Improving IoT Analytics through Selective Edge Execution

Autor:	Galanopoulos, A., Tasiopoulos, A. G., Iosifidis, G., Salonidis, T., Leith, D. J.
Rok vydání:	2020
Předmět:	Computer Science - Networking and Internet Architecture Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	A large number of emerging IoT applications rely on machine learning routines for analyzing data. Executing such tasks at the user devices improves response time and economizes network resources. However, due to power and computing limitations, the devices often cannot support such resource-intensive routines and fail to accurately execute the analytics. In this work, we propose to improve the performance of analytics by leveraging edge infrastructure. We devise an algorithm that enables the IoT devices to execute their routines locally; and then outsource them to cloudlet servers, only if they predict they will gain a significant performance improvement. It uses an approximate dual subgradient method, making minimal assumptions about the statistical properties of the system's parameters. Our analysis demonstrates that our proposed algorithm can intelligently leverage the cloudlet, adapting to the service requirements. Comment: 7 pages, 6 figures. This paper has been accepted for publication in the Proceedings of IEEE International Conference on Communications (ICC) 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2003.03588 Zobrazit plný text záznamu View this record from Arxiv