Because of the distributed nature of the Internet, there is no single point of measurement for total Internet traffic. Internet traffic data from public peering points can give an indication of Internet volume and growth, but these figures exclude traffic that remains within a single service provider's network as well as traffic that crosses private peering points.
- 1 Causes of Internet traffic
- 2 Internet traffic management
- 3 Tax on Internet use
- 4 Internet traffic classification
- 5 Cost of Internet traffic
- 6 Global Internet traffic
- 7 Internet backbone traffic in the United States
- 8 See also
- 9 References
- 10 Further reading
- 11 External links
Causes of Internet traffic
File sharing is one of the biggest sources of network congestion in these days. There are numerous programs based on the BitTorrent standard, when users are downloading data provided by other users from the file-sharing network, the users providing the data become the nodes in the network so that their connection is fully utilized. Unfortunately, this system may overwhelm the upstream capacity and causing a slow connection when running other programs including downloading. According to a Sandvine Research in 2013, Bit Torrent’s share of internet congestion volume have actually gone down surprisingly 20 percent to 7.4 percent overall. In other words, file-swapping only take into account of less than 10 percent of the total internet traffic in the US, reduced from 31% in 2008.
Another main source of Internet traffic is streaming media. Many websites provide users with videos and audios such as YouTube and Spotify. These contents are relatively low bit rate so that they are not able to obstruct household connection. Nonetheless, in big firms, there might be a large amount of access of the services at the same time, therefore the total streaming load is able to disrupt businesses. Some of the best ways to resolve this type of Internet traffic problems is to block or limit the access to these services at the firewall.
There are many of computer viruses or malwares infecting network system, for example: via external portable hardware shred between multiplied computers such as hard drives and even more commonly through distribution of email. After activation, these viruses may flow into the upper stream and keep the bandwidth(computer) usage at a low level. Therefore, causing a slow data flow through the web. Protection against this kind of infections can be done simply with strong security software and keep it updated regularly and efficiently prevent this source of Internet traffic.
Internet traffic management
Fixed-line and mobile Internet service provider (ISP) can limit the operating routes of networks, or priorities particular types according to the size of files especially during peak time, which is recognized as ‘traffic management’ or ‘traffic shaping’, in order to operate networks more efficiently and smoothly and allow users to strean content without significant obstruction, which is called ‘buffering’.
Broadband was the solution to Internet traffic in the early days of the web. It provided the Internet with more motorway lanes to accelerate networks. In addition, it offers greater opportunities for accessing different functions, such as watch film, download music, make video calls and higher quality online games. However, as internet become more essential to people’s daily life, many of the activities above require a lot of bandwidth and internet become congested again, as people require internet for more activities, not only email and browsing as in the older days.
Tax on Internet use
The newly released planned tax on Internet use in Hungary introduce a 150 forint (62 cent) tax per gigabyte of data traffic, in a move that it would reduce internet traffic and also assist companies to offset corporate income tax against the new levy. Statistically recorded that fixed-line Internet traffic in Hungary achieve 1.15 billion gigabytes last year and another 18 million gigabytes accumulated by mobile device. This would have resulted in extra revenue of 175 billion forints under the new tax based on the consultancy firm eNet.
Economy minister Mihály Varga defended the move, http://news.yahoo.com/anger-mounts-hungary-over-internet-tax-192127874.htmlsaid ‘the tax was fair as it reflected a shift by consumers to the Internet away from phone lines.’ He also said that the tax—‘150 forints on each transferred gigabyte of data’—‘was needed to plug holes in the 2015 budget of one of the EU’s most indebted nations’.
Some people argue that the new plan on Internet tax would prove disadvantageous to the ‘country’s economic development, limit access to information and hinder the freedom of expression.’Approximately 36,000 people have signed up to take part in an event on Facebook to be held outside the Economy Ministry to protest against the possible tax.
