# Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
## 1 Introduction  
The Internet has become a fundamental component in people's daily life. More and more users access the Internet through different kinds of networked devices, leading to an exhaustion of IPv4 addresses. Therefore, Internet service providers (ISPs) have gradually increased IPv6 usage [1]. As of February 2022, the percentage of users that access online services using IPv6 has exceeded 35% of all Internet users [2]. A key difference between IPv6 and IPv4 is the size of the address space. An IPv6 address has 128 bits, while an IPv4 address has only 32 bits. As a result, IPv6 has many critical advantages, such as significantly expanding the total number of IP addresses and providing better network services. Moreover, IPv6's privacy extensions can resist hostile tracking for better privacy protection [3]. However, the incompatibility between IPv6 and IPv4 and equipment replacement costs have hindered the deployment of IPv6. Although there are several transition mechanisms helping ISPs migrate IPv4 to IPv6, such as dual-stack [4], tunnel [5], and translation technologies [6], it will take a long time to replace all IPv4 addresses with IPv6 addresses. The study of IPv6 networks will help us understand the Internet's current development and speed up the popularization of IPv6 networks. The China Education and Research Network (CERNET), an ISP serving thousands of universities in China, is one of the early adopters of IPv6. Studying the characteristics of IPv6-based campus network traffic in CERNET will provide insights into understanding IPv6 users' behavior and shed light on the further deployment and management of IPv6. In this paper, we collect the network traffic volume of two universities from CERNET for research. The two main research questions of this paper are shown as follows:RQ1. What are the temporal characteristics of the IPv6 traffic volume? RQ2. How to accurately predict the IPv6 traffic volume? Analyzing and predicting network traffic is beneficial for network resource management and anomaly monitoring [7; 8]. Even though many prior works have examined IPv6 network characteristics [9; 10; 11], we still do not have a clear picture of the recent IPv6 network traffic. The network traffic volume can intuitively reflect the users' demand, which is an important consideration in network resource management [12]. To address RQ1, we have conducted a comprehensive traffic volume analysis of the current situation of IPv6 campus networks. The analysis contains weekday/weekend patterns, the self-similarity phenomenon, and the correlation between IPv6 and IPv4 traffic. Moreover, it is worth noticing that traffic prediction is a time series prediction problem. There are many studies on time series forecasting, and their methods are broadly divided into three categories--statistical methods, machine learning methods, and hybrid methods. One of the most representative statistical methods is the auto-regressive integrated moving average (ARIMA) [13], while the long short-term memory neural network (LSTM) [14] is a popular machine learning method in solving time series forecasting problems. The hybrid method is a combination of statistical methods and machine learning methods. For the traffic forecasting problem, there have been studies [15; 16; 17] using these three categories of methods. Although these studies have improved the prediction accuracy to a certain extent, there is a lack of a proper prediction method for IPv6 traffic. To answer RQ2, we consider the correlation between IPv6 and IPv4 traffic and leverage the advantages of both statistical and machine learning methods. A new model is proposed to achieve a better IPv6 traffic volume prediction than existing methods. The contributions of this paper are summarized as follows:  
* This paper starts with analyzing the IPv6 traffic characteristics of two universities in Shanghai, i.e., Donghua University (DHU) and East China Normal University (ECNU).