RETRACTED ARTICLE: MapReduce-oriented 5G network and information visualization design

Abstract

Since Google proposed the MapReduce programming model, it has been widely used to process big data, and MapReduce is considered to be one of the most efficient tools for processing big data. However, the MapReduce model also has some disadvantages. During the Reduce partitioning process, the default hash part will destroy the overall data. With the rapid development of China’s society and economy, considerable progress has been made in the field of communications, which has completely changed people’s lifestyles. Under this kind of development background, people also put forward higher and higher requirements for communication networks, requiring it to continuously optimize and update services to meet the needs of the times and people. With the advent of the 5G era, it is necessary for us to explore how large operators can better respond to the ever-increasing demand for mobile services. This article considers the problem of scheduling MapReduce tasks. When a user submits a batch of jobs to the cluster, how to determine the execution order of the tasks in the job and to minimize the time required to complete the job? In order to solve this problem, this paper proposes a map task running time prediction model to predict the end time of tasks in the job. In this era of information explosion, people’s lives are inseparable from information. Information can bring many conveniences to food, clothing, housing, and transportation, but information and data flow have also become a heavy burden for people. Information visualization is closely related to people’s lives. Their appearance promotes the transmission of information and contributes to the development of technology, trade, and medical services. Information visualization is attracting more and more social attention.