Reducing End-to-End Latency of Trigger-Action IoT Programs on Containerized Edge Platforms

IoT rule engines are important middlewares that allow users to easily create custom trigger-action programs (TAPs) and interact with the physical world. Users expect their TAPs to give a timely response within a certain deadline. Existing works provide this support by boosting the process of trigger event identification. Many IoT rule engines now run in containerized environments, bringing about new challenges and opportunities. Prior solutions can no longer satisfy the need of mitigating the end-to-end latency of containerized TAPs. In this work, we propose EdgeRuler, which couples the IoT rule engine and the container runtime to assure the performance of latency-critical TAPs. To enable such capability, EdgeRuler precisely models the end-to-end latency by exploiting information from both the physical and the cyber world. EdgeRuler then enforces a deadline-aware life-cycle control and resource provision for meeting the TAP constraints in a lightweight and efficient way. We prototype and evaluate EdgeRuler on top of production-ready open-source components, which shows that EdgeRuler reduces the end-to-end latency by 28.6%-96.2% compared to existing scheduling algorithms and 68.4%-89.1% to that of the state-of-the-art IoT rule engines, incurring negligible runtime overhead.