.png){kind=icon}
Return
Optimized Edge Node Allocation Considering User Delay Tolerance for Cost Reduction
.png){kind=icon}
PRE
AI
DOI：10.1109/TSC.2024.3486174.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
With the rise of 5G technology, Mobile (or Multi-Access) Edge Computing (MEC) has become crucial in modern network architecture. One key research area is the effective placement of edge nodes, which has attracted significant attention. Service providers strive to minimize deployment costs for these nodes within a network. Although many studies have explored optimal strategies for reducing these costs, most overlook the allocation of computational resources and the users' tolerance for delays. These factors add complexity, making previous methods less adaptable. In this paper, we define the Cost Minimization in MEC Edge Node Placement problem. Our goal is to find the optimal strategy for deploying edge nodes that minimize costs while cater to users' delay tolerance limits. We prove the NP-hardness of this problem and provide a range of solutions, including Cluster-based Mixed Integer Programming, Coverage First Search, and Distance-Aware Coverage First Search, to address this challenge effectively and efficiently. Additionally, we propose a fine-grained optimization approach for allocating computational resources to edge nodes based on user service requests, significantly lowering deployment costs. Extensive experiments on a large-scale real-world dataset show that our solutions outperform the state-of-the-art in efficiency, effectiveness, and scalability.
Keywords:
Delays
Costs
Servers
Base stations
Resource management
Quality of service
Scalability
Minimization
Optimization
Multi-access edge computing
Mobile (or multi-access) edge computing
edge node placement
cost minimization
user delay tolerance
Journal
IF:
5.8
Papers:
2.2K
Citations:
6.5K
Organization
Cited Papers
ShareSaveShareSaveSmart Healthcare: RL-Based Task Offloading Scheme for Edge-Enable Sensor Networks.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
ShareSaveShareSaveShareSaveDelay-Aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach
PREAI
ShareSave
View more