Live Adaptation of Dataflow Stream Processing for Hybrid Edge-Cloud Analytics
Yuanli Wang, Prateek Jain, Lei Huang, Le Xu, Vasiliki Kalavri
Dataflow streaming systems, like Apache Flink and Spark Streaming, are integral to modern business analytics, providing real-time insights across industries. However, for emerging hybrid edge-cloud analytics pipelines, neither processing everything in the cloud nor static edge offloading can reliably sustain the target rate under dynamic WAN conditions and limited edge resources. In this paper, we explore how these datacenter-centric platforms can be extended to support such hybrid analytics pipelines. Our goal is to maintain full transparency for user applications, while retaining built-in fault tolerance, out-of-order processing, and existing ecosystem integrations. Achieving this is challenging because existing dataflow engines assume homogeneous resources, rely on static task placement, and do not support adaptation under dynamic network and edge conditions. We introduce Sluice, an adaptive data stream processing system for hybrid clouds, compatible with dataflow streaming engines. Sluice's key contributions are: (i) an automatic query rewriter that decomposes applications into independent, fault-tolerant segments, and (ii) a live routing mechanism that shifts computations between edge and cloud without pausing execution while preserving event-time semantics. We implement Sluice on Apache Flink and evaluate it on a physical testbed with Raspberry Pi devices, as well as on a large simulated testbed of 100 VMs. Across five real-world applications, our results show that Sluice seamlessly shifts computations between edge and cloud, while consistently achieving higher throughput than baseline systems.
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