AIAssisted KnowledgeDefined Network Orchestration for EnergyEfficient Data Center Networks In this article, we discuss the design and implementation of a novel DCN system, which utilizes a knowledgedefined NOM to operate a HOEDCN costeffectively and energyefficiently. The motivations behind the proposed HOEDCN system are the urgent need to address the scalability, energy, and manageability issues in existing DCN systems. To realize the knowledgedefined NOM, we follow the principle of predictive analytics in the human brain to design three artificial intelligence modules based on deep learning and make them operate collaboratively. The proposed HOEDCN system is implemented in a network testbed, and we conduct experiments that involve both control and data plane operations to demonstrate its advantages. The experimental results show that the HOEDCN simultaneously achieves highperformance service provisioning and improved energy efficiency. Furthermore, by analyzing the pros and cons of the HOEDCN system, we also point out several directions to work on in the future.

A Deep Neural Network based Approach to Energy Efficiency Analysis for Cloud Data Center The energy consumption growth of the Information and Communication Technology (ICT) sector contributes to almost 2% of the global carbon footprint with an estimated trend of 3xe2x88x923.6% by 2020. Most of this growth (45%) can be attributed to data centers (DC) which now represent the core infrastructure for different industries. Furthermore, cloud DCs are complex systems composed of several ICT and nonICT (i.e. mechanical and electrical) subsystems. The variety of configurations and the interdependencies of the different DC subsystems leads to enormous challenges in understanding and optimizing DC energy efficiency based on the Power Usage Effectiveness (PUE) metric. Within this context, we focus in this work on analyzing the behavior of Deep Neural Network (DNN)based model to predict the DC energy efficiency metric (PUE). In fact, the proposed model is used to evaluate the impact of various DC subsystems on energy efficiency. Through an experimentation with real datasets from a real DC, we observed that DNNbased model achieves a good Root Mean Square Error (RMSE). The obtained results of this experimentation indicate that our proposed DNNbased model improves the PUE optimization, and consequently, shows its promise for a practical implementation.