Edge server placement in mobile edge computing
By Shangguang Wang, Yali Zhao, Jinliang Xu, Jie Yuan, Ching-Hsien Hsu. J. Parallel Distributed Comput., 2019
421 citations recorded by the source corpus.
Abstract With the rapid increase in the development of the Internet of Things and 5G networks in the smart city context, a large amount of data (i.e., big data) is expected to be generated, resulting in increased latency for the traditional cloud computing paradigm. To reduce the latency, mobile edge computing has been considered for offloading a part of the workload from mobile devices to nearby edge servers that have sufficient computation resources. Although there has been significant research in the field of mobile edge computing, little attention has been given to understanding the placement of edge servers in smart cities to optimize the mobile edge computing network performance. In this paper, we study the edge server placement problem in mobile edge computing environments for smart cities. First, we formulate the problem as a multi-objective constraint optimization problem that places edge servers in some strategic locations with the objective to make balance the workloads of edge servers and minimize the access delay between the mobile user and edge server. Then, we adopt mixed integer programming to find the optimal solution. Experimental results based on Shanghai Telecom’s base station dataset show that our approach outperforms several representative approaches in terms of access delay and workload balancing.
Subfields: Computer Networks and Communications, Hardware and Architecture, Information Systems
View Paper: https://www.semanticscholar.org/paper/f5affe02d2492ade9cf117331f3d386cea224b15
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
In Information Systems
Citations in the corpus
- Resource Scheduling in Edge Computing: A Survey (2021, methodology): “For example, the data from LiDAR and high-definition camera on CAVs is for safety purpose [10], [11]; the data from body area networks (BAN) is for health monitoring; and the data from surveillance cameras is for security [12].”