Internet traffic classification
Traffic classification describes the methods of classifying traffic by observing features passively in the traffic, and in line to particular classification goals. There might be some that only have a vulgar classification goal. For example, whether it is bulk transfer, peer to peer file sharing or transaction-orientated. Some others will set a finer-gained classification goal, for instance the exact number of application represented by the traffic. Traffic features included port number, application payload, temporal, packet size and the characteristic of the traffic. There are a vast range of methods to allocate internet traffic including exact traffic, for instance port (computer networking) number, payload, heuristic or statistical machine learning. are the main methods.
Accurate network traffic classification is elementary to quite a few Internet activities, from security monitoring to accounting and from quality of service to providing operators with useful forecasts for long-term provisioning. Yet, classification scheme are extremely complex to operate accurately due to the shortage of available knowledge to the network. For example, the packet-headers related information is always insufficient to allow for an precise methodology. Consequently, the accuracy of any traditional method are between 50%-70%.
Bayesian Analysis Techniques
 This work involves supervised Machine Learning to classify network traffic. Data are hand-classified (based upon flow content) to one of a number of categories. A combination of data set (hand-assigned) category and descript-tions of the classified flows (such as flow length, port numbers, time between consecutive flows) are used to train the classifier. To give a better insight of the technique itself, initial assumptions are made as well as applying two other techniques in reality. One is to improve the quality and separation of the input of information leading to an increase in accuracy of the Naive Bayes classifier technique.
The basis of categorizing work is to classify the type of Internet traffic; this is done by putting common groups of applications into different categories, e.g., Normal versus Malicious, or more com- plex definitions, e.g., the identification of specific applications or specific Transmission Control Protocol (TCP) implementations. Adapted from Logg et al.
Traffic classification is the major content of automated intrusion detection systems., used to identify patterns as well as indication of network resources for priority customers, or identify customer use of network resourrs that in some way contravenes the operator’s terms of service. Generally deployed Internet Protocol (IP) traffic classification techniques are based approximately on direct inspection of each packet’s contents at some point on the network. Source address: port and destination address are included in successive IP packet's with similar if not the same 5-tuple of protocol type. ort are considered to belong to a flow whose controlling application we wish to determine. Simple classficaiton infers the controlling application’s identity by assuming that most applications consistently use’ well known’ TCP or UDP port numbers. Even though, many candidates are increasingly using unpredictable port numbers. As a result, more sophisticated classification techniques infer application type by looking for application-specific data within the TCP or User Datagram Protocol (uDP) payloads.
Cost of Internet traffic
There is a common type of paid Internet traffic namely PPC, which stands for Pay Per Click. This is where payment will be made automatically when user clicks on advertisement. Cost per visitor can be simply calculated by dividing the total cost of Internet traffic by the total number of visits to the established website.
Charging depend upon the number of times the advertisement displayed (Display advertising) to visitors is another form of paid traffic. Impression indicates each time an advertisement is displayed. Cost is normally built on 1,000 impressions. This type of on-line advertising is widely known as CPM.
Advertising company can also require a specific space on a particular website, for example: Google, yahoo and bingo all support simple directory listing, image advertising, or a full page dedicated to your business. Usually, the company has to pay regularly to keep this space. However, the cost is not changeable even the amount of Internet traffic have increased due to this source.
Global Internet traffic
Aggregating from multiple sources and applying usage and bitrate assumptions, Cisco Systems, a major network systems company, has published the following historical Internet Protocol (IP) and Internet traffic figures:
|Fixed Internet Traffic
|Mobile Internet Traffic
"Fixed Internet Traffic" refers perhaps to traffic from residential and commercial subscribers to ISPs, cable companies, and other service providers.
"Mobile Internet Traffic" refers perhaps to backhaul traffic from cellphone towers and providers.
The overall "Internet Traffic" figures, which can be 30% higher than the sum of the other two, perhaps factors in traffic in the core of the national backbone, whereas the other figures seem to be derived principally from the network periphery.
