No abstract

In the application scenario of the smart factory, the computing tasks of smart devices are intensive and the tasks are sensitive to delay. The smart device needs to offload the task to the edge server for execution. However, the computing resources of edge servers are limited and there will be workload imbalance among multiple edge servers. To solve these problems, we propose a joint optimization scheme of task caching and offloading based on task scene awareness, which decouples the joint optimization problem of task caching and offloading into two subproblems: task caching and task offloading. For the task caching subproblem, a bilateral auction task caching algorithm based on task scene awareness is proposed. For the task offloading subproblem, a task offloading algorithm based on task scenario offloading preferences is proposed. The experimental results show that the optimization scheme proposed in this paper has obvious performance advantages when the number of tasks is large and the number of intelligent devices is large. This scheme also achieves workload balancing among multiple edge servers.

In the application scenario of the smart factory, the computing tasks of smart devices are intensive and the tasks are sensitive to delay. The smart device needs to offload the task to the edge server for execution. However, the computing resources of edge servers are limited and there will be workload imbalance among multiple edge servers. To solve these problems, we propose a joint optimization scheme of task caching and offloading based on task scene awareness, which decouples the joint optimization problem of task caching and offloading into two subproblems: task caching and task offloading. For the task caching subproblem, a bilateral auction task caching algorithm based on task scene awareness is proposed. For the task offloading subproblem, a task offloading algorithm based on task scenario offloading preferences is proposed. The experimental results show that the optimization scheme proposed in this paper has obvious performance advantages when the number of tasks is large and the number of intelligent devices is large. This scheme also achieves workload balancing among multiple edge servers.