LEARNING ADMISSION POLICY FOR OPTIMIZING QUALITY OF SERVICE OF COMPUTING RESOURCES NETWORKS

摘要

A system for learning admission policy for optimizing quality of service of computer resources networks is provided herein. The system includes a statistical data extractor configured to extract historical data of deployment requests issued to an admission unit of a computer resources network. The system further includes a Markov decision process simulator configured to generate a simulation model based on the extracted historical data and resources specifications of the computer resources network, in terms of a Markov decision process. The system further includes a value function generator configured to determine a value function for deployment requests admissions. The system further includes a machine learning unit configured to train a classifier based on the simulation model and the value function, to yield an admission policy usable for processing incoming deployment requests.