Cooperative Edge Computing Task Offloading Strategy for Urban Internet of Things

Abstract

With the continuous progress of edge computing technology and the development of the Internet of Things technology, scenarios such as smart transportation, smart home, and smart medical care enable people to enjoy the smart era’s convenience. Simultaneously, with the addition of many smart devices, a large number of tasks are submitted to the edge server, making the edge server unable to meet the needs of completing tasks submitted by the smart device. Besides, if the task is submitted to the remote cloud data center, it increases the user’s additional delay and cost. Therefore, it is necessary to improve the task offloading strategy and resource allocation scheme to solve these problems. This paper first proposes a new task offloading mechanism and then proposes a two-stage Stackelberg game model to solve each participant’s interaction problem in the task offloading mechanism and ensure the maximization of their respective interests. Finally, a theoretical analysis proves the equilibrium of the two-stage Stackelberg game. Experiments are used to prove the effectiveness of the proposed mechanism. Comparative experimental results show that the proposed model can achieve better results regarding delay and energy consumption.

Deep Reinforcement Learning Based Resource Allocation Strategy in Cloud-Edge Computing System

“…Because of the development of collaborative cloud-edge system, works about resource allocation in the cloud-edge system also exist. Wang et al Wang and Wang (2021) proposed an optimization strategy for computing resource allocation of massive IoHT devices in cloud-edge computing environment. Yuan et al Yuan and Zhou (2020) designed a collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and meet the response time constraint.…”