Crowdsourced network measurements: Benefits and best practices

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• 作者：Matthias Hirth ; ^a ; ^{matthias.hirth@informatik.uni-wuerzburg.de" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Tobias Hoß ; feld^b ; ^{tobias.hossfeld@uni-due.de" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Marco Mellia^c ; ^{mellia@polito.it" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Christian Schwartz^a ; ^{christian.schwartz@informatik.uni-wuerzburg.de" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Frank Lehrieder^a ; ^{lehrieder@informatik.uni-wuerzburg.de" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Crowdsourcing ; Network measurements ; Methodology ; Best practices
• 刊名：Computer Networks
• 出版年：2015
• 出版时间：29 October 2015
• 年：2015
• 卷：90
• 期：Complete
• 页码：85-98
• 全文大小：994 K

文摘

Network measurements are of high importance both for the operation of networks and for the design and evaluation of new management mechanisms. Therefore, several approaches exist for running network measurements, ranging from analyzing live traffic traces from campus or Internet Service Provider (ISP) networks to performing active measurements on distributed testbeds, e.g., PlanetLab, or involving volunteers. However, each method falls short, offering only a partial view of the network. For instance, the scope of passive traffic traces is limited to an ISP’s network and customers’ habits, whereas active measurements might be biased by the population or node location involved. To complement these techniques, we propose to use (commercial) crowdsourcing platforms for network measurements. They permit a controllable, diverse and realistic view of the Internet and provide better control than do measurements with voluntary participants. In this study, we compare crowdsourcing with traditional measurement techniques, describe possible pitfalls and limitations, and present best practices to overcome these issues. The contribution of this paper is a guideline for researchers to understand when and how to exploit crowdsourcing for network measurements.