Research On Map-Reduce Energy Consumption Optimization Model Based On BP Neural Network

This paper studies and discusses the energy optimization algorithm of large data Map-Reduce runtime framework.First,the characteristics,running process and working principle of the Map-Reduce framework are introduced and analyzed.After mastering the working principle,the BP neural network algorithm is used to optimize the energy consumption of the system,and the energy consumption monitoring model based on the utilization rate of CPU is used to confirm the experimental results.The main work is divided into the following three aspects:The first is to analyze the operation process of Map-Reduce,compare the research of other scholars,and put forward a Map-Reduce energy consumption monitoring model based on the utilization ratio of CPU.To achieve the optimization of energy consumption,we first need to make a reasonable estimation of the energy consumption generated by parallel computing Map-Reduce framework during operation.Compared with previous scholars,we can find that the mainstream method of building energy monitoring model in the cloud computing environment is the energy consumption model based on the CPU utilization.By analyzing the running process and working principle of the Map-Reduce energy consumption model and combining the changing trend of the CPU utilization rate during the operation,the relationship between the energy consumption and the CPU utilization rate is successfully deduced.And a Map-Reduce operation energy consumption monitoring model based on CPU utilization is proposed.Second: through the study of the BP neural network algorithm,some advantages of the BP neural network in the energy consumption optimization of the Map-Reduce frame are determined,and the optimization details of the BP algorithm are considered from various angles.The selection of energy consumption optimization algorithm,through the introduction of BP neural network and characteristics analysis,on the basis of determining the use of BP neural network to optimize the energy consumption of MapReduce,the original BP neural network is optimized and modified,one is to optimize the structure parameters of BP neural network,and two is to optimize the primary BP neural network with machine learning algorithm.To achieve the most suitable version of the Map-Reduce framework.Third: according to the analysis of Map-Reduce operation energy consumption,two energy optimization models based on BP neural network are put forward.It has been proved by experiments.The optimization model is verified and the optimal operation parameters can be adjusted to achieve the goal of energy consumption optimization.In this paper,the energy consumption optimization model is established by combining the BP neural network of machine learning with the Map-Reduce operation framework.The BP neural network is trained according to the operational task,the system operating parameters and the energy consumption data produced by Map-Reduce.After the training is completed,the energy consumption of Map-Reduce is made.The optimization model can quickly adjust the optimal operation parameter configuration according to the scale of input data.To achieve the purpose of optimizing energy consumption.And the experiment is set up to compare the effect of energy consumption optimization.