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We present a framework for performance optimization in serverless edge-cloudplatforms using dynamic task placement. We focus on applications for smart edgedevices, for example, smart cameras or speakers, that need to performprocessing tasks on input data in real to near-real time. Our framework allowsthe user to specify cost and latency requirements for each application task,and for each input, it determines whether to execute the task on the edgedevice or in the cloud. Further, for cloud executions, the framework identifiesthe container resource configuration needed to satisfy the performance goals.We have evaluated our framework in simulation using measurements collected fromserverless applications in AWS Lambda and AWS Greengrass. In addition, we haveimplemented a prototype of our framework that runs in these same platforms. Inexperiments with our prototype, our models can predict average end-to-endlatency with less than 6% error, and we obtain almost three orders of magnitudereduction in end-to-end latency compared to edge-only execution.

Performance Optimization for Edge-Cloud Serverless Platforms via Dynamic Task Placement