skip to main content
10.1145/3416010.3423234acmconferencesArticle/Chapter ViewAbstractPublication PagesmswimConference Proceedingsconference-collections
research-article

Maximizing the Quality of User Experience of Using Services in Edge Computing for Delay-Sensitive IoT Applications

Published: 16 November 2020 Publication History

Abstract

The Internet of Things (IoT) technology offers unprecedented opportunities to interconnect human beings. However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices prevents users from experiencing high efficiency and seamless user experience. To address these shortcomings, the integrated MEC with remote clouds is a promising platform, where edge-clouds (cloudlet) are co-located with wireless access points in the proximity of IoT devices, thus intensive-computation and sensing data from IoT devices can be offloaded to the MEC network for processing, and the service response latency can be significantly reduced. In this paper, we study delay-sensitive service provisioning in an MEC network for IoT applications. We first formulate two novel optimization problems, i.e., the total utility maximization problems under both static and dynamic offloading task request settings, with the aim to maximize the accumulative user satisfaction of using the services provided by the MEC. We then show that the defined problems are NP-hard. We instead devise efficient approximation and online algorithms with provable performance guarantees for the problems. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising.

References

[1]
Nasir Abbas, Yan Zhang, Amir Taherkordi, and Tor Skeie. Mobile edge computing: a survey. IEEE Internet of Things Journal, vol. 5, no. 1, pp. 450--465, 2018.
[2]
Reuven Cohen, Liran Katzir, and Danny Raz. An efficient approximation for the generalized assignment problem. Information Processing Letters, Vol. 100, pp. 162--166, 2006.
[3]
GT-ITM. http://www.cc.gatech.edu/projects/gtitm/, 2019.
[4]
Mike Jia, Jiannong Cao, and Weifa Liang,. Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Transactions on Cloud Computing, vol.5, no. 4, pp.725--737, 2017.
[5]
Mike Jia, Weifa Liang, Zichuan Xu, and Meitian Huang. Cloudlet load balancing in wireless metropolitan area networks. Proc of INFOCOM'16, pp.730--738, IEEE, 2016.
[6]
Mike Jia, Weifa Liang, Zichuan Xu, Meitian Huang, and Yu Ma. QoS-aware cloudlet load balancing in wireless metropolitan area networks. IEEE Transactions on Cloud Computing, vol.8, no.2, pp. 623--634, 2020.
[7]
Yu Ma, Weifa Liang, Zichuan Xu, and Song Guo. Profit maximization for admitting requests with network function services in distributed clouds. IEEE Transactions on Parallel and Distributed Systems, vol.30, no. 5, pp. 1143--1157, 2019.
[8]
Yaozhong Song, Stephen S. Yau, Ruozhou Yu, Xiang Zhang, and Guoliang Xue. An approach to QoS-based task distribution in edge computing networks for IoT applications. Proc of the International Conference on Edge Computing(EDGE), pp. 32--39, IEEE, 2017.
[9]
Tuyen X. Tran, and Dario Pompili. Joint task offloading and resource allocation for multi-server mobile-edge computing networks. IEEE Transactions on Vehicular Technology, vol. 68, no. 1, pp. 856--868, 2018.
[10]
David Tse and Pramod Viswanath. Fundamentals of Wireless Communication. Cambridge University Press, 2005.
[11]
Wanqing Wu, Sandeep Pirbhulal, Arun Kumar Sangaiah, Subhas Chandra Mukhopadhyay, and Guanglin Li. Optimization of signal quality over comfortability of textile electrodes for ECG monitoring in fog computing based medical applications. Future generation computer systems, vol. 86, pp. 515--526, 2018.
[12]
Qiufen Xia, Weifa Liang, and Wenzheng Xu. Throughput maximization for online request admissions in mobile cloudlets. Proc of 38th Annual IEEE Conference on Local Computer Networks (LCN), IEEE, 2013.
[13]
Zichuan Xu, Weifa Liang, Wenzheng Xu, Mike Jia, and Song Guo. Efficient algorithms for capacitated cloudlet placements. IEEE Transactions on Parallel and Distributed Systems, vol.27, no.10, pp. 2866--2880, 2016.
[14]
Zichuan Xu, Weifa Liang, Mike Jia, Meitian Huang, and Guoqiang Mao. Task offloading with network function services in a mobile edge-cloud network. IEEE Transactions on Mobile Computing, vol.18, no. 11, pp. 2672--2685, 2019.
[15]
Zichuan Xu, Wanli Gong, Qiufen Xia, Weifa Liang, Omer F. Rana, and Guowei Wu. NFV-enabled IoT service provisioning in mobile edge clouds. IEEE Transactions on Mobile Computing, to be published, 2020.
[16]
Zichuan Xu, Zhiheng Zhang, Weifa Liang, Qiufen Xia, Omer F. Rana, and Guowei Wu. QoS-aware VNF placement and service chaining for IoT applications in multi-tier mobile edge networks. ACM Transactions on Sensor Networks, vol.16, no.3, Article 23: 1--23:27, 2020.
[17]
Ruozhou Yu, Guoliang Xue, and Xiang Zhang. Application provisioning in fog computing-enabled internet-of-things: a network perspective. Proc of INFOCOM'18, pp. 783--791, IEEE, 2018.

