A Resource Provisioning Framework with Adaptive Task Distribution for Edge Devices

Youngwoo Jang, Soonbeom Kwon, Illyoung Choi, Dukyun Nam, Byungchul Tak, Young-Kyoon Suh

Abstract: Edge devices such as wearables, drones, and CCTV systems have been widely deployed to collect real-world data, playing a crucial role in enhancing and securing urban life. However, these devices often struggle with significant performance challenges due to their limited computational and storage capacities when processing data locally. Offloading computation and data to the public cloud is straightforward but introduces high costs and latency. Alternatively, relying on an edge server to support a diverse array of heterogeneous edge devices can standardize operations. Still, it may lead to underutilization of high-performance devices such as Jetson Xavier if all tasks are centralized on the server. To address these concerns, we introduce ERPF, an edge resource provisioner that virtually extends edge devices' computation and storage capabilities, enabling them to handle complex tasks beyond their capacity. ERPF supports dynamic volume provisioning, GPU provisioning, and online execution context migration. Also, we propose a novel technique (ATS) that schedules AI workloads on distributed edge devices and edge servers with adjustment of task partition sizes based on the computational and network performance of the edge devices. ATS is seamlessly integrated into the ERPF prototype, which is implemented on a Kubernetes cluster using the Rook-Ceph storage orchestrator. Experimental results show that ERPF efficiently scales resources for edge devices through strategic offloading, while ATS delivers a substantial performance improvement of up to 23 × compared to baseline methods.

External IDs:dblp:conf/noms/JangKCNTS25