Improving IoT Analytics through Selective Edge Execution
| Autor: | Argyrios G. Tasiopoulos, Theodoros Salonidis, George Iosifidis, Douglas J. Leith, Apostolos Galanopoulos |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Networking and Internet Architecture (cs.NI)
FOS: Computer and information sciences Service (systems architecture) Computer Science - Machine Learning business.industry Computer science Distributed computing 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology 01 natural sciences Machine Learning (cs.LG) 0104 chemical sciences Outsourcing Computer Science - Networking and Internet Architecture Analytics Server Leverage (statistics) Cloudlet Enhanced Data Rates for GSM Evolution Performance improvement 0210 nano-technology business |
| Zdroj: | ICC |
| 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. 7 pages, 6 figures. This paper has been accepted for publication in the Proceedings of IEEE International Conference on Communications (ICC) 2020 |
| Databáze: | OpenAIRE |
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