the time, like when the weather changes, the traffic patterns change, or the noise from sensors changes. RDS makes guidance more reliable and useful, which helps these cars get ready for and deal with these kinds of unknowns. These methods help self-driving cars get ready for problems, change their routes on the fly, and keep working well even when things go wrong. Compared to traditional methods, the results show improvements of up to 25% in utilisation, similar to 70% reduction in SLA violations, 23% lower energy consumption, and 24% lower cost.

Keywords:

Cloud computing

Resource allocation

Machine learning

LSTM

Reinforcement learning

Hybrid framework

SLA compliance

Energy efficiency

Cost optimization

Implementing machine learning for optimized resource allocation in cloud computing environments

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