Serverless edge computing combines the advantages of the well-known serverless computing paradigm with the geographic distribution of the computational infrastructure. Distributed locations of heterogeneous edge servers constitute a multi-cloud continuum, enabling the workload distribution across different cloud environments to leverage the unique capabilities and offerings. It also reduces delays in real-time data processing, the amount of data sent to computational clouds, contextual operation and efficient on-demand resource utilization.

Despite the overall benefits, serverless edge computing is particularly vulnerable to the dynamics of the operating environment due to the usage of renewable energy sources, lack of adequate cooling, limited servicing and low-power wireless communication technologies. Therefore, the research challenge is to make these solutions adaptive to ever-changing working conditions and provide the desired quality of service. A promising solution involves the usage of ML and TinyML to provide adaptive intelligence to the computational infrastructure. In the talk, we will discuss the open problems and possible research directions.