Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems

Yuyi Mao, Jun Zhang, Khaled Letaief

Research output: Chapter in Book/Report/Conference proceedingConference contribution

51 Citations (Scopus)

Abstract

Mobile-edge computing (MEC) has emerged as a prominent technique to provide mobile services with high computation requirement, by migrating the computation- intensive tasks from the mobile devices to the nearby MEC servers. To reduce the execution latency and device energy consumption, in this paper, we jointly optimize task offloading scheduling and transmit power allocation for MEC systems with multiple independent tasks. A low-complexity sub-optimal algorithm is proposed to minimize the weighted sum of the execution delay and device energy consumption based on alternating minimization. Specifically, given the transmit power allocation, the optimal task offloading scheduling, i.e., to determine the order of offloading, is obtained with the help of flow shop scheduling theory. Besides, the optimal transmit power allocation with a given task offloading scheduling decision will be determined using convex optimization techniques. Simulation results show that task offloading scheduling is more critical when the available radio and computational resources in MEC systems are relatively balanced. In addition, it is shown that the proposed algorithm achieves near-optimal execution delay along with a substantial device energy saving.

Original languageEnglish
Title of host publication2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509041831
DOIs
Publication statusPublished - 10 May 2017
Externally publishedYes
Event2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - San Francisco, United States
Duration: 19 Mar 201722 Mar 2017

Other

Other2017 IEEE Wireless Communications and Networking Conference, WCNC 2017
CountryUnited States
CitySan Francisco
Period19/3/1722/3/17

Fingerprint

Scheduling
Energy utilization
Convex optimization
Mobile devices
Energy conservation
Servers

Keywords

  • Convex optimization
  • Flow shop scheduling
  • Mobile-edge computing
  • Power control
  • Task offloading scheduling

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Mao, Y., Zhang, J., & Letaief, K. (2017). Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems. In 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings [7925615] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WCNC.2017.7925615

Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems. / Mao, Yuyi; Zhang, Jun; Letaief, Khaled.

2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. 7925615.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mao, Y, Zhang, J & Letaief, K 2017, Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems. in 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings., 7925615, Institute of Electrical and Electronics Engineers Inc., 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017, San Francisco, United States, 19/3/17. https://doi.org/10.1109/WCNC.2017.7925615
Mao Y, Zhang J, Letaief K. Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems. In 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. 7925615 https://doi.org/10.1109/WCNC.2017.7925615
Mao, Yuyi ; Zhang, Jun ; Letaief, Khaled. / Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems. 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017.
@inproceedings{0596ad3e2d544abbbdf67f1afbb2f028,
title = "Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems",
abstract = "Mobile-edge computing (MEC) has emerged as a prominent technique to provide mobile services with high computation requirement, by migrating the computation- intensive tasks from the mobile devices to the nearby MEC servers. To reduce the execution latency and device energy consumption, in this paper, we jointly optimize task offloading scheduling and transmit power allocation for MEC systems with multiple independent tasks. A low-complexity sub-optimal algorithm is proposed to minimize the weighted sum of the execution delay and device energy consumption based on alternating minimization. Specifically, given the transmit power allocation, the optimal task offloading scheduling, i.e., to determine the order of offloading, is obtained with the help of flow shop scheduling theory. Besides, the optimal transmit power allocation with a given task offloading scheduling decision will be determined using convex optimization techniques. Simulation results show that task offloading scheduling is more critical when the available radio and computational resources in MEC systems are relatively balanced. In addition, it is shown that the proposed algorithm achieves near-optimal execution delay along with a substantial device energy saving.",
keywords = "Convex optimization, Flow shop scheduling, Mobile-edge computing, Power control, Task offloading scheduling",
author = "Yuyi Mao and Jun Zhang and Khaled Letaief",
year = "2017",
month = "5",
day = "10",
doi = "10.1109/WCNC.2017.7925615",
language = "English",
booktitle = "2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems

AU - Mao, Yuyi

AU - Zhang, Jun

AU - Letaief, Khaled

PY - 2017/5/10

Y1 - 2017/5/10

N2 - Mobile-edge computing (MEC) has emerged as a prominent technique to provide mobile services with high computation requirement, by migrating the computation- intensive tasks from the mobile devices to the nearby MEC servers. To reduce the execution latency and device energy consumption, in this paper, we jointly optimize task offloading scheduling and transmit power allocation for MEC systems with multiple independent tasks. A low-complexity sub-optimal algorithm is proposed to minimize the weighted sum of the execution delay and device energy consumption based on alternating minimization. Specifically, given the transmit power allocation, the optimal task offloading scheduling, i.e., to determine the order of offloading, is obtained with the help of flow shop scheduling theory. Besides, the optimal transmit power allocation with a given task offloading scheduling decision will be determined using convex optimization techniques. Simulation results show that task offloading scheduling is more critical when the available radio and computational resources in MEC systems are relatively balanced. In addition, it is shown that the proposed algorithm achieves near-optimal execution delay along with a substantial device energy saving.

AB - Mobile-edge computing (MEC) has emerged as a prominent technique to provide mobile services with high computation requirement, by migrating the computation- intensive tasks from the mobile devices to the nearby MEC servers. To reduce the execution latency and device energy consumption, in this paper, we jointly optimize task offloading scheduling and transmit power allocation for MEC systems with multiple independent tasks. A low-complexity sub-optimal algorithm is proposed to minimize the weighted sum of the execution delay and device energy consumption based on alternating minimization. Specifically, given the transmit power allocation, the optimal task offloading scheduling, i.e., to determine the order of offloading, is obtained with the help of flow shop scheduling theory. Besides, the optimal transmit power allocation with a given task offloading scheduling decision will be determined using convex optimization techniques. Simulation results show that task offloading scheduling is more critical when the available radio and computational resources in MEC systems are relatively balanced. In addition, it is shown that the proposed algorithm achieves near-optimal execution delay along with a substantial device energy saving.

KW - Convex optimization

KW - Flow shop scheduling

KW - Mobile-edge computing

KW - Power control

KW - Task offloading scheduling

UR - http://www.scopus.com/inward/record.url?scp=85019740134&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85019740134&partnerID=8YFLogxK

U2 - 10.1109/WCNC.2017.7925615

DO - 10.1109/WCNC.2017.7925615

M3 - Conference contribution

AN - SCOPUS:85019740134

BT - 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -