R-MStorm: A Resilient Mobile Stream Processing System for Dynamic Edge Networks

Mobile Stream Processing (MSP) provides a promising approach to run computation-intensive stream applications, e.g., video face recognition, on a cluster of mobile devices at the edge. However, the performance of MSP is severely restricted by the fluctuating bandwidth and intermittent connectivity of the wireless networks connecting those devices. Therefore, to achieve a good MSP performance, implementing a resilient MSP system that adapts to dynamic edge networks is essential. In this paper, we present R-MStorm, a resilient MSP system deployed at the edge. R-MStorm improves the system survivability by (1) assigning tasks to mobile devices with higher availability to improve the availability of whole system; (2) assigning tasks of the same application components to different devices to increase the diversity of physical stream paths. Besides, to efficiently divide the output of upstream tasks to downstream tasks, R-MStorm adopts adaptive stream grouping, which considers both the transmission rate to and processing rate at each downstream task. Moreover, to alleviate congestion caused by network disconnection and stream redirection, adaptive stream selection is applied to skip some data to achieve a short response time. We conduct extensive experiments on R-MStorm by executing a video face recognition App under different network conditions. The experimental results show that, compared with baseline approaches, R-MStorm achieves up to 1.5x higher throughput, 75% lower response time, at a cost of 3.3% accuracy loss.