harshaljanjani/taskschedulingdqn
Designing energy-aware scheduling and task allocation algorithms for online reinforcement learning in cloud environments (IEEE Transactions on Computational Social Systems).
This project helps optimize how computing tasks are assigned and scheduled in cloud environments to reduce energy consumption. It takes in information about incoming tasks and available cloud resources, then outputs an energy-efficient plan for task allocation and scheduling. Cloud operations managers, data center administrators, or anyone managing large-scale cloud infrastructure will find this useful for 'green computing' initiatives.
Use this if you need to dynamically allocate and schedule machine learning or other demanding tasks across cloud servers while significantly minimizing energy usage.
Not ideal if your primary concern is latency or cost without specific emphasis on energy efficiency, or if you are not operating within a cloud environment.
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Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Mar 13, 2026
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