论文成果

Efficient Workload Allocation and User-Centric Utility Maximization for Task Scheduling in Collaborative Vehicular Edge Computing

发布时间:2021-06-24 点击次数:

DOI码:10.1109/TVT.2021.3064426

发表刊物:IEEE Transactions on Vehicular Technology

摘要:By integrating Mobile Edge Computing (MEC) into vehicular networks, vehicular edge computing extends computing capability to the vehicular network edge and hosts services in close proximity of connected vehicles. Parked Vehicles (PVs) occupy a large portion of the global vehicle and have idle states and resources. They collaborate with the MEC servers for cooperative task processing. This gives rise to a new computing paradigm, called by Collaborative Vehicular Edge Computing (CVEC). In CVEC, we introduce an offloading service provider that deploys an MEC server and schedules PVs on demand to handle offloading tasks. Efficient workload allocation and user-centric utility maximization are studied to optimize the network-wide task scheduling. In dynamic environment, offloading destination of each task is determined in a probabilistic manner. When necessary, the offloading service provider represents an offloading user to design a contract based incentive mechanism for the PVs. Based on contract theory and prospect theory, we model the offloading user's subjective evaluations on the utility in computation offloading, and derive an optimal contract to maximize the subjective utility under information asymmetry. Finally, numerical results are provided to demonstrate the effectiveness and efficiency of our scheme.

第一作者:X. Huang, R. Yu, D. Ye, L. Shu, S. Xie

论文类型:期刊论文

卷号:70

期号:4

ISSN号:1939-9359

是否译文:

发表时间:2021-03-08

下一条:Toward Resource-Efficient Federated Learning in Mobile Edge Computing

广东工业大学网络信息与现代教育技术中心电话:020-39323866 版权所有@ 2020-2021保留所有权利
粤ICP备05008833号

访问量: | 最后更新时间:-- | 开通时间:-- | 手机版
Baidu
map