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

Tian, H. and Xu, X. and Qi, L. and Zhang, X. and Dou, W. and Yu, S. and Ni, Q. (2021) CoPace:Edge Computation Offloading and Caching for Self-Driving with Deep Reinforcement Learning. IEEE Transactions on Vehicular Technology. ISSN 0018-9545

[img]
Text (Author final accepted version)
Author_final_accepted_version.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (2MB)

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.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Vehicular Technology
Additional Information:
©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.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2202
Subjects:
ID Code:
162276
Deposited By:
Deposited On:
15 Nov 2021 13:56
Refereed?:
Yes
Published?:
Published
Last Modified:
03 Dec 2021 09:32