An HTTP web traffic model based on the top one million visited web pages

Pries, Rastin; Magyari, Zsolt; Tran-Gia, Phuoc

doi:10.1109/ngi.2012.6252145

Cited by 43 publications

(35 citation statements)

References 12 publications

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“…This behaviour is consistent with the self-similar nature of web browsing traffic, as it has been described as an ON-OFF model in the literature, e.g. [9]. The average throughput (T ) of such a sequential browsing Session would be:…”

Section: A Web Trafficsupporting

confidence: 82%

“…For this page size, the dwell time follows a uniform distribution within [6,50] seconds, with a mean of 28 seconds. Both the average page size and dwell time are comparable with the relevant averages from the HTTP model in [9]. For Good Quality Sessions the load time would be within [2.625, 6.2812], with an average of 4.45 seconds.…”

Section: A Interactive Webmentioning

confidence: 53%

“…Web traffic is commonly described with an ON-OFF model (e.g. [9], with the ON periods modelling the HTTP GET requests, the responses, and the requests for the embedded objects, while the OFF periods are used to model the processing and client dwell time. Similar to application source-level models, we present a source-level model of user behaviour, tuned to persons operating mobile devices.…”

Section: Related Workmentioning

confidence: 99%

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Mobile network traffic: A user behaviour model

Tsompanidis

Zahran

Sreenan

2014

2014 7th IFIP Wireless and Mobile Networking Conference (WMNC)

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Abstract-What would the same users do in a different network? The performance of the network has a significant effect on the traffic profiles of users, which cannot be easily identified from network traces. This work combines a number of studies to compile a new responsive traffic model for mobile networks that realistically mimics user behaviour. Users continuously evaluate the performance of the network, and initiate or terminate their sessions accordingly. The presented model utilises Markov chains to capture this behaviour, while the performance of a session is depicted with the binary distinction of good and bad quality. We analyse and showcase the implications of this model, and discuss its benefits on network planning and research applications.

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Section: A Web Trafficsupporting

confidence: 82%

Section: A Interactive Webmentioning

confidence: 53%

Section: Related Workmentioning

confidence: 99%

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Mobile network traffic: A user behaviour model

Tsompanidis

Zahran

Sreenan

2014

2014 7th IFIP Wireless and Mobile Networking Conference (WMNC)

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“…User Behavior [20,21] The process of visiting the Web site can be roughly divided into the following three stages: First, a user starts a conversation by selecting a link what they are interested in; Second, after browsing, they often click another link, in which they are interested, in a relatively short time; Third, when he obtains the required information, he will stay for a long time with an inactive state. In particular, Figure 2 shows the important traffic parameters, including think-time, session-time, etc.…”

Section: The Existing Approaches Of Traffic Generationmentioning

confidence: 99%

A Scalable Parallel Architecture Based on Many-Core Processors for Generating HTTP Traffic

Wang

Jin

et al. 2017

Applied Sciences

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Abstract:The past years have witnessed the significant development of the Internet. Numerous emerging network architectures and protocols have triggered the demand for traffic generators which stand in stark contrast to previous schemes. Namely, fixed test content is inefficient in the presence of such a dynamic and realistic demand. Moreover, the requirement of high-performance has raised the stakes on developing a new concurrent system. In this paper, we present a hierarchical parallel design for a Web traffic generator on a TILERAGX36 processor, called TGMP. We discuss the challenges in developing its hierarchical architectural design, and elaborate on its implementation details. Specifically, in order to generate a realistic network workload over a long and large time scale, we propose a user-control scheme based on cubic spline interpolation. To better improve the scalability of the system and satisfy the required flow rate, we adopt techniques, including optimization of parameters under the Linux kernel, event-driven concurrency, and parallel architectures of a TILERAGX36 processor. The experimental results demonstrate that TGMP is able to create real traffic and simulate 50,000 users accessing the Web server simultaneously.

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“…Similar to [1,12,13], I focused entirely on the HTTP protocol which is the primary method of communication in the World Wide Web [20]. HTTP is a request-response protocol in which clients request resources identified by a unique URI and the corresponding server responds with a response payload.…”

Section: Overviewmentioning

confidence: 99%

Bandwidth Aggregation Across Multiple Smartphone Devices

Zeller

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Bandwidth Aggregation Across Multiple Smartphone Devices Bradley Raymond ZellerSmartphones now account for the majority of all cell phones in use today [23].Ubiquitous Internet access is a valuable feature offered by these devices and the vast majority of smartphone applications make use of the Internet in one way or another.However, the bandwidth offered by these cellular networks is often much lower than we typically experience on our standard home networks, leading to a less-than-optimal user experience. This makes it very challenging and frustrating to access certain types of web content such as video streaming, large file downloads, loading large webpages, etc.Given that most modern smartphones are multi-homed and are capable of accessing multiple networks simultaneously, this thesis attempts to utilize all available network interfaces in order to achieve the aggregated bandwidth of each to improve the overall network performance of the phone. To do so, I implement a bandwidth aggregation system for iOS that combines the bandwidths of multiple devices located within close proximity of each other. Deployed on up to three devices, speedups of up to 1.82x were achieved for downloading a single, 10mb file. Webpage loading saw speedups of up to 1.55x. iv ACKNOWLEDGMENTS

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An HTTP web traffic model based on the top one million visited web pages

Cited by 43 publications

References 12 publications

Mobile network traffic: A user behaviour model

Mobile network traffic: A user behaviour model

A Scalable Parallel Architecture Based on Many-Core Processors for Generating HTTP Traffic

Bandwidth Aggregation Across Multiple Smartphone Devices

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