Skip to main content

Machine Learning Driven Latency Optimization for Internet of Things Applications in Edge Computing.

Awada, U., Zhang, J., Chen, S., Li, S. and Yang, S., 2023. Machine Learning Driven Latency Optimization for Internet of Things Applications in Edge Computing. ZTE Communications, 21 (2), 40-52.

Full text available as:

[img]
Preview
PDF (OPEN ACCESS ARTICLE)
Machine Learning Driven Latency Optimization for Internet of Things Applications in Edge Computing.pdf - Published Version

2MB
[img] PDF
ZTE Communications.pdf - Accepted Version
Restricted to Repository staff only
Available under License Creative Commons Attribution Non-commercial.

2MB

Official URL: https://www.zte.com.cn/global/about/magazine/zte-c...

DOI: 10.12142/ZTECOM.202302007

Abstract

Emerging Internet of Things (IoT) applications require faster execution time and response time to achieve optimal performance. However, most IoT devices have limited or no computing capability to achieve such stringent application requirements. To this end, computation offloading in edge computing has been used for IoT systems to achieve the desired performance. Nevertheless, randomly offloading applications to any available edge without considering their resource demands, inter-application dependencies and edge resource availability may eventually result in execution delay and performance degradation. We introduce Edge-IoT, a machine learning-enabled orchestration framework in this paper, which utilizes the states of edge resources and application resource requirements to facilitate a resource-aware offloading scheme for minimizing the average latency. We further propose a variant bin-packing optimization model

Item Type:Article
ISSN:1673-5188
Uncontrolled Keywords:Edge computing; Execution time; IoT; Machine learning; Resource efficiency
Group:Faculty of Science & Technology
ID Code:38647
Deposited By: Symplectic RT2
Deposited On:06 Jun 2023 12:05
Last Modified:24 May 2024 10:30

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -