Mobile edge computing (MEC) is an emerging platform for supporting rapidly growing low-latency mobile applications and Internet of Things (IoT) systems. It involves deploying nodes with computing, storage, and communication capabilities at the edge of mobile networks and utilizing client mobile devices for computing and data processing services near the end users. This project addresses the challenges of resource allocation and task assignment in MEC systems, enabling horizontal and vertical cooperation among edge nodes, remote cloud data centers, and mobile users for performance optimization of multiple services with different quality of service (QoS) requirements. Specifically, a stochastic framework for cooperative resource allocation and task assignment in MEC systems is designed using deep learning, stochastic game, and optimization techniques. The research agenda includes several synergistic thrusts. First, the complex interactions among the various entities of an MEC system will be investigated, new optimization problems will be formulated, and novel centralized and distributed algorithms will be developed. These algorithms enable a service provider to optimally orchestrate its computational tasks under stochastic and time-varying network states and task arrivals. Lightweight heuristic algorithms will also be designed and compared with the optimal solutions. Second, network slicing and resource allocation schemes will be developed so as to allow multiple service providers to dynamically share a virtualized heterogeneous MEC network infrastructure and jointly optimize resource utilization and task assignment for different services. Third, an experimental testbed will be instrumented and used to validate through proof-of-concept prototyping the algorithms developed under this project.
The MEC solutions developed in this project are expected to significantly enhance user experience for many emerging low-latency mobile services such as intelligent transportation and smart cities. The research outcomes, coupled with industry collaboration, will provide fresh ideas for technology transfer and will impact related industry standards. The proposed research is fully integrated into the educational plan to train undergraduate and graduate students. The project will also broaden participation of underrepresented groups by reaching out to various organizations. Project outcomes, which include technical reports, presentations, results, datasets, software, and other artifacts, will be made available online. A data repository will be maintained during the course of this project and for at least five additional years after its completion.
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.
