Incremental Stream Query Deployment under Continuous Infrastructure Changes in the Cloud-Edge Continuum
Distributed data stream processing engines (DSPEs) operating over the cloud-edge continuum must deploy data processing operators across a distributed infrastructure. However, the volatile nature of these infrastructure nodes—where devices frequently join, leave, or move—caninvalidate existing query operator-to-topology node mappings, leading to interruptions in query execution and potential data loss. To ensure continuous processing while maintaining correctness, DSPEs must dynamically adapt these mappings and redeploy (part of) a ected queries. In this paper, we introduce incremental stream query deployment (ISQD), a framework that efficiently redeploys queries affected by topology changes. ISQD employs a greedy strategy to identify and redeploy only affected operators. It uses ad-hoc queries to migrate operator state seamlessly, and leverages reconfiguration markers to synchronize the redeployment process. Our evaluation shows that ISQD achieves up to 7.5× lower deployment latency and up to 39× lower event time latency compared to state-of-the-art approaches, even under high-frequency topology changes.
