Tracking the evolution of Web traffic: 1995-2003

Hernández-Campos, Félix; Jeffay, Kevin; Smith, F. Donelson

doi:10.1109/mascot.2003.1240638

Cited by 39 publications

(38 citation statements)

References 11 publications

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“…Similar changes have also been observed in wireless traffic [327]. Likewise, the structure of web traffic changed as more objects were embedded in web pages, and as banner ads and delivery services caused more data to be fetched from servers other than the one that served the "top-level" page [329].…”

Section: Workload Evolutionsupporting

confidence: 55%

“…For example, HTTP traffic on the Internet when observed for 90 seconds is practically the same as when observed over 4 hours [329]. But it will not be stationary if observed over long periods; at a minimum, the daily cycle implies that activity at night will be different from that during peak hours.…”

Section: Stationarity In Workloadsmentioning

confidence: 91%

“…They therefore included a heavy tail as one of the features of their SURGE workload generator [57]. Years later this decision was justified when further data showed that the heavy tail indeed extends at least up to a size of 10 9 bytes [329]. But in principle it could also have been truncated at say 10 7 .…”

Section: Generalization and Extrapolationmentioning

confidence: 99%

“…• The function of the different packets, and in particular to which protocol they belong. As Internet technology evolves, the dominant protocols observed in the traffic change [252,329]. This also affects the statistical properties of the workload [534].…”

Section: Packet Trafficmentioning

confidence: 99%

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Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

196

131

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Reliable performance evaluations require the use of representative workloads. This is no easy task since modern computer systems and their workloads are complex, with many interrelated attributes and complicated structures. Experts often use sophisticated mathematics to analyze and describe workload models, making these models difficult for practitioners to grasp. This book aims to close this gap by emphasizing the intuition and the reasoning behind the definitions and derivations related to the workload models. It provides numerous examples from real production systems, with hundreds of graphs. Using this book, readers will be able to analyze collected workload data and clean it if necessary, derive statistical models that include skewed marginal distributions and correlations, and consider the need for generative models and feedback from the system. The descriptive statistics techniques covered are also useful for other domains.

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Section: Workload Evolutionsupporting

confidence: 55%

Section: Stationarity In Workloadsmentioning

confidence: 91%

Section: Generalization and Extrapolationmentioning

confidence: 99%

Section: Packet Trafficmentioning

confidence: 99%

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Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

196

131

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“…Our web traffic is based on Hernández-Campos et al [25]'s "Data Set 4," collected in April 2003 [48]. Our simplified BitTorrent clients always maintain four connections, and upload and download data at the maximum speed Tor allows.…”

Section: Experimental Apparatusmentioning

confidence: 99%

Building Incentives into Tor

Ngan

Dingledine

Wallach

2010

Financial Cryptography and Data Security

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Abstract. Distributed anonymous communication networks like Tor depend on volunteers to donate their resources. However, the efforts of Tor volunteers have not grown as fast as the demands on the Tor network. We explore techniques to incentivize Tor users to relay Tor traffic too; if users contribute resources to the Tor overlay, they should receive faster service in return. In our design, the central Tor directory authorities measure performance and publish a list of Tor relays that should be given higher priority when establishing circuits. Simulations of our proposed design show that conforming users receive significant improvements in performance, in some cases experiencing twice the network throughput of selfish users who do not relay traffic for the Tor network.

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Dynamic information‐based scalable hashing on a cluster of web cache servers

Kwak

Sohn

Chung

2011

Concurrency and Computation

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SUMMARYCaching web pages is an important part of web infrastructures. Medium to large-scale infrastructures deploy a cluster of servers to solve the scalability and storage problems inherent in caching. In this paper we present dynamic information-based scalable hashing that evenly hashes client requests to a cluster of cache servers, resulting in performance scalability. Runtime information is used to determine when and how to cache pages. Cached pages are stored and retrieved mutually exclusively to/from all the servers to minimize the use of storage, resulting in storage scalability. We set up an experimental environment consisting of various machines, including client servers, a cluster of 16 cache servers, and a load balancer. We demonstrate through experimental results that dynamic information-based scalable hashing maximizes both performance scalability and storage scalability while the existing approaches do only either one of the two.

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Tracking the evolution of Web traffic: 1995-2003

Abstract: Abstract

Cited by 39 publications

References 11 publications

Workload Modeling for Computer Systems Performance Evaluation

Workload Modeling for Computer Systems Performance Evaluation

Building Incentives into Tor

Dynamic information‐based scalable hashing on a cluster of web cache servers

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