@inproceedings{73deea2e48864a0dbb91b8b34b2f7c8a,
title = "Collaborative Edge Service Placement for Maximizing QoS with Distributed Data Cleaning",
abstract = "The proliferation of dirty data on Internet of Things (IoT) devices can undermine the accuracy of data-driven decision-making by affecting the distribution of original data. The Quality of Service (QoS) of data cleaning on these devices is heavily impacted by processing delay and accuracy. In this paper, we find that edge service placement is a key step aligned with data cleaning and consider the collaborative edge service placement with distributed data cleaning (SPDC) problem. To address this issue, we propose a novel distributed collaborative edge-based architecture that effectively balances the demands of storage, communication, computation, and load constraints. Experimental results show that the proposed approach significantly improves the accuracy of data cleaning by 0.31%-86.07% and reduces delay by 2.73%-58.71% compared to state-of-the-art baselines.",
keywords = "collaborative edge computing, data cleaning, QoS, Service placement",
author = "Yuzhu Liang and Wenhua Wang and Xi Zheng and Qin Liu and Liang Wang and Tian Wang",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023 ; Conference date: 19-06-2023 Through 21-06-2023",
year = "2023",
doi = "10.1109/IWQoS57198.2023.10188694",
language = "英语",
series = "IEEE International Workshop on Quality of Service, IWQoS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE/ACM 31st International Symposium on Quality of Service, IWQoS 2023",
}