An Offloading Scheduling Strategy with Minimized Power Overhead for Internet of Vehicles Based on Mobile Edge Computing
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초록
By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization,distributed computing and parallel computing technologies to enable users dynamically obtain computingpower, storage space and other services as needed. Applying edge computing architectures to Internet ofVehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicleapplications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloadingstrategy based on the MEC load state is proposed, which not only considers reducing the delay of calculationresults by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, thedelay factor and the energy consumption factor are introduced according to the characteristics of tasks, and thecost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective ofvehicles, the delay preference factor and the energy consumption preference factor are introduced to define thecost of executing a computing task for another computing task. Furthermore, a mathematical optimizationmodel for minimizing the power overhead is constructed with the constraints of time delay and powerconsumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. Thesimulation results show that this strategy can effectively reduce the system power consumption by shorteningthe task execution delay. Finally, we can choose whether to offload computing tasks to MEC server forexecution according to the size of two costs. This strategy not only meets the requirements of time delay andenergy consumption, but also ensures the lowest cost.
Abstract
By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization,distributed computing and parallel computing technologies to enable users dynamically obtain computingpower, storage space and other services as needed. Applying edge computing architectures to Internet ofVehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicleapplications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloadingstrategy based on the MEC load state is proposed, which not only considers reducing the delay of calculationresults by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, thedelay factor and the energy consumption factor are introduced according to the characteristics of tasks, and thecost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective ofvehicles, the delay preference factor and the energy consumption preference factor are introduced to define thecost of executing a computing task for another computing task. Furthermore, a mathematical optimizationmodel for minimizing the power overhead is constructed with the constraints of time delay and powerconsumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. Thesimulation results show that this strategy can effectively reduce the system power consumption by shorteningthe task execution delay. Finally, we can choose whether to offload computing tasks to MEC server forexecution according to the size of two costs. This strategy not only meets the requirements of time delay andenergy consumption, but also ensures the lowest cost.
참고문헌 (14)
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