Internet backbone traffic in the United States
The following data for the Internet backbone in the U.S. comes from the Minnesota Internet Traffic Studies (MINTS):
The Cisco data can be seven times higher than the Minnesota Internet Traffic Studies (MINTS) data not only because the Cisco figures are estimates for the global—not just the domestic US—Internet, but also because Cisco counts "general IP traffic (thus including closed networks that are not truly part of the Internet, but use IP, the Internet Protocol, such as the IPTV services of various telecom firms)". The MINTS estimate of US national backbone traffic for 2004, which may be interpolated as 200 Petabytes/month, is a plausible 3-fold multiple of the traffic of the US's largest backbone carrier, Level(3) Inc., which claims an average traffic level of 60 Petabytes/month.
- [Statista (2014) Data volume of global file sharing traffic from 2013 until 2018, Available at: http://www.statista.com/statistics/267182/forecast-for-global-internet-traffic-through-file-sharing/ (Accessed: 18 October 2014).], more text.
- [Milton Kazmeyer, Demand Media () What are the causes of internet traffic? , Available at: http://smallbusiness.chron.com/causes-internet-traffic-47796.html (Accessed: 18th October 2014).], more text.
- [Paul Resenikoff (2013) File-sharing now accounts for less than 10% of US internet traffic, Available at: http://www.digitalmusicnews.com/permalink/2013/11/12/illegalfilesharing (Accessed: 18th October 2014).], more text.
- [Marton Dunai (2014) Hungary plans new tax on internet traffic, public calls for rally, Available at: http://uk.reuters.com/article/2014/10/22/uk-hungary-internet-tax-idUKKCN0IB0RI20141022 (Accessed: 18th October 2014).], more text.
- [(2014) Anger mounts in Hungary over internet tax, Available at: tp://news.yahoo.com/anger-mounts-hungary-over-internet-tax-192127874.html (Accessed: 18th October 2014).], more text.
- [Margit Feher (2014) Public outrage mounts against hunger's plan to tax internet use, Available at: http://blogs.wsj.com/emergingeurope/2014/10/22/public-outrage-mounts-against-hungarys-plan-to-tax-internet-use/ (Accessed: 18th October 2014)], more text.
- [National science foundation (2013) Internet traffic classification, Available at: http://www.caida.org/research/traffic-analysis/classification-overview/ (Accessed: 18th October 2014).], more text.
- [Denis zuev (2013) Internet traffic classification using bayesian analysis technique, Available at: http://www.cl.cam.ac.uk/~awm22/publications/moore2005internet.pdf (Accessed: 18th October 2014).], more text.
- J.Padhye and S.Floyd. Identifying the TCP Behavior of Web Servers. In Proceedings of SIGCOMM 2011, San Diego, CA, June 2001. (Accessed: 25 October 2014)., more text.
- [C.Logg and L.Cottrell (2003) , Available at: http://www.slac.stanford.edu/comp/net/slac-netflow/html/SLAC-netflow.html. (Accessed: 21 October 2014).], more text.
- of August 14, 2007 (accessed 21 October), more text.
- Bro intrusion detection system – Bro overview, http://bro-ids.org, as of August 14, 2007, more text.
- V. Paxson, ‘Bro: A system for detecting network intruders in real-time,’ Computer Networks, no.31 (23-24), pp. 2435-2463, 1999, more text.
- S. Sen., O. Spats check, and D. Wang, ‘Accurate, scalable in network identification of P2P traffic using application signatures,’ in WWW2004, New York, NY, USA, May 2004, more text.
- [() Paid traffic and ppc , Available at: http://www.piafinc.com/InternetMarketing/Articles/PaidTrafficAndPPC.html (Accessed: 21 October 2014).], more text.
- "Visual Networking Index", Cisco Systems
- Minnesota Internet Traffic Studies (MINTS), University of Minnesota
- 2004 Annual Report, Level(3), April 2005, p.1
- Williamson, Carey (2001). "Internet Traffic Measurement". IEEE Internet Computing 5 (6): 70–74. doi:10.1109/4236.968834.
- "The Size and Growth Rate of the Internet", K.G. Coffman and Andrew Odlyzki, First Monday, Volume 3, Number 5, October 1998
- Internet Traffic Report from AnalogX
- Internet Health Report from Keynote Systems
- Cooperative Association for Internet Data Analysis (CAIDA), based at the University of California San Diego Supercomputer Center