Falcon: Live Reconfiguration for Stateful Stream Processing on the Edge

Stream processing is an attractive paradigm for deploying applications in geo-distributed edge-cloud environments. However, the reverse economics of scale in edge networks and the movement of data sources between edges require the ability to dynamically reconfigure the deployment of stateful applications to adapt to workload variations and user mobility. Unfortunately, existing stream processing engines either do not support the reconfiguration of stateful operators or are ill-suited to edge-cloud environments since they stop application processing during reconfiguration or require costly duplication of application state. We propose Falcon, a new stream processing engine. At its core lies a live key migration approach to allow reconfiguration to occur with minimal disruption to processing, even across distant datacenters. Falcon supports the reconfiguration of stateful operators including different windowing approaches and source mobility across different edge regions. It scales gracefully with network latency, the number of datacenters, and the size and number of keys. Our evaluation in geo-distributed edge-cloud deployments shows that Falcon reduces the length of processing interruptions and their impact on latency by 2 to 4 orders of magnitude compared to the existing state-of-the-art frameworks such as Apache Flink, Trisk, and Meces.