An Agent-based Model for Resource Provisioning and Task Scheduling in Cloud Computing Using DRL

Abstract

The Resource Provisioning (RP) and Task Scheduling (TS) issues has become an attractive paradigms in cloud industry, this is due to the increasing demand for the services provided by virtual machines that are structured by physical servers owned by the data centers of cloud service providers (CSPs). In this paper, we propose a new model based on multi-agent system for the RP and TS reducing the cost of energy using Deep Reinforcement Learning DRL. A Quantile Regression Deep Q Network (QR-DQN) algorithm generates an appropriate policy and the optimal long-term decisions. A set of experiments show the efficiency of our proposed scheduling approach and the performance of our task allocation method..