Deep Reinforcement Learning Based Resource Allocation Strategy in Cloud-Edge Computing System - Xu, Jianqiao; Xu, Zhuohan; Shi, Bing (Frontiers in Bioengineering and Biotechnology, 2022)
The rapid development of mobile devices applications has put tremendous pressure on edge nodes with limited computing capabilities, which may cause poor user experience. To solve this problem, collaborative cloud-edge computing is proposed. In the cloud-edge computing, a edge node with limited local resources can rent more resources from a cloud node. According to the character of cloud service, cloud service can be divided into private cloud and public cloud. In a private cloud environment, edge node needs to allocates resources between the cloud node and the edge node. In a public cloud environment, since public cloud service providers offer a variety of pricing modes for users’ different computing demands, edge node also needs to select appropriate pricing mode of cloud service. It is a sequential decision problem. In this paper, we model it as a Markov Decision Process and Parameterized Action Markov Decision Process, and propose a resource allocation algorithm CERAI and CERAU in the collaborative cloud-edge environment based on the deep reinforcement learning algorithm DDPG and P-DQN. We evaluate CERAI and CERAU against three typical resource allocation algorithms based on synthetic data and real data of Google dataset. The experimental results show that CERAI and CERACE can effectively reduce the long-term operation cost of collaborative cloud-side computing in various demanding settings. Our analysis can provide some useful insights for enterprises to design the resource allocation strategy in the collaborative cloud-side computing system.