Research On Resource Allocation Algorithm In MEC System Combined With Energy Harvesting Considering User Mobility

Mobile edge computing(MEC)places computing resources close to the edge of the wireless network,which can significantly reduce service delays.Radio frequency energy harvesting technology is a new way of power supply,which can harvest the energy from electromagnetic waves in the environment and provide power to mobile terminals continuously.MEC system combined with energy harvesting has attracted wide attention,which can provide power for the user terminal and relieve local computing pressure.In the network scenario of MEC system combined with energy harvesting,in order to improve the energy efficiency and reduce the energy consumption of the system,this thesis considers the effect of user mobility on MEC service quality and studies a series of resource allocation strategies.The main contributions are summarized as follows:(1)The resource allocation strategy in MEC system combined with energy harvesting based on virtual machine migration is investigated to maximize the energy efficiency.The effect of user mobility on MEC system is not considered in the existing research.A resource allocation strategy based on virtual machine migration is proposed in the energy harvesting MEC system.Mobility model and energy harvesting model are presented by taking user mobility into account.By adopting the way of virtual machine migration,the computational tasks offloaded by the user are transferred from the initial MEC server to the current MEC server.The computational results are directly fed back to the user after the current MEC server completing the computational tasks.By jointly considering offloading computational task by the user and results feedback of MEC server,the problem of power and subcarrier allocation is modeled as a mixed integer nonlinear programming problem.The objective is to maximize the system energy efficiency while satisfying the constraints of energy consumption,subcarrier allocation,and transmitting power.In order to reduce the computational complexity,the suboptimal solution is obtained by introducing a genetic algorithm.Simulation results show that the proposed method has higher energy efficiency than partial power or subcarrier allocation methods based on the genetic algorithm.(2)The resource allocation strategy in MEC system combined with energy harvesting considering edge server load is investigated to minimize the energy consumption.In the energy harvesting MEC system based on virtual machine migration technology,considering the finiteness of edge server resources,time-series prediction for resource load on the edge server by K-order Markov model can effectively avoid the edge server overload caused by frequent virtual machin e migration,and make MEC service more stable.The objective of resource allocation strategy is to minimize energy consumption while satisfying the constraints of energy consumption,subcarrier allocation,and transmitting power.Quantum-behaved particle swarm optimization algorithm is employed to derive the suboptimal solution at relatively low time complexity.Simulation results show that the proposed method has higher energy efficiency than partial power or subcarrier allocation methods based on the quantum-behaved particle swarm optimization algorithm.