Energy Efficient Resorce Allocation in Mobile Edge Computing
Title:- An intelligent Bin Packing Approach based energy efficient allocation for mobile edge computing
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Implementation Plan:
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Step 1: Initially we construct a network consisting of 50-Mobile Devices, 2-Edge Servers.
Step 2: Then, we collect the data from the network in the form of packets and preprocessing is done using PCA SGD technique.
Step 3: Next, we implement decision making using Hybrid Heuristic Deep MCF method.
Step 4: Then, we balance the objective to improve the decision making performance using the Multi-objective Evolutionary algorithm (MOE).
Step 5: Next, we allocate the energy resources using the Low Complexity WOA Resource Allocation (LC-WOA) to reduce the energy levels.
Step 6: Then, we implement the Bandit learning mechanism to interpret the feedback of the energy allocation.
Step 7: Finally, Performance Metrics will be plotted for the following:
7.1: Number of Devices vs Energy efficiency
7.2: Number of Devices vs Scalability
7.3: Number of Devices vs Throughput
7.4: Number of Devices vs Cost efficiency
7.5: Time (s) vs CPU usage (%)
Software Requirement:
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1. Development Tool: Ns3.35 with Python
2. Development OS: Ubuntu 22.04 LTS
Note:
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1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
2) Please note that this implementation plan does not include any further steps after it is put into implementation.
3) This project is only based on simulations. Not a real time project.
4) If the above plan satisfies your requirement please confirm with us.
We perform the EXISTING process based on the Reference 1 Title: An efficient cloudlet scheduling via bin packing in cloud computing.
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#ResourceAllocation
#MobileEdgeComputing
#EfficientTechnology
#EdgeComputing
#SustainableEnergy
#SmartAllocation
#GreenTechnology
#EdgeComputingSolutions
#EfficientMobileDevices
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