Cited By

View all
  • (2024)A Cost-Benefit Model for Feasible IoT Edge Resources Scalability to Improve Real-Time Processing PerformanceCybernetics and Information Technologies10.2478/cait-2024-003624:4(59-77)Online publication date: 18-Dec-2024
  • (2024)M4: A Framework for Per-Flow Quantile Estimation2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00364(4787-4800)Online publication date: 13-May-2024
  • (2023)Service Home Identification of Multiple-Source IoT Applications in Edge ComputingIEEE Transactions on Services Computing10.1109/TSC.2022.317657616:2(1417-1430)Online publication date: 1-Mar-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
MSWiM '20: Proceedings of the 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems
November 2020
278 pages
ISBN:9781450381178
DOI:10.1145/3416010
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 November 2020

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. approximation algorithms
  2. cost modeling
  3. delay-sensitive services
  4. edge computing
  5. generalized assignment problems
  6. heterogeneous mec networks
  7. online algorithms
  8. service provisioning
  9. the average service delay
  10. the user experience of using services
  11. user service experience

Qualifiers

  • Research-article

Conference

MSWiM '20
Sponsor:

Acceptance Rates

Overall Acceptance Rate 398 of 1,577 submissions, 25%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)29
  • Downloads (Last 6 weeks)2
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)A Cost-Benefit Model for Feasible IoT Edge Resources Scalability to Improve Real-Time Processing PerformanceCybernetics and Information Technologies10.2478/cait-2024-003624:4(59-77)Online publication date: 18-Dec-2024
  • (2024)M4: A Framework for Per-Flow Quantile Estimation2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00364(4787-4800)Online publication date: 13-May-2024
  • (2023)Service Home Identification of Multiple-Source IoT Applications in Edge ComputingIEEE Transactions on Services Computing10.1109/TSC.2022.317657616:2(1417-1430)Online publication date: 1-Mar-2023
  • (2023)Budget-Aware User Satisfaction Maximization on Service Provisioning in Mobile Edge ComputingIEEE Transactions on Mobile Computing10.1109/TMC.2022.3205427(1-13)Online publication date: 2023
  • (2023)Throughput Maximization of Delay-Aware DNN Inference in Edge Computing by Exploring DNN Model Partitioning and Inference ParallelismIEEE Transactions on Mobile Computing10.1109/TMC.2021.312594922:5(3017-3030)Online publication date: 1-May-2023
  • (2023)Internet of Things Control Center2023 4th International Conference on Signal Processing and Communication (ICSPC)10.1109/ICSPC57692.2023.10125704(225-228)Online publication date: 23-Mar-2023
  • (2023)How to Enrich Metaverse? Blockchains, AI, and Digital TwinFrom Blockchain to Web3 & Metaverse10.1007/978-981-99-3648-9_2(27-61)Online publication date: 25-May-2023
  • (2022)Multi-step Prediction of Worker Resource Usage at the Extreme EdgeProceedings of the 25th International ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems10.1145/3551659.3559051(25-32)Online publication date: 24-Oct-2022
  • (2022)Maximizing User Service Satisfaction for Delay-Sensitive IoT Applications in Edge ComputingIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.310713733:5(1199-1212)Online publication date: 1-May-2022
  • (2022)Efficient Mobile Computation Offloading over a Finite-State Markovian Channel using Spectral State Aggregation2022 IEEE 47th Conference on Local Computer Networks (LCN)10.1109/LCN53696.2022.9843606(81-89)Online publication date: 26-Sep-2022
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media