返回
Edge-Cloud Offloading: Knapsack Potential Game in 5G Multi-Access Edge Computing
DOI:10.1109/TWC.2023.3248270.png)
摘要
En 中文
In 5G, multi-access edge computing enables the applications to be offloaded to near-end edge servers for faster response. According to the 3GPP standards, users in 5G are separated into many types, e.g., vehicles, AR/VR, IoT devices, etc. Specifically, the high-priority traffic can preempt edge resources to guarantee the service quality. However, even if a traffic is transmitted with low priority, its latency requirement in 5G is much lower than that in 4G. Too strict latency requirement and priority-based service make resource configuration difficult on the edge side. Therefore, we propose the edge-cloud offloading mechanism, in which each edge server can offload tasks to back-end cloud server to ensure service quality of both high- and low-priority traffic. In this paper, we establish a priority-based queuing system to model the edge-cloud offloading behaviors. Based on the formulation of our system model, we propose Knapsack Potential Game (KPG) to derive an optimal offloading ratio for each edge server to balance the cost-effectiveness of the overall system. We demonstrate that KPG has low computational complexity and outperforms two baseline algorithms. The results indicate that KPG's performance is optimal and provides a theoretical guideline to operators while designing their edge-cloud offloading strategies without large-scale implementation.
Keyword:
Quality of service
Servers
5G mobile communication
Wireless communication
Resource management
Costs
Time factors
Multi-access edge computing
QoS
5G
performance analysis
3GPP standards
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
机构
暂无机构信息
引用论文
Energy Efficiency Based Joint Computation Offloading and Resource Allocation in Multi-Access MEC Systems多址MEC系统中基于能效的联合计算卸载和资源分配
IEEE ACCESS
IF3.6
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks用于无线供电的移动边缘计算网络中在线计算卸载的深度强化学习
Task Offloading and Resource Allocation for Mobile Edge Computing by Deep Reinforcement Learning Based on SARSA
IEEE ACCESS
IF3.6

