Few suggestions:

1. **Optimal virtual machine placement**: Investigate machine learning techniques to optimize the placement of virtual machines (VMs) in an edge computing environment. The goal is to dynamically allocate VMs to edge devices in a way that minimizes energy consumption while meeting the application's performance requirements.

2. **Resource allocation and scheduling**: Develop resource allocation and scheduling algorithms that leverage machine learning to intelligently allocate computational resources at the edge. This involves dynamically adjusting the allocation of CPU, memory, and network resources to minimize energy consumption while maintaining quality of service.

3. **Predictive workload management**: Explore machine learning models to predict the workload patterns in an edge computing environment. By accurately forecasting the future workload, it becomes possible to proactively optimize VM placement and resource allocation to maximize energy efficiency.

4. **Energy-aware task offloading**: Investigate machine learning techniques for determining the optimal task offloading strategy in edge computing. This involves deciding whether to process a task locally on the edge device or offload it to a remote cloud server, taking into consideration energy consumption, network latency, and other relevant factors.

5. **Dynamic power management**: Explore machine learning-based approaches for dynamically managing the power states of edge devices. By analyzing workload patterns and user behavior, intelligent power management strategies can be developed to adjust the power consumption of edge devices based on demand, leading to improved energy efficiency.

6. **Edge device selection**: Investigate machine learning algorithms to assist in the selection of energy-efficient edge devices for deploying specific workloads. By considering factors such as device capabilities, energy profiles, and network conditions, machine learning models can help identify the most suitable edge devices for specific tasks.

Good luck!!

Mohammad Imam, thank you very much brother, may the good God continue to bless you.

Dear Andrew Ramarumo,

the idea of Digital Twins (DTs) in the IoT is well suited for using Machine Learning – i.e. with the help of Artificial Intelligence – to achieve the best possible energy efficiency and also the optimal placement of virtual machines in Edge Computing. So I would suggest exploring the idea of DTs for these purposes. My literature sources serve to provide you with first aid.

For a general idea of DTs, see my presentation at address:

https://www.researchgate.net/publication/366167747_Digital_Twins_in_IoT

For a general presentation of the idea of power savings in computerized systems, see my answers:

https://www.researchgate.net/post/How_can_we_design_a_communication_system_that_is_energy_efficient

https://www.researchgate.net/post/How-can-I-calculate-using-of-energy-by-WSN-node

Best regards

Anatol Badach

Gernot Steindl, Martin Stagl, Lukas Kasper, Wolfgang Kastner, Rene Hofmann: Twin Architecture for Industrial Energy Systems; Applied Sciences, Vol. 10, Issue 24, Dec 2020, DOI: 10.3390/app10248903

https://www.mdpi.com/2076-3417/10/24/8903

Abiodun E. Onile, Ram Machlev, Eduard Petlenkov, Yoash Levron, Juri Belikov: “Uses of the digital twins concept for energy services, intelligent recommendation systems, and demand side management: A review”; Energy Reports, Vol. 7, Jan 2021; DOI: 10.1016/j.egyr.2021.01.090

https://www.sciencedirect.com/science/article/pii/S2352484721000913

Sin Yong Teng, Michal Touš, Wei Dong Leong, Bing Shen How, Hon Loong Lam, Vítězslav Máša: “Recent advances on industrial data-driven energy savings: Digital twins and infrastructures”; Renewable and Sustainable Energy Reviews; Vol. 135, Jan 2021, DOI: 10.1016/j.rser.2020.110208

https://www.sciencedirect.com/science/article/pii/S1364032120304974

Abiodun Onile, Ram Machlev, Eduard Petlenkov, Yoash Levron, Juri Belikov: „Uses of the digital twins concept for energy services, intelligent recommendation systems, and demand side management: A review“; Energy Reports, Vol. 7, Nov 2021, DOI: 10.1016/j.egyr.2021.01.090

https://www.sciencedirect.com/science/article/pii/S2352484721000913?via%3Dihub

P.Tugarinov, F.Truckenmüller, B.Nold: DIGITAL TWIN OF DISTRIBUTED ENERGY DEVICES, Reutlingen University, Germany

https://core.ac.uk/download/pdf/288815796.pdf