Paper Info
Title
Bandit Learning-based Service Placement and Resource Allocation for Mobile Edge Computing
Abstract
Service placement is a significant issue in mobile edge computing (MEC) system. Many works have proposed efficient offline approaches for service placement problems in MEC system. However, because of the randomness and uncertainty of mobile networks, it is impractical for these approaches to be implemented. Facing these uncertainty, we propose an online service placement scheme for MEC system without knowing service demand and network states in advance. In order to maximize the long-term accumulated reward obtained by service placement with limited resource constraint, we analyse this problem by a combinatorial multi-armed bandit (MAB) framework. In addition, because we simultaneously consider the service placement and resource allocation among services, it can be formulated as a multiple choice knapsack problem (MCKP) in each time slot. To solve this long-term reward maximization problem, we first propose a combinatorial upper bound confidence(CUCB)-based online service placement and resource allocation scheme. Then, we analyse the performance of this algorithm theoretically. Finally, simulation results show the efficiency of the algorithm.
Year
DOI
Venue
2020
10.1109/PIMRC48278.2020.9217105
2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications
Keywords
DocType
ISSN
Service placement,multi-armed bandit,mobile edge computing,resource allocation
Conference
2166-9570
ISBN
Citations 
PageRank 
978-1-7281-4490-0
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Wen He101.35
Dazhi He201.35
Yihang Huang3156.00
Yizhe Zhang400.68
Yin Xu510.75
Guan Yun-feng600.34
Wenjun Zhang71789177.28