CAREER: Orchestrating Edge Infrastructures and Mobile Devices under Uncertainty to Provision Edge AI as a Service

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Artificial Intelligence (AI) is often trained by data first, and then used to perform inference upon data which may have never been seen. Today's AI services are mostly trained in remote data centers and accessed via the Internet. However, increasingly, as large volumes of training data are generated by end users and inference tasks are also performed on premises, AI needs to be moved to the network edge in closer proximity to users. The objective of this project is to address this emerging paradigm shift by orchestrating the distributed computing and networking infrastructures and provisioning AI as a service from the network edge to serve large numbers of users at different locations.This project will define the optimization problems, design the control algorithms, and develop the deployable implementations for operating AI as a service across cloud-edge networks and mobile devices. Centered on system dynamics and uncertainty, the project consists of multiple research thrusts. First, the project will optimize the training and the inference of AI through making smart decisions on participant selection, model placement, aggregation control, and request dispatching, and will produce time-based algorithms running in an online manner with provable performance guarantees. Second, the project will investigate the interactions among different parties in the AI service ecosystem from an economics perspective, and will devise incentive mechanisms towards superior resource utilization and optimal social welfare with desired economic properties. Third, the project will conduct a combination of simulations, measurements, and implementations with real-world data traces and realistic experimental settings to comprehensively evaluate and validate the models, algorithms, and systems.This project will impact the industry by providing telecom carriers, network operators, and service providers with a critical set of techniques to enable them to provision and operate dedicated or value-added edge-AI services upon distributed infrastructures. Further, the project will devise novel theories and algorithms based on the mathematics of optimization, control, learning, and mechanism design, and could be of independent interest and have extended applications to problems in other related fields that also face dynamic and uncertain inputs. Finally, besides contributing to undergraduate and graduate course materials, the project will execute the education plan that focuses on facilitating K-12 education via training teachers and also equipping students with AI knowledge and experiences in an ethical manner.The deliverables of this project, which include but are not limited to papers, data, and codes, will be made publicly available at the following website: https://github.com/ai-at-edge. This website will be updated in time for the duration of this project and maintained online thereafter.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.