Intelligent Workload Readjustment of Serverless Functions in Cloud to Edge Environment

Serverless technologies have represented a significant advancement in cloud computing, characterized by its exceptional scalability and the granular subscription-based model provided by leading public cloud vendors. Concurrently, serverless platforms that facilitate the FaaS architecture enable users to use numerous benefits while functioning on the on-site infrastructures of enterprises. It makes it possible to install and use them on several tiers of the cloud-to-edge continuum, from IoT devices at the user end to on-site clusters near to the main sources or directly in the Cloud. The challenges caused by varying data input rates on low-powered gadgets at the user-end layers are addressed in this work in two ways. It offers an event-driven, open-source file handling system designed to dynamically distribute and rearrange serverless operations throughout the cloud-to-edge spectrum. A fire detection use case illustrates the efficacy of these techniques, utilizing small Kubernetes clusters at the Edge for Fog-level processing, on-premises elastic clusters for private cloud computing, and AWS Lambda for cloud computing execution. Findings demonstrate that coordinated multi-layer computing markedly diminishes system overload, hence improving performance in distributed cloud systems.