Smart resource allocation using reinforcement learning in content-centric cyber-physical systems

The exponential growing rate of the networking technologies has led to a dramatical large scope of the connected computing environment. As a novel computing deployment, Cyber-Physical Systems (CPSs) are considered an alternative for achieving high performance by the enhanced capabilities in system controls, resource allocations, data exchanges, and flexible adoptions. However, current CPS is encountering the bottleneck concerning the resource allocation due to the mismatching networking service quality and complicated service offering environments. The concept of Quality of Experience (QoE) in networks further increases the demand for intensifying intelligent resource allocations to satisfy distinct user groups in a dynamic manner. This paper concentrates on the issue of resource allocations in CPS and also considers the satisfactory of QoE in content-centric computing systems. A novel approach is proposed by this work, which utilizes the mechanism of reinforcement learning to obtain high accurate QoE in resource allocations. The assessments of the proposed approach were processed by both theoretical proofs and experimental evaluations.