@inproceedings{4dabf065c0594a2b9c34c1fc7abf0814,
title = "ECSRL: A Learning-Based Scheduling Framework for AI Workloads in Heterogeneous Edge-Cloud Systems",
abstract = "Recent advances in both lightweight models and edge computing make it possible for inference tasks to be executed concurrently on resource-constrained edge devices. However, our preliminary experiments show that the execution of different lightweight models on edge devices may lead to a performance downgrade. In this paper, we propose a Learning-Based Scheduling Framework - -ECSRL, to optimize the latency and power consumption for those inference tasks running in heterogeneous Edge-Cloud systems.",
keywords = "Heterogeneous Edge Computing, Reinforcement Learning, Task Scheduling",
author = "Changyao Lin and Ziyang Zhang and Huan Li and Jie Liu",
note = "Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 19th ACM Conference on Embedded Networked Sensor Systems, SenSys 2021 ; Conference date: 15-11-2021 Through 17-11-2021",
year = "2021",
month = nov,
day = "15",
doi = "10.1145/3485730.3492886",
language = "英语",
series = "SenSys 2021 - Proceedings of the 2021 19th ACM Conference on Embedded Networked Sensor Systems",
publisher = "Association for Computing Machinery, Inc",
pages = "386--387",
booktitle = "SenSys 2021 - Proceedings of the 2021 19th ACM Conference on Embedded Networked Sensor Systems",
}