Rescheduling serverless workloads across the cloud-to-edge continuum

Serverless computing was a breakthrough in Cloud computing due to its high elasticity capabilities and finegrained pay -per -use model offered by the main public Cloud providers. Meanwhile, open -source serverless platforms supporting the FaaS (Function as a Service) model allow users to take advantage of many of their benefits while operating on the on -premises platforms of organizations. This opens the possibility to deploy and exploit them on the different layers of the cloud -to -edge continuum, either on IoT (Internet of Things) devices located at the Edge (i.e. next to data acquisition devices), in on -premises clusters closer to the data sources (i.e. Fog computing) or directly on the Cloud. This paper presents two strategies to mitigate the overload that disparate data ingestion rates may cause in low -powered devices at the Edge or Fog layers. To this end, it is proposed to delegate and reschedule serverless jobs between the different layers of the cloud -to -edge continuum using an open -source platform for event -driven file processing. To demonstrate the performance of these strategies, a use case for fire detection is proposed that includes processing in the Fog via minified Kubernetes clusters located near the Edge, in the private Cloud via on -premises elastic clusters and, finally, in the public Cloud by using the AWS (Amazon Web Services) Lambda FaaS service. The results indicate that these strategies can mitigate overloads in use cases involving processing across the cloud -to -edge continuum by coordinating several layers of computing resources.