EsratMaria/Reinforcement-Learning_for_Energy_Minimization_Using_CLoudsim
Implementation of RL in the cloud for energy minimization due to migration and excess power consumption.
This project helps data center managers reduce electricity costs by intelligently managing server migrations and power use. It takes your typical server request patterns and outputs optimized schedules for virtual machine placement to minimize energy consumption. Cloud infrastructure engineers and data center operators who want to lower their operational expenses would find this useful.
No commits in the last 6 months.
Use this if you manage a cloud data center and want to apply reinforcement learning to automatically optimize server energy usage based on incoming workload demands.
Not ideal if you are looking for a plug-and-play solution for live production systems, as this is a simulation-based research implementation.
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HTML
License
LGPL-3.0
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Last pushed
Feb 20, 2024
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