Speed is a critical factor in Internet traffic. When speed increases, users stream and download greater volumes of content, and adaptive bit-rate streaming increases bit rates automatically according to available bandwidth. Service providers find that users with greater bandwidth generate more traffic. By 2022, households with high-speed fiber connectivity will generate 23 percent more traffic than households connected by DSL or cable broadband, globally (Figure 26). The average FTTH household generated 86 GB per month in 2017 and will generate 264 GB per month by 2022.
A few countries also have users that currently experience greater than 125 Mbps, paving the path for the future demands of video. Video continues to be of enormous demand in today’s home, but there will be significant bandwidth demands with the video application requirements of the future, even beyond the forecast period of 2022. In Figure 19, a scenario with video applications of the future is explored; today’s bandwidth needs are a sliver of the future needs.
The Cisco Visual Networking Index Forecast methodology has been developed based on a combination of analyst projections, in-house estimates and forecasts, and direct data collection. The analyst projections for broadband connections, video subscribers, mobile connections, and Internet application adoption come from SNL Kagan, Ovum, IDC, Gartner, Ookla Speedtest.net, Strategy Analytics, Dell’Oro Group, Synergy, comScore, Nielsen, Maravedis, ACG Research, ABI Research, Media Partners Asia, IHS, International Telecommunications Union (ITU), CTIA, UN, telecommunications regulators, and others. Upon this foundation are layered Cisco’s own estimates for application adoption, minutes of use, and kilobytes per minute. The adoption, usage, and bit-rate assumptions are tied to fundamental enablers such as broadband speed and computing speed. All usage and traffic results are then validated using data shared with Cisco from service providers. Figure 28 shows the forecast methodology.
Work involving 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 descriptions 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.
Consider how long it takes to download an HD movie at these speeds: at 10 Mbps, it takes 20 minutes; at 25 Mbps, it takes 9 minutes; but at 100 Mbps, it takes only 2 minutes. High-bandwidth speeds will be essential to support consumer cloud storage, making the download of large multimedia files as fast as a transfer from a hard drive. Table 5 shows the percentage of broadband connections that will be faster than 10 Mbps, 25 Mbps, and 50 Mbps by region.
After the number of Internet video users has been established, the number of users for each video subsegment must be estimated. It was assumed that all Internet video users view short-form video in addition to other forms of video they may watch. The number of Internet video users who watch long-form video (based partially on comScore Video Metrix figures for video sites whose average viewing time is longer than 5 minutes), live video, ambient video, and Internet Personal Video Recorder (PVR) is estimated.
"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.
Traffic classification is a major component of automated intrusion detection systems. They are used to identify patterns as well as indication of network resources for priority customers, or identify customer use of network resources 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 classification 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.
Although average Internet traffic has settled into a steady growth pattern, busy hour traffic (or traffic in the busiest 60 minute period of the day) continues to grow more rapidly than average Internet traffic. Service providers plan network capacity according to peak rates rather than average rates. Between 2017 and 2022, global busy hour Internet use will grow at a CAGR of 37 percent, compared with 30 percent for average Internet traffic (Figure 23).
Many voucher code web sites use a click-to-reveal format, which requires the web site user to click to reveal the voucher code. The action of clicking places the cookie on the website visitor's computer. In the United Kingdom, the IAB Affiliate Council under chair Matt Bailey announced regulations that stated that "Affiliates must not use a mechanism whereby users are encouraged to click to interact with content where it is unclear or confusing what the outcome will be."
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