Internet-of-Things (IoT) applications such as self-driving cars, augmented reality, interactive gaming, and event monitoring have a tremendous potential to improve our lives. These 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. This CAREER project aims to support time-critical IoT applications by applying the stream processing paradigm to the Edge computing architecture in the dynamic, heterogeneous Edge environment. As an integral part of its research program, this CAREER project involves K-12, undergraduate and graduate level education in partnership with the local Public School system.
This project includes three primary research directions. (1) A new dynamic dataflow graph abstraction is proposed, which automatically chains, parallelizes and replicates stream operators to adapt to the Edge dynamics. (2) A new customizable data shuffling service abstraction is proposed, which customizes the data shuffling path (e.g., ring shuffle, hierarchical tree shuffle, butterfly wrap shuffle) at runtime for given network topology and workload. (3) A fully decentralized architecture with many distributed schedulers is proposed, in which each scheduler operates autonomously to process IoT queries. All three parts of the project will be prototyped and implemented on real-world stream processing systems and validated by performing real-world experiments.
Principal Investigator
Members
Dr. Liting Hu, DART: A Scalable and Adaptive Edge Stream Processing Engine
USXNIX ATC’21
[Slides]
This material is based upon work supported by the National Science Foundation CAREER award NSF-CAREER-23313737.