TY - GEN
T1 - DMRA
T2 - 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019
AU - Zhang, Chen
AU - Du, Hongwei
AU - Ye, Qiang
AU - Liu, Chuang
AU - Yuan, He
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Mobile Edge Computing (MEC) is a burgeoning paradigm that pushes data and services away from remote clouds to distributed Base Stations (BSs) equipped with MEC servers, which are deployed by Service Providers (SPs) at the edge of cellular networks. Normally, a SP prefers to use its own BSs, instead of those deployed by other SPs, to provide data and storage services. This can not only improve the quality of user experience but also increase its own revenue. In a densely deployed MEC network where a User Equipment (UE) tends to be covered by multiple BSs from varied SPs, how to allocate the resources in the BSs to provide the best service is a challenging problem. In this paper, we propose a novel resource allocation scheme, Decentralized Multi-SP Resource Allocation (DMRA), for densely-deployed MEC networks in order to maximize the total profit of all SPs and provide high-quality services. Our experimental results indicate that the proposed scheme outperforms the existing resource allocation algorithms for MEC.
AB - Mobile Edge Computing (MEC) is a burgeoning paradigm that pushes data and services away from remote clouds to distributed Base Stations (BSs) equipped with MEC servers, which are deployed by Service Providers (SPs) at the edge of cellular networks. Normally, a SP prefers to use its own BSs, instead of those deployed by other SPs, to provide data and storage services. This can not only improve the quality of user experience but also increase its own revenue. In a densely deployed MEC network where a User Equipment (UE) tends to be covered by multiple BSs from varied SPs, how to allocate the resources in the BSs to provide the best service is a challenging problem. In this paper, we propose a novel resource allocation scheme, Decentralized Multi-SP Resource Allocation (DMRA), for densely-deployed MEC networks in order to maximize the total profit of all SPs and provide high-quality services. Our experimental results indicate that the proposed scheme outperforms the existing resource allocation algorithms for MEC.
KW - Mobile Edge Computing
KW - Profit Maximization
KW - Resource Allocation
UR - https://www.scopus.com/pages/publications/85074838794
U2 - 10.1109/ICDCS.2019.00046
DO - 10.1109/ICDCS.2019.00046
M3 - 会议稿件
AN - SCOPUS:85074838794
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 390
EP - 398
BT - Proceedings - 2019 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 7 July 2019 through 9 July 2019
ER -