AgileDART: An Agile and Scalable Edge Stream Processing Engine
<|reference_start|>AgileDART: An Agile and Scalable Edge Stream Processing Engine: Edge applications generate a large influx of sensor data at massive scales. Under many time-critical scenarios, these massive data streams must be processed in a very short time to derive actionable intelligence. However, traditional data processing systems (e.g., stream processing systems, cloud-based IoT data processing systems) are not well-suited for these edge applications. This is because they often do not scale well with a large number of concurrent stream queries, do not support low-latency processing under limited edge computing resources, and do not adapt to the level of heterogeneity and dynamicity commonly present in edge computing environments. These gaps suggest a need for a new edge stream processing system that advances the stream processing paradigm to achieve efficiency and flexibility under the constraints presented by edge computing architectures.   We present AgileDart, an agile and scalable edge stream processing engine that enables fast stream processing of a large number of concurrently running low-latency edge applications' queries at scale in dynamic, heterogeneous edge environments. The novelty of our work lies in a dynamic dataflow abstraction that leverages distributed hash table (DHT) based peer-to-peer (P2P) overlay networks to automatically place, chain, and scale stream operators to reduce query latencies, adapt to workload variations, and recover from failures; and a bandit-based path planning model that can re-plan the data shuffling paths to adapt to unreliable and heterogeneous edge networks. We show analytically and empirically that AgileDart outperforms Storm and EdgeWise on query latency and significantly improves scalability and adaptability when processing a large number of real-world edge stream applications' queries.<|reference_end|>
@article{hu2024agiledart:,
    title={AgileDART: An Agile and Scalable Edge Stream Processing Engine},
    author={Liting Hu, Cheng-Wei Ching},
    journal={arXiv preprint arXiv:2407.14953},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14953},
    primaryClass={cs.DB cs.DC}
}