Adaptive Task Partitioning at Local Device or Remote Edge Server for Offloading in MEC

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Standard

Adaptive Task Partitioning at Local Device or Remote Edge Server for Offloading in MEC. / Liu, Jianhui; Zhang, Qi.

2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings. Seoul : IEEE, 2020. 9120484.

Publikation: Bidrag til bog/antologi/rapport/proceedingKonferencebidrag i proceedingsForskningpeer review

Harvard

Liu, J & Zhang, Q 2020, Adaptive Task Partitioning at Local Device or Remote Edge Server for Offloading in MEC. i 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings., 9120484, IEEE, Seoul, 2020 IEEE Wireless Communications and Networking Conference (WCNC), 25/05/2020. https://doi.org/10.1109/WCNC45663.2020.9120484

APA

Liu, J., & Zhang, Q. (2020). Adaptive Task Partitioning at Local Device or Remote Edge Server for Offloading in MEC. I 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings [9120484] IEEE. https://doi.org/10.1109/WCNC45663.2020.9120484

CBE

Liu J, Zhang Q. 2020. Adaptive Task Partitioning at Local Device or Remote Edge Server for Offloading in MEC. I 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings. Seoul: IEEE. Article 9120484. https://doi.org/10.1109/WCNC45663.2020.9120484

MLA

Liu, Jianhui og Qi Zhang "Adaptive Task Partitioning at Local Device or Remote Edge Server for Offloading in MEC". 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings. Seoul: IEEE. 2020. https://doi.org/10.1109/WCNC45663.2020.9120484

Vancouver

Liu J, Zhang Q. Adaptive Task Partitioning at Local Device or Remote Edge Server for Offloading in MEC. I 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings. Seoul: IEEE. 2020. 9120484 https://doi.org/10.1109/WCNC45663.2020.9120484

Author

Liu, Jianhui ; Zhang, Qi. / Adaptive Task Partitioning at Local Device or Remote Edge Server for Offloading in MEC. 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings. Seoul : IEEE, 2020.

Bibtex

@inproceedings{83458b4f3218473bb7bab0cbffe2384d,
title = "Adaptive Task Partitioning at Local Device or Remote Edge Server for Offloading in MEC",
abstract = "Mobile edge computing (MEC) is one of the promising solutions to process computational-intensive tasks for the emerging time-critical Internet-of-Things (IoT) use cases, e.g., virtual reality (VR), augmented reality (AR), autonomous vehicle. The latency can be reduced further, when a task is partitioned and computed by multiple edge servers' (ESs) collaboration. However, the state-of-the-art work studies the MEC-enabled offloading based on a static framework, which partitions tasks at either the local user equipment (UE) or the primary ES. The dynamic selection between the two offloading schemes has not been well studied yet. In this paper, we investigate a dynamic offloading framework in a multi-user scenario. Each UE can decide who partitions a task according to the network status, e.g., channel quality and allocated computation resource. Based on the framework, we model the latency to complete a task, and formulate an optimization problem to minimize the average latency among UEs. The problem is solved by jointly optimizing task partitioning and the allocation of the communication and computation resources. The numerical results show that, compared with the static offloading schemes, the proposed algorithm achieves the lower latency in all tested scenarios. Moreover, both mathematical derivation and simulation illustrate that the wireless channel quality difference between a UE and different ESs can be used as an important criterion to determine the right scheme.",
keywords = "IoT, Mobile edge computing, adaptive task partitioning, computation offloading, time critical",
author = "Jianhui Liu and Qi Zhang",
year = "2020",
month = may,
doi = "10.1109/WCNC45663.2020.9120484",
language = "English",
booktitle = "2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings",
publisher = "IEEE",
note = "null ; Conference date: 25-05-2020 Through 28-05-2020",
url = "https://wcnc2020.ieee-wcnc.org/",

}

RIS

TY - GEN

T1 - Adaptive Task Partitioning at Local Device or Remote Edge Server for Offloading in MEC

AU - Liu, Jianhui

AU - Zhang, Qi

PY - 2020/5

Y1 - 2020/5

N2 - Mobile edge computing (MEC) is one of the promising solutions to process computational-intensive tasks for the emerging time-critical Internet-of-Things (IoT) use cases, e.g., virtual reality (VR), augmented reality (AR), autonomous vehicle. The latency can be reduced further, when a task is partitioned and computed by multiple edge servers' (ESs) collaboration. However, the state-of-the-art work studies the MEC-enabled offloading based on a static framework, which partitions tasks at either the local user equipment (UE) or the primary ES. The dynamic selection between the two offloading schemes has not been well studied yet. In this paper, we investigate a dynamic offloading framework in a multi-user scenario. Each UE can decide who partitions a task according to the network status, e.g., channel quality and allocated computation resource. Based on the framework, we model the latency to complete a task, and formulate an optimization problem to minimize the average latency among UEs. The problem is solved by jointly optimizing task partitioning and the allocation of the communication and computation resources. The numerical results show that, compared with the static offloading schemes, the proposed algorithm achieves the lower latency in all tested scenarios. Moreover, both mathematical derivation and simulation illustrate that the wireless channel quality difference between a UE and different ESs can be used as an important criterion to determine the right scheme.

AB - Mobile edge computing (MEC) is one of the promising solutions to process computational-intensive tasks for the emerging time-critical Internet-of-Things (IoT) use cases, e.g., virtual reality (VR), augmented reality (AR), autonomous vehicle. The latency can be reduced further, when a task is partitioned and computed by multiple edge servers' (ESs) collaboration. However, the state-of-the-art work studies the MEC-enabled offloading based on a static framework, which partitions tasks at either the local user equipment (UE) or the primary ES. The dynamic selection between the two offloading schemes has not been well studied yet. In this paper, we investigate a dynamic offloading framework in a multi-user scenario. Each UE can decide who partitions a task according to the network status, e.g., channel quality and allocated computation resource. Based on the framework, we model the latency to complete a task, and formulate an optimization problem to minimize the average latency among UEs. The problem is solved by jointly optimizing task partitioning and the allocation of the communication and computation resources. The numerical results show that, compared with the static offloading schemes, the proposed algorithm achieves the lower latency in all tested scenarios. Moreover, both mathematical derivation and simulation illustrate that the wireless channel quality difference between a UE and different ESs can be used as an important criterion to determine the right scheme.

KW - IoT

KW - Mobile edge computing

KW - adaptive task partitioning

KW - computation offloading

KW - time critical

UR - http://www.scopus.com/inward/record.url?scp=85087278429&partnerID=8YFLogxK

U2 - 10.1109/WCNC45663.2020.9120484

DO - 10.1109/WCNC45663.2020.9120484

M3 - Article in proceedings

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

PB - IEEE

CY - Seoul

Y2 - 25 May 2020 through 28 May 2020

ER -