Performance Analysis of Renewable energy scheduling in geographical data center
Implementation Plan:
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Step 1: Initially, we generate Synthetic Data and perform energy forecasting using LSTM.
Step 2: Then, we classify and schedule the work load using Enhanced Earliest Deadline First (E-EDF) scheduling algorithm.
Step 3: Next, we implement Deep Multi-Agent Reinforcement Learning (MARL) for Decision Making.
Step 4: Next, we monitor the heat generation then distribute it into volts using Linear Programming (LP) optimization model based on collected data.
Step 5: Finally, we plot performance for the following metrics:
5.1: Time Vs. Energy utilization (%)
5.2: Time Vs. Task execution success rate (%)
5.3: Time Vs. Heat reuse efficiency (%)
5.4: Time Vs. Thermal distribution loss (%)
5.5: Time Vs. Average task waiting time (sec)
Software requirement:
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1. Development Tool: Python 3.11.4 or above
2. Operating System: Windows 10 (64-bit) or above
Note:
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1) If the plan does not meet your requirements, provide detailed steps, parameters, models, or expected results in advance. Once implemented, changes won’t be possible without prior input; otherwise, we’ll proceed as per our implementation plan.
2) If the plan satisfies your requirement, Please confirm with us.
3) Project based on Simulation only, not a real time project.
4) If you have any changes in the Dataset , kindly provide before implementation. Our work is completely based on dataset values.
5) Please understand that any modifications made to the confirmed implementation plan will not be made after the project development.
We perform with an Existing Approach Ref:1 Title:- Multi-Agent Deep Reinforcement Learning Framework for Renewable Energy-Aware Workflow Scheduling on Distributed Cloud Data Centers
