TY - GEN
T1 - Time-dependent broadband pricing
T2 - 31st International Conference on Distributed Computing Systems, ICDCS 2011
AU - Joe-Wong, Carlee
AU - Ha, Sangtae
AU - Chiang, Mung
PY - 2011
Y1 - 2011
N2 - Charging different prices for Internet access at different times induces users to spread out their bandwidth consumption across times of the day. Potential impact on ISP revenue, congestion management, and consumer behavior can be significant, yet some fundamental questions remain: is it feasible to operate time dependent pricing and how much benefit can it bring? We develop an efficient way to compute the cost-minimizing time-dependent prices for an Internet service provider (ISP), using both a static session-level model and a dynamic session model with stochastic arrivals. A key step is choosing the representation of the optimization problem so that the resulting formulations remain computationally tractable for large-scale problems. We next show simulations illustrating the use and limitation of time-dependent pricing. These results demonstrate that optimal prices, which "reward" users for deferring their sessions, roughly correlate with demand in each period, and that changing prices based on real-time traffic estimates may significantly reduce ISP cost. The degree to which traffic is evened out over times of the day depends on the timesensitivity of sessions, cost structure of the ISP, and amount of traffic not subject to time-dependent prices. Finally, we present our system integration and implementation, called TUBE, and proof-of-concept experimentation.
AB - Charging different prices for Internet access at different times induces users to spread out their bandwidth consumption across times of the day. Potential impact on ISP revenue, congestion management, and consumer behavior can be significant, yet some fundamental questions remain: is it feasible to operate time dependent pricing and how much benefit can it bring? We develop an efficient way to compute the cost-minimizing time-dependent prices for an Internet service provider (ISP), using both a static session-level model and a dynamic session model with stochastic arrivals. A key step is choosing the representation of the optimization problem so that the resulting formulations remain computationally tractable for large-scale problems. We next show simulations illustrating the use and limitation of time-dependent pricing. These results demonstrate that optimal prices, which "reward" users for deferring their sessions, roughly correlate with demand in each period, and that changing prices based on real-time traffic estimates may significantly reduce ISP cost. The degree to which traffic is evened out over times of the day depends on the timesensitivity of sessions, cost structure of the ISP, and amount of traffic not subject to time-dependent prices. Finally, we present our system integration and implementation, called TUBE, and proof-of-concept experimentation.
UR - http://www.scopus.com/inward/record.url?scp=80051867517&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80051867517&partnerID=8YFLogxK
U2 - 10.1109/ICDCS.2011.81
DO - 10.1109/ICDCS.2011.81
M3 - Conference contribution
AN - SCOPUS:80051867517
SN - 9780769543642
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 288
EP - 298
BT - Proceedings - 31st International Conference on Distributed Computing Systems, ICDCS 2011
Y2 - 20 June 2011 through 24 July 2011
ER -