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Edge computing research in IMCL can be summarized in two directions:

  • Collaborative Scheduling for Edge Computing: We study and propose scheduling solutions for different resource management problems in edge computing including task partitioning and offloading, data dissemination, etc. We are also working on developing EdgeOS service to support collaborative scheduling for jointly managing resources in the distributed edge computing environments.
  • Distributed Edge Intelligence for AI-empowered Applications: On top of EdgeOS, we develop solutions for distributed training and inference at the resource-constraint edge devices. We are also developing a real prototype to test the efficacy of our proposed solutions for applications like collaborative real-time video surveillance.


Collaborative Scheduling for Edge Computing

Edge Learning as a Service for AI-empowered Applications