Deep reinforcement learning-based resource allocation and seamless handover in multi-access edge computing based on SDN

2021 ◽  
Author(s):  
Chunlin Li ◽  
Yong Zhang ◽  
Youlong Luo
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Edge Computing ◽  
Seamless Handover ◽  
Multi Access

scholarly journals Efficient Computation Offloading in Multi-Tier Multi-Access Edge Computing Systems: A Particle Swarm Optimization Approach

2019 ◽  
Vol 10 (1) ◽  
pp. 203 ◽  
Author(s):  
Luan N. T. Huynh ◽  
Quoc-Viet Pham ◽  
Xuan-Qui Pham ◽  
Tri D. T. Nguyen ◽  
Md Delowar Hossain ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mixed Integer ◽  
5G Networks ◽  
Swarm Optimization ◽  
Multi Access

In recent years, multi-access edge computing (MEC) has become a promising technology used in 5G networks based on its ability to offload computational tasks from mobile devices (MDs) to edge servers in order to address MD-specific limitations. Despite considerable research on computation offloading in 5G networks, this activity in multi-tier multi-MEC server systems continues to attract attention. Here, we investigated a two-tier computation-offloading strategy for multi-user multi-MEC servers in heterogeneous networks. For this scenario, we formulated a joint resource-allocation and computation-offloading decision strategy to minimize the total computing overhead of MDs, including completion time and energy consumption. The optimization problem was formulated as a mixed-integer nonlinear program problem of NP-hard complexity. Under complex optimization and various application constraints, we divided the original problem into two subproblems: decisions of resource allocation and computation offloading. We developed an efficient, low-complexity algorithm using particle swarm optimization capable of high-quality solutions and guaranteed convergence, with a high-level heuristic (i.e., meta-heuristic) that performed well at solving a challenging optimization problem. Simulation results indicated that the proposed algorithm significantly reduced the total computing overhead of MDs relative to several baseline methods while guaranteeing to converge to stable solutions.

Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT

2020 ◽  
Vol 151 ◽  
pp. 355-364 ◽  
Author(s):  
S. Vimal ◽  
Manju Khari ◽  
Nilanjan Dey ◽  
Rubén González Crespo ◽  
Y. Harold Robinson
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Edge Computing ◽  
Mobile Edge Computing
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