Online Resource Provisioning and Batch Scheduling for AIoT Inference Serving in an XPU Edge Cloud

Abstract

Driven by the accelerated convergence of artificial intelligence (AI) and the Internet of Things (IoT), the recent years have witnessed the booming of Artificial Intelligence of Things (AIoT). Edge clouds place computing and service capabilities at the network edges to reduce network transmission overhead, which has been widely recognized as the critical infrastructure for AIoT applications. Meanwhile, to accelerate computation-intensive edge cloud AI operations, specialized AI accelerators such as GPU, NPU, and TPU have been increasingly integrated into edge clouds. For such emerging XPU edge clouds, utilizing costly XPUs more efficiently has become a significant challenge. In this paper, we present an online optimization framework for joint resource provisioning and batch scheduling for more cost-efficient AIoT inference serving in an XPU edge cloud. The essential optimization process for the online framework is to first adaptively batch inference tasks to increase the system throughput without compromising the service level agreement (SLA). Next, heterogeneous XPU resources are provisioned for the batches. Finally, the resource instance is consolidated to a minimum of physical servers. Via extensive trace-driven simulations, we verify the performance of the presented online optimization framework.