Home > Research > Publications & Outputs > CoPace

Electronic data

  • Author final accepted version

    Rights statement: ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Accepted author manuscript, 2.81 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

CoPace: Edge Computation Offloading and Caching for Self-Driving with Deep Reinforcement Learning

Research output: Contribution to journalJournal articlepeer-review

Published

Standard

CoPace : Edge Computation Offloading and Caching for Self-Driving with Deep Reinforcement Learning. / Tian, H.; Xu, X.; Qi, L.; Zhang, X.; Dou, W.; Yu, S.; Ni, Q.

In: IEEE Transactions on Vehicular Technology, Vol. 70, No. 12, 31.12.2021, p. 13281-13293.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Tian, H, Xu, X, Qi, L, Zhang, X, Dou, W, Yu, S & Ni, Q 2021, 'CoPace: Edge Computation Offloading and Caching for Self-Driving with Deep Reinforcement Learning', IEEE Transactions on Vehicular Technology, vol. 70, no. 12, pp. 13281-13293. https://doi.org/10.1109/TVT.2021.3121096

APA

Tian, H., Xu, X., Qi, L., Zhang, X., Dou, W., Yu, S., & Ni, Q. (2021). CoPace: Edge Computation Offloading and Caching for Self-Driving with Deep Reinforcement Learning. IEEE Transactions on Vehicular Technology, 70(12), 13281-13293. https://doi.org/10.1109/TVT.2021.3121096

Vancouver

Tian H, Xu X, Qi L, Zhang X, Dou W, Yu S et al. CoPace: Edge Computation Offloading and Caching for Self-Driving with Deep Reinforcement Learning. IEEE Transactions on Vehicular Technology. 2021 Dec 31;70(12):13281-13293. https://doi.org/10.1109/TVT.2021.3121096

Author

Tian, H. ; Xu, X. ; Qi, L. ; Zhang, X. ; Dou, W. ; Yu, S. ; Ni, Q. / CoPace : Edge Computation Offloading and Caching for Self-Driving with Deep Reinforcement Learning. In: IEEE Transactions on Vehicular Technology. 2021 ; Vol. 70, No. 12. pp. 13281-13293.

Bibtex

@article{aa514e81739d45d58ac8d9e797b4e176,
title = "CoPace: Edge Computation Offloading and Caching for Self-Driving with Deep Reinforcement Learning",
abstract = "Currently, self-driving, emerging as a key automatic application, has brought a huge potential for the provision of in-vehicle services (e.g., automatic path planning) to mitigate urban traffic congestion and enhance travel safety. To provide high-quality vehicular services with stringent delay constraints, edge computing (EC) enables resource-hungry self-driving vehicles (SDVs) to offload computation-intensive tasks to the edge servers (ESs). In addition, caching highly reusable contents decreases the redundant transmission time and improves the quality of services (QoS) of SDVs, which is envisioned as a supplement to the computation offloading. However, the high mobility and time-varying requests of SDVs make it challenging to provide reliable offloading decisions while guaranteeing the resource utilization of content caching. To this end, in this paper we propose a \underline{co}llaborative com\underline{p}utation offlo\underline{a}ding and \underline{c}ont\underline{e}nt caching method, named CoPace, by leveraging deep reinforcement learning (DRL) in EC for self-driving system. Specifically, we resort to a deep learning model to predict the future time-varying content popularity, taking into account the temporal-spatial attributes of requests. Moreover, a DRL-based algorithm is developed to jointly optimize the offloading and caching decisions, as well as the resource allocation (i.e., computing and communication resources) strategies. Extensive experiments with real-world datasets in Shanghai, China, are conducted to evaluate the performance, which demonstrates that CoPace is both effective and well-performed. ",
keywords = "computation offloading, content caching, deep reinforcement learning, Delays, edge computing, Optimization, Prediction algorithms, Quality of service, Resource management, Self-driving, Software, Task analysis, Application programs, Computer software reusability, Edge computing, Job analysis, Motion planning, Quality control, Reinforcement learning, Resource allocation, Traffic congestion, Computation offloading, Content caching, Delay, Optimisations, Quality-of-service, Self drivings, Deep learning",
author = "H. Tian and X. Xu and L. Qi and X. Zhang and W. Dou and S. Yu and Q. Ni",
note = "{\textcopyright}2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2021",
month = dec,
day = "31",
doi = "10.1109/TVT.2021.3121096",
language = "English",
volume = "70",
pages = "13281--13293",
journal = "IEEE Transactions on Vehicular Technology",
issn = "0018-9545",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "12",

}

RIS

TY - JOUR

T1 - CoPace

T2 - Edge Computation Offloading and Caching for Self-Driving with Deep Reinforcement Learning

AU - Tian, H.

