Queec: QoE-aware Edge Computing for Complex IoT Event Processing under Dynamic Workloads

Many IoT applications have the requirements of conducting complex IoT events processing (e.g., speech recognition) which are hardly supported by low-end IoT devices due to limited resources. Most existing approaches enable complex IoT event processing on low-end IoT devices by statically allocating tasks to the edge or the cloud. In this paper, we present Queec, a QoE-aware edge computing system for complex IoT event processing under dynamic workloads. With Queec, the complex IoT event processing tasks that are relative computation-intensive for low-end IoT devices can be transparently offloaded to nearby edge nodes at runtime. We formulate the problem of scheduling multi-user tasks to multiple edge nodes as an optimization problem which minimizes the overall offloading latency of all tasks while avoiding the overloading problem. We implement Queec on low-end IoT devices, edge nodes and the cloud. We conduct extensive evaluations and the results show that Queec reduces 56.98% of the offloading latency on average compared with the state of art under dynamic workloads, while incurring acceptable overhead.