.png){kind=icon}
返回
A Hyper Heuristic Algorithm for Efficient Resource Allocation in 5G Mobile Edge Clouds
.png){kind=icon}
PRE
AI
DOI：10.1109/TMC.2022.3213410.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Emergence of intelligent devices and mobile edge clouds (MECs) in 5G networks has exponentially increased the number of applications that demand low latency services. However, their resource heterogeneity, limited computing power and storage including congestion in the ultra-dense 5G network, make the real-time services challenging. Existing works are limited either by addressing application delay requirements or computational load balancing. This article develops an efficient resource allocation framework for selecting optimal servers and routing paths in the 5G MEC network by jointly optimizing latency, computational, and network load variances. First, we formulate the above multi-objective problem as a mixed-integer non-linear programming problem. Further, we adopt a hyper-heuristic (AWSH) algorithm by leveraging the combined powers of Ant Colony, Whale, Sine-Cosine, and Henry Gas Solubility Optimization algorithms. The proposed AWSH algorithm works at the higher level, and it explores and exploits one of the three lower-level heuristics in each iteration to efficiently capture the dynamically varying environmental parameters and thereby address the resource allocation problem. Their collaborative effort helps to achieve a global optimum in allocating resources of 5G MEC network. Simulation results prove the superiority of the AWSH algorithm compared to state-of-the-art solutions in terms of service latency, successful offloading ratio, and load balancing.
Keyword:
Servers
Resource management
Optimization
5G mobile communication
Delays
Task analysis
Mobile computing
5G
Mobile edge cloud
resource allocation
load balancing
multi-objective optimization problem
hyper-heuristic algorithm
期刊
IF:
9.2
论文数:
5.6K
被引数:
1.8W
机构
引用论文
Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach移动边缘计算的智能资源分配: 一种深度强化学习方法
PREAI
分享收藏Selective Offloading in Mobile Edge Computing for the Green Internet of Things.png)
IEEE NETWORK
IEEE NETWORKIF6.3
PREAI
分享收藏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
OAAI
OAAI分享收藏Integration of D2D, Network Slicing, and MEC in 5G Cellular Networks: Survey and Challenges5g蜂窝网络中D2D、网络切片和MEC的集成: 调查和挑战IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容