CRII: CSR: Federated Resource Management in Mobile Edge Computing

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Smart cities are urban areas that use Internet connected devices, called the Internet-of-Things (IoT), to collect and analyze large amounts of data to increase the productivity and efficiency of city resources. Smart home sensors and smartphones are examples of things used in the era of smart cities, each creating a tremendous amount of data that needs to be processed. Edge computing is an emerging technology used to process such large amounts of data in real-time. Edge computing also poses new resource management challenges due to its limited capacity. This project focuses on optimization of resource management in edge and cloud computing frameworks.This project proposes to use concepts of federated learning and reinforcement learning to provide optimal solutions for service placement and request scheduling in multi-tier edge and cloud computing frameworks. This project will focus on new algorithms and system design technologies to trade off the quality-of-service (QoS) and the cost in edge computing systems. Three main thrusts include: 1) finding theoretical bounds through mathematical formulation and algorithm design, 2) finding more practical and adaptive solutions through multi-agent reinforcement learning and the federated learning framework, and 3) comparison of practical solutions with theoretical bounds through numerical analysis and implementations on a mobile edge computing test-bed. This project will have impacts on the future IoT and smart cities applications by improving the cost of providing such applications while maintaining certain QoS levels for users. Algorithms and an optimization framework developed in this project will contribute to the research in design and development of scalable and reliable edge computing systems as building blocks of IoT applications. This research will be integrated into classroom teaching as a special topics graduate level course offered by the investigator. Specific outreach activities for middle school, high school and Berea College students aim to increase participation of underrepresented groups in computer science.The data produced by this project including codes, experimental results, videos of talks, and publications will be shared using the project page that will be maintained during the lifetime of this project. Upon finalizing the two-year term of the project, an archive of the project webpage and information will be digitally stored and made available upon request by any researcher or entity if the webpage is taken down. Link to the project repository: http://www.cs.uky.edu/~khamfroush/fed-mec.php. This project is jointly funded by the Computer Systems Research Program in the Division of Computer and Network Systems and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.