Helping Users Shop for ISPs with Internet Nutrition Labels

University of Napoli Federico II Napoli, Italy
08/2011; DOI: 10.1145/2018567.2018571


When purchasing home broadband access from Internet service providers (ISPs), users must decide which service plans are most appropriate for their needs. Today, ISPs advertise their available service plans using only generic upload and download speeds. Un-fortunately, these metrics do not always accurately reflect the vary-ing performance that home users will experience for a wide range of applications. In this paper, we propose that each ISP service plan carry a "nutrition label" that conveys more comprehensive informa-tion about network metrics along many dimensions, including vari-ous aspects of throughput, latency, loss rate, and jitter. We first jus-tify why these metrics should form the basis of a network nutrition label. Then, we demonstrate that current plans that are superficially similar with respect to advertised download rates may have differ-ent performance according to the label metrics. We close with a discussion of the challenges involved in presenting a nutrition label to users in a way that is both accurate and easy to understand.

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    • "An imminent technical and usability challenge is to summarize access network performance data so that users can make informed choices about the service plans that are most appropriate for them (akin to a " performance nutrition label "[2]). Our recent work proposes some first steps in this direction[22]. Lesson 3 (Home Network Equipment Matters) A user's home network infrastructure can significantly affect performance. "
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    • "One approach might be to map these low-level performance metrics to more meaningful application performance metrics. For example , researchers have proposed a nutrition label that presents more user-centric metrics that are derived from low-level metrics [51]. For example, a typical user may have difficulty understanding how packet loss can introduce severe performance degradations in voice stream; on the other hand, one might imagine profiling a user or household to identify the common applications used in the home, and presenting a report for the performance of each of those applications , based on the observed the low-level metrics (e.g., Skype performance may be poor, without necessarily telling the user it is because packet loss is higher than the application expects.) "
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