Network Traffic Statistics Method for Resource-Constrained Industrial Project Group Scheduling under Big Data

Zongjie Huo,Pei Pei,Wei Zhu,Wenqing Wu

doi:10.1155/2021/5594663

Abstract

With the advent of the Internet era, the demand for network in various fields is growing, and network applications are increasingly rich, which brings new challenges to network traffic statistics. How to carry out network traffic statistics efficiently and accurately has become the focus of research. Although the current research results are many, they are not very ideal. Based on the era background of big data and machine learning algorithm, this paper uses the ant colony algorithm to solve the typical resource-constrained project scheduling problem and finds the optimal solution of network traffic resource allocation problem. Firstly, the objective function and mathematical model of the resource-constrained project scheduling problem are established, and the ant colony algorithm is used for optimization. Then, the project scheduling problem in PSPLIB is introduced, which contains 10 tasks and 1 renewable resource. The mathematical model and ant colony algorithm are used to solve the resource-constrained project scheduling problem. Finally, the data quantity and frequency of a PCU with a busy hour IP of 112.58.14.66 are analyzed and counted. The experimental results show that the algorithm can get the unique optimal solution after the 94th generation, which shows that the parameters set in the solution method are appropriate and the optimal solution can be obtained. The schedule of each task in the optimal scheduling scheme is very compact and reasonable. The peak time of network traffic is usually between 9 : 00 and 19 : 00-21 : 00. We can reasonably schedule the network resources according to these time periods. Therefore, the network traffic statistics method based on the solution of resource constrained industrial project group scheduling problem under big data can effectively carry out network traffic statistics and trend analysis.

Highlights

It is innovative to take network traffic as a renewable resource to solve the optimal solution of project scheduling, which can provide new ideas for the statistics and analysis of network traffic
The optimal scheduling scheme is shown in Figure 4: Based on the above plan, the starting time and completion time of each task are planned, and the specific arrangement is as follows
The effective and accurate network traffic statistics method can determine whether the equipment is likely to fail and can plan the market according to the distribution and type of data traffic

Summary

Introduction

With the increasing coverage of the Internet, the Internet has been widely used in various fields, and the demand for network construction is growing. A network connection is becoming more and more complex, and the cost of network interconnection is gradually increasing [1]. The statistical analysis of network traffic is very important, so it is imperative to explore a new efficient and convenient network traffic statistics method. In the era of big data, there is a new solution to the resource-constrained project scheduling problem. On this basis, it is innovative to take network traffic as a renewable resource to solve the optimal solution of project scheduling, which can provide new ideas for the statistics and analysis of network traffic

Methods

Results

Conclusion