SCS Faculty Candidate Talk

  • Ph.D. Candidate and Jacobus Fellow
  • Program in Applied and Computational Mathematics
  • Princeton University

Smart Data Pricing

Data traffic has increased sharply over the past decade and is expected to grow further as the Internet becomes ever more popular. Yet data network capacity is not expanding fast enough to handle this exponential growth, leading service providers to change their mobile data plans in an effort to reduce congestion. Inspired by these ongoing changes and building on work from the 1990s, smart data pricing (SDP) aims to rethink data pricing for tomorrow's networks. In this talk, I will focus on first the temporal and then the content dimensions of SDP. Time-dependent pricing (TDP) proposes to lower short-lived peaks in network congestion by incentivizing users to shift their data usage to less congested times.

While TDP has been used in industries such as smart grids, TDP for mobile data presents unique challenges, e.g., it is difficult to predict how users will react to the prices on different days. Thus, we developed algorithms that continually infer users' changing responses to the offered prices, without collecting private data usage information. We implemented these algorithms in a prototype system, which we used to conduct the first field trial of TDP for mobile data. We showed that our TDP algorithms led to significantly less temporal fluctuation in demand, benefiting the service provider and lowering users' data prices overall.

Sponsored data, an emerging form of data pricing offered by AT&T, allows content providers to subsidize their users' data traffic; the resulting revenue can be used to expand existing data networks. We consider the impact of sponsored data on different content providers and users, showing that cost-aware users and cost-unaware content providers reap disproportionate benefits. Simulations across representative users and content providers verify that sponsored data may help to bridge the digital divide between different types of users, yet can exacerbate competition between content providers.

Carlee Joe-Wong is a Ph.D. candidate and Jacobus fellow at Princeton University's Program in Applied and Computational Mathematics. She is interested in mathematical aspects of computer and information networks, including work on smart data pricing and fair resource allocation. Carlee received her A.B. in mathematics in 2011 and her M.A. in applied mathematics in 2013, both from Princeton University.  In 2013–2014, she was the Director of Advanced Research at DataMi, a startup she co-founded from her data pricing research. Carlee received the INFORMS ISS Design Science Award in 2014 and the Best Paper Award at IEEE INFOCOM 2012.

Faculty Host: Srinivasan Seshan

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