In the first half of 2021, video accounted for over half of global traffic. Social occupied the next largest share at 12.7 percent, while web browsing accounted for around a tenth. Audio accounted for only 0.31 percent of traffic worldwide.

This statistic gives information on the consumer internet data traffic worldwide from 2016 to 2022, by application category. In 2017, the global consumer data traffic from internet video amounted to 56 exabytes per month.

The average daily internet traffic per capita in Romania has been increasing over the observed period, for both broadband and mobile internet connections. As a result, the average daily mobile internet traffic per person was around 412 megabytes in the first half of 2024.

Streaming is by far the predominant type of traffic in communication networks. With thispublic dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3G/4G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases.

The statistic shows estimated internet data traffic per month in the United States from 2018 to 2023. In 2018, total internet data traffic was estimated to amount to 33.45 million exabytes per month.

In January 2025 mobile devices excluding tablets accounted for over 62 percent of web page views worldwide. Meanwhile, over 75 percent of webpage views in Africa were generated via mobile. In contrast, just over half of web traffic in North America still took place via desktop connections with mobile only accounting for 51.1 percent of total web traffic. While regional infrastructure remains an important factor in broadband vs. mobile coverage, most of the world has had their eyes on the recent 5G rollout across the globe, spearheaded by tech-leaders China and the United States. The number of mobile 5G subscriptions worldwide is forecast to reach more than 8 billion by 2028. Social media: room for growth in Africa and southern Asia Overall, more than 92 percent of the world’s mobile internet subscribers are also active on social media. A fast-growing market, with newcomers such as TikTok taking the world by storm, marketers have been cashing in on social media’s reach. Overall, social media penetration is highest in Europe and America while in Africa and southern Asia, there is still room for growth. As of 2021, Facebook and Google-owned YouTube are the most popular social media platforms worldwide. Facebook and Instagram are most effective With nearly 3 billion users, it is no wonder that Facebook remains the social media avenue of choice for the majority of marketers across the world. Instagram, meanwhile, was the second most popular outlet. Both platforms are low-cost and support short-form content, known for its universal consumer appeal and answering to the most important benefits of using these kind of platforms for business and advertising purposes.

This resource includes input data used in the work "Machine-Learning Based Prediction of Multiple Types of Network Traffic" by Aleksandra Knapińska, Piotr Lechowicz, and Krzysztof Walkowiak; published in International Conference on Computational Science (ICCS) 2021, Lecture Notes in Computer Science, vol 12742. pp. 122-136. Springer, Cham. https://doi.org/10.1007/978-3-030-77961-0_12 The work was supported by the National Science Centre, Poland, under Grant 2019/35/B/ST7/04272. Both seattle_november.xml and seattle_december.xml files include internet traffic data from Seattle Internet Exchange Point. The european.xml file includes internet traffic data from one of the European Internet Exchange Points. Each file includes the traffic volume decomposed into specific frame size ranges. Each file starts with a metadata section providing general information. The period covered by a specific file is indicated by its 'start' and 'end' tags. They provide Unix timestamps in the GMT timezone. It should be noted that Seattle lies in the PST time zone, and the European IXP is located in the CET timezone, so the start and end times should be adjusted accordingly. The step parameter is given in seconds, so the samples are stored every 5 minutes in all three files. Each file has multiple columns providing traffic data in bits per second for different frame size ranges. Column names specify the ranges in bytes. The 'total' column stores information about the total aggregate traffic volume, which is a sum of values in all the remaining columns in each row.

Mobile Data Traffic Market Outlook

The global mobile data traffic market size was estimated at approximately USD 68 billion in 2023 and is projected to surge to about USD 320 billion by 2032, exhibiting a remarkable compound annual growth rate (CAGR) of 18.5% over the forecast period. This growth is driven by the increasing penetration of smartphones, advancements in network technologies, and the rising consumption of data-intensive applications and services.

One of the primary growth factors for the mobile data traffic market is the rapid expansion of the smartphone user base globally. As smartphones become more affordable and accessible, especially in emerging markets, the number of mobile internet users is skyrocketing. This trend is further amplified by the increasing availability of high-speed mobile networks, which make data-heavy applications such as video streaming and online gaming more feasible and attractive to users. The proliferation of affordable data plans is also encouraging users to consume more mobile data, thereby bolstering market growth.

