Collaborative edge service placement for maximizing QoS with distributed data cleaning

Yuzhu Liang*, Wenhua Wang, Xi Zheng, Qin Liu, Liang Wang, Tian Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

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.

Original languageEnglish
Title of host publication2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)9798350399738
ISBN (Print)9798350399745
DOIs
Publication statusPublished - 2023
Event31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023 - Orlando, United States
Duration: 19 Jun 202321 Jun 2023

Publication series

Name
ISSN (Print)1548-615X
ISSN (Electronic)2766-8568

Conference

Conference31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023
Country/TerritoryUnited States
CityOrlando
Period19/06/2321/06/23

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