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

Abstract

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, an edge node with limited local resources can rent more resources from a cloud node. Since cloud service providers offer a variety of pricing modes for users' different computing demands, edge node needs to select appropriate pricing mode of cloud service and allocates resources between the cloud node and the edge node. It is a sequential decision problem. In this paper, we model it as a Parameterized Action Markov Decision Process, and propose a resource allocation algorithm Cost Efficient Resource Allocation under collaborative Cloud-Edge (CERACE) based on the deep reinforcement learning algorithm Parametrized Deep Q-learning (P-DQN). We evaluate CERACE against three typical resource allocation algorithms Edge First + On Demand (E+O), Edge + Random (E+R) and Random + Random (R+R) based on synthetic data and real data of Google dataset. The experimental results show that 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.