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Qi Zhang

Computation Resource Allocation for Heterogeneous Time-Critical IoT Services in MEC

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Standard

Computation Resource Allocation for Heterogeneous Time-Critical IoT Services in MEC. / Liu, Jianhui; Zhang, Qi.
2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings. IEEE, 2020. 9120832.

Publikation: Bidrag til bog/antologi/rapport/proceeding › Konferencebidrag i proceedings › Forskning › peer review

Harvard

Liu, J & Zhang, Q 2020, Computation Resource Allocation for Heterogeneous Time-Critical IoT Services in MEC. i 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings., 9120832, IEEE, IEEE Wireless Communications and Networking Conference 2020, 25/05/2020. https://doi.org/10.1109/WCNC45663.2020.9120832

APA

Liu, J., & Zhang, Q. (2020). Computation Resource Allocation for Heterogeneous Time-Critical IoT Services in MEC. I 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings [9120832] IEEE. https://doi.org/10.1109/WCNC45663.2020.9120832

CBE

Liu J, Zhang Q. 2020. Computation Resource Allocation for Heterogeneous Time-Critical IoT Services in MEC. I 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings. IEEE. Article 9120832. https://doi.org/10.1109/WCNC45663.2020.9120832

MLA

Liu, Jianhui og Qi Zhang "Computation Resource Allocation for Heterogeneous Time-Critical IoT Services in MEC". 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings. IEEE. 2020. https://doi.org/10.1109/WCNC45663.2020.9120832

Vancouver

Liu J, Zhang Q. Computation Resource Allocation for Heterogeneous Time-Critical IoT Services in MEC. I 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings. IEEE. 2020. 9120832 doi: 10.1109/WCNC45663.2020.9120832

Author

Liu, Jianhui ; Zhang, Qi. / Computation Resource Allocation for Heterogeneous Time-Critical IoT Services in MEC. 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings. IEEE, 2020.

Bibtex

@inproceedings{77652c424b004e6a819414e4123d558e,
title = "Computation Resource Allocation for Heterogeneous Time-Critical IoT Services in MEC",
abstract = "Mobile edge computing (MEC) is one of the promising solutions to process computational-intensive tasks within short latency for emerging Internet-of-Things (IoT) use cases, e.g., virtual reality (VR), augmented reality (AR), autonomous vehicle. Due to the coexistence of heterogeneous services in MEC system, the task arrival interval and required execution time can vary depending on services. It is challenging to schedule computation resource for the services with stochastic arrivals and runtime at an edge server (ES). In this paper, we propose a flexible computation offloading framework among users and ESs. Based on the framework, we propose a Lyapunov-based algorithm to dynamically allocate computation resource for heterogeneous time-critical services at the ES. The proposed algorithm minimizes the average timeout probability without any prior knowledge on task arrival process and required runtime. The numerical results show that, compared with the standard queuing models used at ES, the proposed algorithm achieves at least 35% reduction of the timeout probability, and approximated utilization efficiency of computation resource to non-cause queuing model under various scenarios.",
keywords = "IoT, Lyapunov optimization, Mobile edge computing, augmented reality, computation management, latency and reliability",
author = "Jianhui Liu and Qi Zhang",
year = "2020",
doi = "10.1109/WCNC45663.2020.9120832",
language = "English",
booktitle = "2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings",
publisher = "IEEE",
note = "IEEE Wireless Communications and Networking Conference 2020 ; Conference date: 25-05-2020 Through 28-05-2020",
url = "https://wcnc2020.ieee-wcnc.org/",

}

RIS

TY - GEN

T1 - Computation Resource Allocation for Heterogeneous Time-Critical IoT Services in MEC

AU - Liu, Jianhui

AU - Zhang, Qi

PY - 2020

Y1 - 2020

N2 - Mobile edge computing (MEC) is one of the promising solutions to process computational-intensive tasks within short latency for emerging Internet-of-Things (IoT) use cases, e.g., virtual reality (VR), augmented reality (AR), autonomous vehicle. Due to the coexistence of heterogeneous services in MEC system, the task arrival interval and required execution time can vary depending on services. It is challenging to schedule computation resource for the services with stochastic arrivals and runtime at an edge server (ES). In this paper, we propose a flexible computation offloading framework among users and ESs. Based on the framework, we propose a Lyapunov-based algorithm to dynamically allocate computation resource for heterogeneous time-critical services at the ES. The proposed algorithm minimizes the average timeout probability without any prior knowledge on task arrival process and required runtime. The numerical results show that, compared with the standard queuing models used at ES, the proposed algorithm achieves at least 35% reduction of the timeout probability, and approximated utilization efficiency of computation resource to non-cause queuing model under various scenarios.

AB - Mobile edge computing (MEC) is one of the promising solutions to process computational-intensive tasks within short latency for emerging Internet-of-Things (IoT) use cases, e.g., virtual reality (VR), augmented reality (AR), autonomous vehicle. Due to the coexistence of heterogeneous services in MEC system, the task arrival interval and required execution time can vary depending on services. It is challenging to schedule computation resource for the services with stochastic arrivals and runtime at an edge server (ES). In this paper, we propose a flexible computation offloading framework among users and ESs. Based on the framework, we propose a Lyapunov-based algorithm to dynamically allocate computation resource for heterogeneous time-critical services at the ES. The proposed algorithm minimizes the average timeout probability without any prior knowledge on task arrival process and required runtime. The numerical results show that, compared with the standard queuing models used at ES, the proposed algorithm achieves at least 35% reduction of the timeout probability, and approximated utilization efficiency of computation resource to non-cause queuing model under various scenarios.

KW - IoT

KW - Lyapunov optimization

KW - Mobile edge computing

KW - augmented reality

KW - computation management

KW - latency and reliability

UR - http://www.scopus.com/inward/record.url?scp=85087272250&partnerID=8YFLogxK

U2 - 10.1109/WCNC45663.2020.9120832

DO - 10.1109/WCNC45663.2020.9120832

M3 - Article in proceedings

BT - 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings

PB - IEEE

T2 - IEEE Wireless Communications and Networking Conference 2020

Y2 - 25 May 2020 through 28 May 2020

ER -