Shepherd: Seamless Stream Processing on the Edge

Brian Ramprasad, Pritish Mishra, Myles Thiessen, Hongkai Chen, Alexandre da Silva Veith, Moshe Gabel, Oana Balmau, Abelard Chow, Eyal de Lara

IEEE/ACM 7th Symposium on Edge Computing (SEC), Seattle, WA, December 2022



Next generation applications such as augmented/vir-tual reality, autonomous driving, and Industry 4.0, have tight latency constraints and produce large amounts of data. To address the real-time nature and high bandwidth usage of new applications, edge computing provides an extension to the cloud infrastructure through a hierarchy of datacenters located between the edge devices and the cloud. Outside of the cloud and closer to the edge, the network becomes more dynamic requiring stream processing frameworks to adapt more frequently. Cloud based frameworks adapt very slowly because they employ a stop-the-world approach and it can take several minutes to reconfigure jobs resulting in downtime. In this paper, we propose Shepherd, a new stream processing framework for edge computing. Shepherd minimizes downtime during application reconfiguration, with almost no impact on data processing latency. Our experiments show that, compared to Apache Storm, Shepherd reduces application downtime from several minutes to a few tens of milliseconds.