Another significant driver of growth is the continuous evolution of network technologies. The transition from 3G to 4G, and now to 5G, has significantly enhanced data transmission speeds and network capabilities. 5G technology, in particular, promises ultra-low latency, higher capacity, and faster download and upload speeds, which are expected to revolutionize various sectors such as healthcare, automotive, and smart cities. The deployment and adoption of 5G networks are anticipated to boost mobile data traffic volumes exponentially, as it facilitates the seamless use of high-bandwidth applications, including augmented reality (AR), virtual reality (VR), and Internet of Things (IoT) devices.

The increase in video content consumption is also a major factor driving the market. Video traffic accounts for a substantial portion of mobile data usage, driven by platforms like YouTube, Netflix, and social media sites that prioritize video content. The trend of live streaming and video-on-demand services is creating a massive surge in data traffic, with users increasingly accessing high-definition (HD) and even 4K content. Moreover, the COVID-19 pandemic has accelerated the adoption of digital entertainment and online education, further increasing the demand for mobile data.

Regionally, the growth of mobile data traffic is witnessing variations with Asia Pacific leading the charge. The region's high population density, coupled with increasing urbanization and smartphone penetration, makes it a significant contributor to global data traffic. Countries like China and India are at the forefront, driven by government initiatives to promote digitalization and the rollout of advanced mobile networks. North America and Europe are also substantial markets due to their well-established network infrastructure and early adoption of new technologies. However, the growth rates in these regions are relatively moderate compared to the exponential growth seen in Asia Pacific and Latin America.

Traffic Type Analysis

The mobile data traffic market can be segmented by traffic type into video, audio, data, and others. Video traffic is the most dominant segment, accounting for the largest share of mobile data usage worldwide. The proliferation of video streaming services, alongside user-generated video content on social media platforms, significantly contributes to this dominance. As more users switch to high-definition and 4K streaming, the demand for data-intensive video content continues to rise. Additionally, the growing popularity of live streaming and video calls, particularly in the context of remote work and online education, further propels this segment's growth.

Audio traffic also plays a significant role in the mobile data traffic market. The increasing usage of music streaming services such as Spotify, Apple Music, and various podcast platforms are driving the growth of this segment. The trend of consuming audio content on the go, facilitated by improved network speeds and unlimited data plans, is contributing to a steady rise in mobile data traffic from audio services. Furthermore, the adoption of smart speakers and voice assistant technologies is expected to continue bolstering this segment.

Data traffic, encompassing all forms of non-visual and non-audio data, is another crucial segment. This includes browsing, app usage, emails, and other types of data transmission over mobile networks. With the increasing reliance on mobile applications for a wide array of activities—ra

This resource includes input data used in the work "Long-term prediction of multiple types of time-varying network traffic using chunk-based ensemble learning" by Aleksandra Knapińska, Piotr Lechowicz, Weronika Węgier, and Krzysztof Walkowiak. The work was supported by the National Science Centre, Poland, under Grants 2019/35/B/ST7/04272, 2018/31/D/ST6/0304, and 2019/35/B/ST6/04442.
The SIX2021.xml file includes internet traffic data from the Seattle Internet Exchange Point collected for one year. The file contains information about the traffic volume decomposed into specific frame size ranges. It starts with a metadata section providing general information. The covered period is indicated by the 'start' and 'end' tags. They provide Unix timestamps in the GMT timezone. It should be noted that Seattle lies in the PST time zone, so the start and end times should be adjusted accordingly. The step parameter is given in seconds, so the samples are stored every 5 minutes. The file has multiple columns providing traffic data in bits per second for different frame size ranges. Column names specify the ranges in bytes. The 'total' column stores information about the total aggregate traffic volume, which is a sum of values in all the remaining columns in each row.

