2012/08/09 by Poo Kuan Hoong, Hoong, Poo Kuan, Ian K. T. Tan +3 · 2 citations
Computer Science · #FOS: Computer and information sciences #Network Security and Intrusion Detection #Network Traffic and Congestion Control #Networking and Internet Architecture (cs.NI) #Peer-to-Peer Network Technologies #cs.NI
paper · pdf · doi:10.48550/arxiv.1208.1896
14 pages
arxiv created 2012/08/09 · openalex publication_date 2012/08/09 · arxiv updated 2012/08/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In recent years, there are some major changes in the way content is being distributed over the network. The content distribution techniques have recently started to embrace peer-to-peer (P2P) systems as an alternative to the traditional client-server architecture. P2P systemsthat are based on the BitTorrent protocol uses end-users' resources to provide a cost effective distribution of bandwidth intensive content to thousands of users. The BitTorrent protocol system offers a scalable mechanism for distributing a large volume of data to a set of peers over the Internet. With the growing demand for file sharing and content distribution, BitTorrent has become one of the most popular Internet applications and contributes to a signification fraction of the Internet traffic. With the wide usage of the BitTorrent protocol system, it has basically solved one of the major problems where data can be quickly transferred to a group of interested parties. The strength of the BitTorrent protocol lies in efficient bandwidth utilization for the downloading and uploading processes. However, the usage of BitTorrent protocol also causes latency for other applications in terms of network bandwidth which in turn has caused concerns for the Internet Service Providers, who strives for quality of service for all their customers. In this paper, we study the network traffic patterns of theBitTorrent network traffic and investigate its behavior by usingthe time series ARMA model. Our experimental results show that BitTorrent network traffic can be modeled and forecasted by using ARMA models. We compared and evaluated the forecasted network traffic with the real traffic patterns. This modeling can be utilized by the Internet Service Providers to manage their network bandwidth and also detect any abnormality in their network.