EdgeBatch: Towards AI-Empowered Optimal Task Batching in Intelligent Edge Systems

Modern Internet of Things (IoT) systems are increasingly leveraging deep neural networks (DNNs) with the goal of enabling intelligence at the edge of the network. While applying DNNs can greatly improve the accuracy of autonomous decisions and inferences, a significant challenge is that DNNs are traditionally designed and developed for advanced hardware (e.g., GPU clusters) and can not easily meet the real time requirements when deployed in a resource-constrained edge computing environment. While many systems have been proposed to facilitate deep learning at the edge, a key limitation lies in the under-utilization of the parallelizable GPU resources of edge nodes (e.g., IoT devices). In this paper, we propose EdgeBatch, a collaborative intelligent edge computing framework that minimizes the delay and energy consumption of executing DNN tasks at the edge by sharing idle GPU resources among privately owned IoT devices. EdgeBatch develops 1) a stochastic task batching mechanism that identifies the optimal batching strategy for the GPUs on IoT devices given uncertain task arrival times, and 2) a dynamic task offloading scheme that coordinates the collaboration among edge nodes to optimize the utilization of idle GPU resources in the system. We implemented EdgeBatch on a real-world edge computing testbed that consists of heterogeneous IoT devices (Jetson TX2, TX1, TK1, and Raspberry Pi3s). The results show that EdgeBatch achieved significant performance gains in terms of both the end-to-end delay and energy savings compared to the state-of-the-art baselines.