The global traffic control cabinet market, valued at $2.869 billion in 2025, is projected to experience steady growth, driven by increasing urbanization, rising investments in smart city infrastructure, and the growing need for efficient traffic management systems worldwide. The market's 3.8% CAGR indicates a consistent expansion through 2033, fueled by advancements in adaptive control cabinet technology offering improved traffic flow optimization and reduced congestion. Key application areas, such as urban transportation and public facilities, are experiencing significant growth, as cities worldwide prioritize improving road safety and traffic efficiency. The market is segmented by type, with timing control cabinets and adaptive control cabinets dominating the market share. Adaptive control cabinets are gaining traction due to their ability to dynamically adjust traffic signals based on real-time traffic conditions, thus optimizing traffic flow and reducing travel times. Major players in the market include SWARCO, Bison Profab, and others, competing on factors like technological innovation, product features, and geographical reach. While challenges remain, such as high initial investment costs for advanced systems and potential cybersecurity vulnerabilities, the long-term growth prospects remain positive due to ongoing government initiatives promoting smart city development and sustainable transportation solutions. The regional distribution of the market reflects global urbanization patterns, with North America and Europe holding substantial market shares due to advanced infrastructure and technological adoption. However, Asia-Pacific is expected to witness significant growth in the coming years driven by rapid urbanization and infrastructure development in countries like China and India. The competitive landscape is characterized by both established international players and regional manufacturers. The market is likely to see further consolidation through mergers and acquisitions as companies strive to expand their product portfolios and global reach. Technological advancements, such as the integration of artificial intelligence and the Internet of Things (IoT) in traffic management systems, are expected to drive innovation and shape the future of the traffic control cabinet market. The focus on sustainable and energy-efficient solutions is also a significant factor shaping the market's trajectory.

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

Please refer to the original data article for further data description: Jan Luxemburk et al. CESNET-QUIC22: A large one-month QUIC network traffic dataset from backbone lines, Data in Brief, 2023, 108888, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2023.108888. We recommend using the CESNET DataZoo python library, which facilitates the work with large network traffic datasets. More information about the DataZoo project can be found in the GitHub repository https://github.com/CESNET/cesnet-datazoo. The QUIC (Quick UDP Internet Connection) protocol has the potential to replace TLS over TCP, which is the standard choice for reliable and secure Internet communication. Due to its design that makes the inspection of QUIC handshakes challenging and its usage in HTTP/3, there is an increasing demand for research in QUIC traffic analysis. This dataset contains one month of QUIC traffic collected in an ISP backbone network, which connects 500 large institutions and serves around half a million people. The data are delivered as enriched flows that can be useful for various network monitoring tasks. The provided server names and packet-level information allow research in the encrypted traffic classification area. Moreover, included QUIC versions and user agents (smartphone, web browser, and operating system identifiers) provide information for large-scale QUIC deployment studies. Data capture The data was captured in the flow monitoring infrastructure of the CESNET2 network. The capturing was done for four weeks between 31.10.2022 and 27.11.2022. The following list provides per-week flow count, capture period, and uncompressed size:

W-2022-44

Uncompressed Size: 19 GB Capture Period: 31.10.2022 - 6.11.2022 Number of flows: 32.6M W-2022-45

Uncompressed Size: 25 GB Capture Period: 7.11.2022 - 13.11.2022 Number of flows: 42.6M W-2022-46

Uncompressed Size: 20 GB Capture Period: 14.11.2022 - 20.11.2022 Number of flows: 33.7M W-2022-47

Uncompressed Size: 25 GB Capture Period: 21.11.2022 - 27.11.2022 Number of flows: 44.1M CESNET-QUIC22

Uncompressed Size: 89 GB Capture Period: 31.10.2022 - 27.11.2022 Number of flows: 153M

