Design And Implementation Of Energy Consumption Data Real-time Acquisition System In High Efficiency Data Center

With the rapid development of the Internet and computers,the national strategy of cloud computing continues to advance,and various data centers used to carry cloud computing tasks are also increasing.One of the major challenges facing the construction and operation of data centers in my country today is the sharp increase in energy consumption of data centers.Compared with the internationally advanced data centers,China's data centers generally have huge energy consumption and extremely low resource utilization.PUE(Power Usage Effectiveness)is used internationally to measure the energy use efficiency of a data center.Compared with foreign countries,the PUE value of data center in China is relatively high,and the degree of greenness is low.Data center energy consumption mainly comes from IT equipment,air conditioning and refrigeration systems,lighting systems,power supply and distribution systems,etc.,and different equipment has different types,interfaces and manufacturers,which leads to complex sources of data center energy consumption data.Data formats and collection methods not the same,which brings huge challenges to the collection and monitoring of energy consumption data in the data center.Therefore,this thesis starts from the real-time collection and load forecasting of data center energy consumption,collects and monitors energy consumption data in real time,and improves energy consumption management.It aims to reduce the PUE value of the data center and improve the overall energy efficiency of the data center.The main work completed in this thesis is as follows:(1)Analyzes the characteristics of energy consumption distribution in China's data center and the principles of various energy consumption data collection,expounds the operation mechanism of the existing monitoring system at home and abroad,and summarizes and analyzes the shortcomings.(2)Study high-frequency,fine-grained sensing data collection,aggregation and synchronization mechanisms for massive measurement points;define scalable and consistent data collection interfaces to shield the heterogeneity of resources and achieve the purpose of collecting diverse and heterogeneous energy consumption data.(3)Establish real-time energy consumption data collection methods and models to study the impact of different types of loads on data center energy consumption;amulti-step online prediction framework for host load based on deep recurrent neural network is proposed to achieve host load prediction.(4)Design and implement the overall architecture of the energy consumption data collection system,and deploy and verify it.This thesis designs and implements a high-performance data center real-time data collection system for energy consumption data,and quantifies the data center's resource consumption in terms of energy consumption data collection and host load prediction.Effectively realize the real-time collection of data center energy consumption data in the data center,and provide data support for monitoring and evaluating data center energy consumption and efficient energy management.