An efficient green computing fair resource allocation in cloud computing using modified deep reinforcement learning algorithm

Cloud computing provides services and resources in the Internet, and many applications are self-service-supported, on-demand resource allocation-adapted. These dynamic networks allocated necessary resource to the users' need and they require proper resource allocation scheme. Since various resources are consumed by users if resource allocation is not proper, this leads the system to load imbalance nature. Using Internet-connected devices for storage and computation not only communicates the cloud resources but also connects the devices to network through various protocols. These changes make the network into a complex, dense, heterogeneous system. In this paper, a green computing fair resource allocation through deep reinforcement learning model is proposed to provide efficient resource allocation scheme to the users in the network. Conventional Q-learning model fails in dimensionality problem when the state space increases exponentially. The proposed model is combined with fair resource allocation with deep reinforcement learning to provide better allocation schemes compared to the conventional model.