Data description The dataset consists of network flows describing encrypted QUIC communications. Flows were created using ipfixprobe flow exporter and are extended with packet metadata sequences, packet histograms, and with fields extracted from the QUIC Initial Packet, which is the first packet of the QUIC connection handshake. The extracted handshake fields are the Server Name Indication (SNI) domain, the used version of the QUIC protocol, and the user agent string that is available in a subset of QUIC communications. Packet Sequences Flows in the dataset are extended with sequences of packet sizes, directions, and inter-packet times. For the packet sizes, we consider payload size after transport headers (UDP headers for the QUIC case). Packet directions are encoded as ±1, +1 meaning a packet sent from client to server, and -1 a packet from server to client. Inter-packet times depend on the location of communicating hosts, their distance, and on the network conditions on the path. However, it is still possible to extract relevant information that correlates with user interactions and, for example, with the time required for an API/server/database to process the received data and generate the response to be sent in the next packet. Packet metadata sequences have a length of 30, which is the default setting of the used flow exporter. We also derive three fields from each packet sequence: its length, time duration, and the number of roundtrips. The roundtrips are counted as the number of changes in the communication direction (from packet directions data); in other words, each client request and server response pair counts as one roundtrip. Flow statistics Flows also include standard flow statistics, which represent aggregated information about the entire bidirectional flow. The fields are: the number of transmitted bytes and packets in both directions, the duration of flow, and packet histograms. Packet histograms include binned counts of packet sizes and inter-packet times of the entire flow in both directions (more information in the PHISTS plugin documentation There are eight bins with a logarithmic scale; the intervals are 0-15, 16-31, 32-63, 64-127, 128-255, 256-511, 512-1024, >1024 [ms or B]. The units are milliseconds for inter-packet times and bytes for packet sizes. Moreover, each flow has its end reason - either it was idle, reached the active timeout, or ended due to other reasons. This corresponds with the official IANA IPFIX-specified values. The FLOW_ENDREASON_OTHER field represents the forced end and lack of resources reasons. The end of flow detected reason is not considered because it is not relevant for UDP connections. Dataset structure The dataset flows are delivered in compressed CSV files. CSV files contain one flow per row; data columns are summarized in the provided list below. For each flow data file, there is a JSON file with the number of saved and seen (before sampling) flows per service and total counts of all received (observed on the CESNET2 network), service (belonging to one of the dataset's services), and saved (provided in the dataset) flows. There is also the stats-week.json file aggregating flow counts of a whole week and the stats-dataset.json file aggregating flow counts for the entire dataset. Flow counts before sampling can be used to compute sampling ratios of individual services and to resample the dataset back to the original service distribution. Moreover, various dataset statistics, such as feature distributions and value counts of QUIC versions and user agents, are provided in the dataset-statistics folder. The mapping between services and service providers is provided in the servicemap.csv file, which also includes SNI domains used for ground truth labeling. The following list describes flow data fields in CSV files:

ID: Unique identifier SRC_IP: Source IP address DST_IP: Destination IP address DST_ASN: Destination Autonomous System number SRC_PORT: Source port DST_PORT: Destination port PROTOCOL: Transport protocol QUIC_VERSION QUIC: protocol version QUIC_SNI: Server Name Indication domain QUIC_USER_AGENT: User agent string, if available in the QUIC Initial Packet TIME_FIRST: Timestamp of the first packet in format YYYY-MM-DDTHH-MM-SS.ffffff TIME_LAST: Timestamp of the last packet in format YYYY-MM-DDTHH-MM-SS.ffffff DURATION: Duration of the flow in seconds BYTES: Number of transmitted bytes from client to server BYTES_REV: Number of transmitted bytes from server to client PACKETS: Number of packets transmitted from client to server PACKETS_REV: Number of packets transmitted from server to client PPI: Packet metadata sequence in the format: [[inter-packet times], [packet directions], [packet sizes]] PPI_LEN: Number of packets in the PPI sequence PPI_DURATION: Duration of the PPI sequence in seconds PPI_ROUNDTRIPS: Number of roundtrips in the PPI sequence PHIST_SRC_SIZES: Histogram of packet sizes from client to server PHIST_DST_SIZES: Histogram of packet sizes from server to client PHIST_SRC_IPT: Histogram of inter-packet times from client to server PHIST_DST_IPT: Histogram of inter-packet times from server to client APP: Web service label CATEGORY: Service category FLOW_ENDREASON_IDLE: Flow was terminated because it was idle FLOW_ENDREASON_ACTIVE: Flow was terminated because it reached the active timeout FLOW_ENDREASON_OTHER: Flow was terminated for other reasons

Link to other CESNET datasets

https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/ https://github.com/CESNET/cesnet-datazoo Please cite the original data article:

@article{CESNETQUIC22, author = {Jan Luxemburk and Karel Hynek and Tomáš Čejka and Andrej Lukačovič and Pavel Šiška}, title = {CESNET-QUIC22: a large one-month QUIC network traffic dataset from backbone lines}, journal = {Data in Brief}, pages = {108888}, year = {2023}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2023.108888}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923000069} }

Domain Name Service Market Outlook

The global Domain Name Service (DNS) market size was valued at approximately USD 1.2 billion in 2023, and is projected to reach around USD 2.5 billion by 2032, exhibiting a CAGR of 8.3% during the forecast period. This remarkable growth can be attributed to the increasing need for efficient traffic management, enhanced network security, and the growing number of internet users globally. As businesses increasingly operate in the digital domain, the demand for reliable and scalable DNS solutions has never been higher. The surge in e-commerce, cloud computing, and online services further propels the requirement for advanced DNS solutions to ensure optimal performance and user experience.

