Time-Dependent Pricing for Multimedia Data Traffic: Analysis, Systems, and Trials

The explosive growth of multimedia data traffic in wired and wireless networks have led Internet service providers (ISPs) to use penalty mechanisms like throttling, capping, overage fees to manage network congestion; however, such measures are harmful to the Internet ecosystem. Therefore, we use ideas from economics to create incentive-based, as opposed to penalty-based, solutions for data plans. In particular, we explore time-dependent pricing (TDP) - a form of dynamic pricing that manages congestion by offering time-varying discounts to incentivize users to shift some data traffic temporally. To realize TDP data plans in practice, we provide (i) an optimization model to compute time-dependent prices, (ii) a system implementation for deployment in operational networks, and (iii) experiments with two cellular networks for demonstrating feasibility. Our results show that the users respond to such pricing plans by using higher volume of traffic in lower-priced (off-peak) periods and benefit from a lower $/GB fee, while the ISPs benefit from a higher revenue due to increase in off-peak usage and lower peak-to-average traffic ratio in their network. This suggests that such a pricing solution can incentivize users to modify their usage behavior and enable better revenue management in multimedia-rich networks.

[1]  Jorge J. Moré,et al.  The Levenberg-Marquardt algo-rithm: Implementation and theory , 1977 .

[2]  Sangtae Ha,et al.  Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility , 2012, IEEE Journal on Selected Areas in Communications.

[3]  Sangtae Ha,et al.  TUBE: time-dependent pricing for mobile data , 2012, SIGCOMM '12.

[4]  L. Green,et al.  Discounting of delayed rewards: Models of individual choice. , 1995, Journal of the experimental analysis of behavior.

[5]  Amit Mukhopadhyay,et al.  Mobile data explosion: Monetizing the opportunity through dynamic policies and QoS pipes , 2011, Bell Labs Technical Journal.

[6]  Sangtae Ha,et al.  When the price is right: enabling time-dependent pricing of broadband data , 2013, CHI.

[7]  Richard Banks,et al.  You're capped: understanding the effects of bandwidth caps on broadband use in the home , 2012, CHI.

[8]  Sajal K. Das,et al.  An Approach to Pre-Schedule Traffic in Time-Dependent Pricing Systems , 2019, IEEE Transactions on Network and Service Management.

[9]  D. G. Watts,et al.  Nonlinear Regression: Iterative Estimation and Linear Approximations , 2008 .

[10]  L. Green,et al.  A comparison of four models of delay discounting in humans , 2009, Behavioural Processes.

[11]  Rebecca E. Grinter,et al.  Why is my internet slow?: making network speeds visible , 2011, CHI.

[12]  Wolfgang Ketter,et al.  Demand side management—A simulation of household behavior under variable prices , 2011 .

[13]  Phone Lin,et al.  Time dependent adaptive pricing for mobile internet access , 2015, 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[14]  Sangtae Ha,et al.  A survey of smart data pricing , 2012, ACM Comput. Surv..

[15]  Sang Pil Han,et al.  An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet , 2011, Manag. Sci..

[16]  Sangtae Ha,et al.  Time-Dependent Broadband Pricing: Feasibility and Benefits , 2011, 2011 31st International Conference on Distributed Computing Systems.

[17]  Dan Wang,et al.  Time dependent pricing in wireless data networks: Flat-rate vs. usage-based schemes , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[18]  W. Keith Edwards,et al.  Eden: supporting home network management through interactive visual tools , 2010, UIST '10.

[19]  Randy H. Katz,et al.  Pricing experiments for a computer-telephony-service usage allocation , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[20]  Carlee Joe-Wong,et al.  To Accept or Not to Accept: The Question of Supplemental Discount Offers in Mobile Data Plans , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[21]  Yong Li,et al.  Time Dependent Pricing for Large-Scale Mobile Networks of Urban Environment: Feasibility and Adaptability , 2020, IEEE Transactions on Services Computing.

[22]  D. Read,et al.  The Psychology of Intertemporal Tradeoffs , 2009, Psychological review.

[23]  Hal R. Varian,et al.  The Demand for Bandwidth: Evidence from the INDEX Project , 2002 .

[24]  Shanling Li,et al.  Structural Properties of Buyback Contracts for Price-Setting Newsvendors , 2008, Manuf. Serv. Oper. Manag..

[25]  M. Nerlove,et al.  Biases in dynamic models with fixed effects , 1988 .

[26]  Christos Gkantsidis,et al.  Who's hogging the bandwidth: the consequences of revealing the invisible in the home , 2010, CHI.

[27]  Sangtae Ha,et al.  Do Mobile Data Plans Affect Usage? Results from a Pricing Trial with ISP Customers , 2015, PAM.

[28]  Tom Rodden,et al.  Homework: putting interaction into the infrastructure , 2012, UIST '12.

[29]  Nobuo Yamashita,et al.  On a Global Complexity Bound of the Levenberg-Marquardt Method , 2010, J. Optim. Theory Appl..

[30]  Angelika Dimoka,et al.  Research Commentary - NeuroIS: The Potential of Cognitive Neuroscience for Information Systems Research , 2011, Inf. Syst. Res..

[31]  Arthur H. Neill,et al.  Reply Comments of New Media Rights in the Matter of Protecting and Promoting the Open Internet , 2014 .