From statistical‐ to machine learning‐based network traffic prediction. Issue 4 (13th November 2021)

• Record Type: Journal Article
• Authors: Lohrasbinasab, Iraj; Shahraki, Amin; Taherkordi, Amir; Delia Jurcut, Anca
• Other Names: Cheng Xiaochun guestEditor.; Liu Zheli guestEditor.; Ning Yongsheng guestEditor.
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 33:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-13
• Subjects: Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.4394
• Languages: English
• ISSNs: 2161-5748

Abstract

Abstract: Nowadays, due to the exponential and continuous expansion of new paradigms such as Internet of Things (IoT), Internet of Vehicles (IoV) and 6G, the world is witnessing a tremendous and sharp increase of network traffic. In such large‐scale, heterogeneous, and complex networks, the volume of transferred data, as big data, is considered a challenge causing different networking inefficiencies. To overcome these challenges, various techniques are introduced to monitor the performance of networks, called Network Traffic Monitoring and Analysis (NTMA). Network Traffic Prediction (NTP) is a significant subfield of NTMA which is mainly focused on predicting the future of network load and its behavior. NTP techniques can generally be realized in two ways, that is, statistical‐ and Machine Learning (ML)‐based. In this paper, we provide a study on existing NTP techniques through reviewing, investigating, and classifying the recent relevant works conducted in this field. Additionally, we discuss the challenges and future directions of NTP showing that how ML and statistical techniques can be used to solve challenges of NTP. Abstract : In this paper, we provide a study on the existing NTP techniques through reviewing, investigating, and classifying the recent relevant works conducted in this field. We classify the NTP techniques based on statistical‐, ML‐based, and hybrid techniques. Additionally, we discuss the existing challenges and future directions of NTP showing that how ML and statistical techniques can be used to solve challenges of NTP. We propose a schema to integrate statistical‐based techniques and ML techniques to improve the performance of NTP techniques.