One of the primary growth factors driving the DNS market is the exponential increase in internet traffic and the number of web-based applications. The proliferation of IoT devices and the expansion of cloud services have led to a substantial rise in the number of domain names being registered. This surge necessitates more sophisticated DNS management solutions that can handle large volumes of queries and ensure low-latency responses. Furthermore, the growing concerns around cybersecurity and the rising incidences of DDoS attacks have underscored the importance of DNS security, prompting organizations to invest in robust managed DNS services to safeguard their digital assets.

Another significant factor contributing to market growth is the growing trend of digital transformation across various industries. Companies across sectors such as healthcare, BFSI, retail, and IT are increasingly adopting digital strategies to enhance their operations and customer engagement. This digital shift requires reliable and efficient DNS solutions to manage the increased web traffic and ensure uninterrupted online services. The adoption of cloud-based DNS services is particularly prominent, driven by the benefits of scalability, flexibility, and cost-effectiveness that cloud solutions offer. Moreover, the increasing adoption of multi-cloud environments further boosts the demand for comprehensive DNS management solutions.

Technological advancements and innovations in DNS solutions also play a crucial role in driving market growth. The development of advanced DNS features such as DNSSEC (Domain Name System Security Extensions) to prevent data breaches and the integration of AI and machine learning for predictive analytics and automated traffic management are gaining traction. Additionally, the implementation of IPv6 and the continuous expansion of the internet infrastructure are expected to create new opportunities for DNS service providers. These technological strides not only enhance the functionality and security of DNS solutions but also cater to the evolving needs of modern enterprises.

Regionally, North America holds a significant share of the DNS market, driven by the presence of major DNS service providers and the high adoption rate of advanced technologies. The region's well-established IT infrastructure and the increasing focus on cybersecurity further propel the demand for DNS solutions. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. Rapid digitalization, the growing internet user base, and the expansion of e-commerce in countries like China and India are key factors contributing to this growth. Europe also presents substantial opportunities, with increasing investments in IT infrastructure and the rising adoption of cloud services.

Type Analysis

The Domain Name Service market can be segmented based on type into Managed DNS and Unmanaged DNS. Managed DNS services are gaining substantial traction due to their ability to provide enhanced control, security, and performance. These services are particularly beneficial for businesses with complex and large-scale DNS requirements, offering features such as traffic load balancing, failover support, and advanced security protocols. The growing need to mitigate cyber threats and ensure uninterrupted online services is driving organizations to opt for managed DNS solutions, which are often provided by third-party vendors with expertise in DNS management.

Unmanaged DNS services, on the other hand, are typically used by smaller enterprises or individual users who require basic DNS functionalities without the need for advanced features. These services offer a cost-effective solution for managing domain n

This dataset consists of 24-hour traffic volumes which are collected by the City of Tempe high (arterial) and low (collector) volume streets. Data located in the tabular section shares with its users total volume of vehicles passing through the intersection selected along with the direction of flow.Historical data from this feature layer extends from 2016 to present day.Contact: Sue TaaffeContact E-Mail: sue_taaffe@tempe.govContact Phone: 480-350-8663Link to embedded web map:http://www.tempe.gov/city-hall/public-works/transportation/traffic-countsLink to site containing historical traffic counts by node: https://gis.tempe.gov/trafficcounts/Folders/Data Source: SQL Server/ArcGIS ServerData Source Type: GeospatialPreparation Method: N/APublish Frequency: As information changesPublish Method: AutomaticData Dictionary

The average daily internet traffic per capita in Romania had been increasing over the observed period, for both broadband and mobile internet connections. As a result, the average daily broadband internet traffic per person reached 2.9 GB (gigabytes) in the first half of 2024, which represented an increase of nine percent compared to the same period of 2023.

This is a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. Historical traffic is based on the average of observed speeds over the past three years. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map image can be requested for the current time and any time in the future. A map image for a future request might be used for planning purposes. The map layer also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

The statistic shows the percentage of data downloaded on fixed networks in the United Kingdom (UK) in 2013, by traffic type. In June 2013, 27 percent of data downloaded on fixed networks in the UK was web browsing traffic.