AU - Xu, X.

AU - Qi, L.

AU - Zhang, X.

AU - Dou, W.

AU - Yu, S.

AU - Ni, Q.

N1 - ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2021/12/31

Y1 - 2021/12/31

N2 - Currently, self-driving, emerging as a key automatic application, has brought a huge potential for the provision of in-vehicle services (e.g., automatic path planning) to mitigate urban traffic congestion and enhance travel safety. To provide high-quality vehicular services with stringent delay constraints, edge computing (EC) enables resource-hungry self-driving vehicles (SDVs) to offload computation-intensive tasks to the edge servers (ESs). In addition, caching highly reusable contents decreases the redundant transmission time and improves the quality of services (QoS) of SDVs, which is envisioned as a supplement to the computation offloading. However, the high mobility and time-varying requests of SDVs make it challenging to provide reliable offloading decisions while guaranteeing the resource utilization of content caching. To this end, in this paper we propose a \underline{co}llaborative com\underline{p}utation offlo\underline{a}ding and \underline{c}ont\underline{e}nt caching method, named CoPace, by leveraging deep reinforcement learning (DRL) in EC for self-driving system. Specifically, we resort to a deep learning model to predict the future time-varying content popularity, taking into account the temporal-spatial attributes of requests. Moreover, a DRL-based algorithm is developed to jointly optimize the offloading and caching decisions, as well as the resource allocation (i.e., computing and communication resources) strategies. Extensive experiments with real-world datasets in Shanghai, China, are conducted to evaluate the performance, which demonstrates that CoPace is both effective and well-performed.

AB - Currently, self-driving, emerging as a key automatic application, has brought a huge potential for the provision of in-vehicle services (e.g., automatic path planning) to mitigate urban traffic congestion and enhance travel safety. To provide high-quality vehicular services with stringent delay constraints, edge computing (EC) enables resource-hungry self-driving vehicles (SDVs) to offload computation-intensive tasks to the edge servers (ESs). In addition, caching highly reusable contents decreases the redundant transmission time and improves the quality of services (QoS) of SDVs, which is envisioned as a supplement to the computation offloading. However, the high mobility and time-varying requests of SDVs make it challenging to provide reliable offloading decisions while guaranteeing the resource utilization of content caching. To this end, in this paper we propose a \underline{co}llaborative com\underline{p}utation offlo\underline{a}ding and \underline{c}ont\underline{e}nt caching method, named CoPace, by leveraging deep reinforcement learning (DRL) in EC for self-driving system. Specifically, we resort to a deep learning model to predict the future time-varying content popularity, taking into account the temporal-spatial attributes of requests. Moreover, a DRL-based algorithm is developed to jointly optimize the offloading and caching decisions, as well as the resource allocation (i.e., computing and communication resources) strategies. Extensive experiments with real-world datasets in Shanghai, China, are conducted to evaluate the performance, which demonstrates that CoPace is both effective and well-performed.

KW - computation offloading

KW - content caching

KW - deep reinforcement learning

KW - Delays

KW - edge computing

KW - Optimization

KW - Prediction algorithms

KW - Quality of service

KW - Resource management

KW - Self-driving

KW - Software

KW - Task analysis

KW - Application programs

KW - Computer software reusability

KW - Edge computing

KW - Job analysis

KW - Motion planning

KW - Quality control

KW - Reinforcement learning

KW - Resource allocation

KW - Traffic congestion

KW - Computation offloading

KW - Content caching

KW - Delay

KW - Optimisations

KW - Quality-of-service

KW - Self drivings

KW - Deep learning

U2 - 10.1109/TVT.2021.3121096

DO - 10.1109/TVT.2021.3121096

M3 - Journal article

VL - 70

SP - 13281

EP - 13293

JO - IEEE Transactions on Vehicular Technology

JF - IEEE Transactions on Vehicular Technology

SN - 0018-9545

IS - 12

ER -