Nathan Vance, Md. Tahmid Rashid, D. Zhang, Dong Wang
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Towards Reliability in Online High-Churn Edge Computing: A Deviceless Pipelining Approach
Social sensing based Edge Computing (SSEC) is an emerging application paradigm for rich context awareness in sensing applications in which people collaborate in both data acquisition and processing at the edge of the network. While keeping people in the loop can be an immense benefit, the unreliability and churn introduced can pose a dangerous stability threat for long-running SSEC applications. In the past this problem was addressed by offloading these responsibilities to more reliable server hardware in the cloud or a fog layer, however, this does not take full advantage of the power at the edge. In this paper we address the issue of reliable edge computing on dynamic high-churn edge systems. We develop a deviceless pipeline based approach (DPA) to establish workflows in which stages of the analysis pipeline are completed on edge devices, and any devices that leave the system can be replaced without data loss. We evaluate the performance of our system on a real-world edge computing system performing an object detection application and demonstrate that it can provide significant performance gains over traditional computation offloading schemes in terms of throughput and error recovery.