Internet Backbone Service Market Outlook

The global Internet Backbone Service market size is projected to grow significantly, with a CAGR of 6.4% from 2023 to 2032. In 2023, the market size is estimated to be at $10.2 billion and is expected to reach $18.2 billion by 2032. This growth is driven by the increasing demand for high-speed internet, the proliferation of cloud services, and the expansion of data centers worldwide.

One of the primary growth factors in the Internet Backbone Service market is the surging demand for high-speed, reliable internet connectivity. As more businesses and consumers require uninterrupted online services, the need for robust internet infrastructure becomes essential. Video streaming, online gaming, and remote work are heavily reliant on a stable internet backbone, pushing service providers to enhance their infrastructure. Additionally, the growth of 5G networks is expected to further increase the demand for internet backbone services, as these networks require high-capacity and low-latency backhaul solutions.

The exponential growth of cloud computing is another significant driver for the Internet Backbone Service market. Cloud service providers require vast amounts of bandwidth to support their operations, ensuring seamless data transfer and access to cloud-based applications and storage. The rise of Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS) models has created a substantial need for efficient and scalable internet backbone services. As more enterprises migrate to the cloud, the demand for high-capacity backbone services is set to rise.

Moreover, the expansion of data centers globally is a critical factor fueling the market's growth. Data centers, which host and manage data for various applications, depend heavily on internet backbone services to provide reliable and high-speed connectivity. As industries such as finance, healthcare, and e-commerce increasingly rely on data-centric operations, the proliferation of data centers is expected to continue. This expansion necessitates advanced backbone services to ensure optimal performance and connectivity, driving market growth.

From a regional perspective, North America remains a significant player in the Internet Backbone Service market, driven by the presence of major technology companies and extensive internet infrastructure. However, Asia Pacific is expected to witness the highest growth rate during the forecast period. The rapid adoption of digital technologies, increasing internet penetration, and the expansion of data centers in countries like China and India are propelling the market in this region. Europe also holds a substantial share, supported by advancements in broadband services and ongoing investments in digital infrastructure.

Service Type Analysis

The Internet Backbone Service market by service type is segmented into IP Transit, Peering, Colocation, and Others. Each of these services plays a critical role in the functioning and efficiency of the internet infrastructure. IP Transit services are essential for providing internet connectivity to ISPs and large enterprises. These services ensure that data can be transferred efficiently across different networks. The increasing demand for high-speed internet and the proliferation of online services are driving the growth of IP Transit services. As more businesses expand their online presence, the need for efficient data transfer across networks is paramount, boosting the demand for IP Transit services.

Peering services, on the other hand, involve the direct interconnection of networks to exchange traffic. This service is crucial for reducing latency and improving the speed and reliability of internet connections. With the growing emphasis on providing seamless online experiences, the demand for peering services is on the rise. Enterprises and service providers are increasingly opting for peering arrangements to enhance their network performance and ensure optimal user experiences. The continuous expansion of internet traffic and the need for efficient data exchange are expected to drive the growth of peering services in the coming years.

Colocation services involve housing servers and other hardware in third-party data centers. These services are vital for businesses looking to reduce the costs and complexities associated with maintaining their own data centers. The growing demand for data storage and management solutions is propelling the growth of colocation services. As more enterprises adopt cloud-based solutions, the ne

We are publishing a dataset we created for the HTTPS traffic classification.

Since the data were captured mainly in the real backbone network, we omitted IP addresses and ports. The datasets consist of calculated from bidirectional flows exported with flow probe Ipifixprobe. This exporter can export a sequence of packet lengths and times and a sequence of packet bursts and time. For more information, please visit ipfixprobe repository (Ipifixprobe).

During our research, we divided HTTPS into five categories: L -- Live Video Streaming, P -- Video Player, M -- Music Player, U -- File Upload, D -- File Download, W -- Website, and other traffic.

We have chosen the service representatives known for particular traffic types based on the Alexa Top 1M list and Moz's list of the most popular 500 websites for each category. We also used several popular websites that primarily focus on the audience in our country. The identified traffic classes and their representatives are provided below:

Live Video Stream Twitch, Czech TV, YouTube Live

Video Player DailyMotion, Stream.cz, Vimeo, YouTube

Music Player AppleMusic, Spotify, SoundCloud

File Upload/Download FileSender, OwnCloud, OneDrive, Google Drive

Website and Other Traffic Websites from Alexa Top 1M list