SmartTransfer: transferring your mobile multimedia contents at the "right" time
Proceedings of the 22nd international workshop on Network and Operating System Support for Digital Audio and Video
Demo: enabling mobile time-dependent pricing
Proceedings of the 10th international conference on Mobile systems, applications, and services
Vision: mClouds - computing on clouds of mobile devices
Proceedings of the third ACM workshop on Mobile cloud computing and services
TUBE: time-dependent pricing for mobile data
Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication
TUBE: time-dependent pricing for mobile data
ACM SIGCOMM Computer Communication Review - Special october issue SIGCOMM '12
Mercado: using market principles to drive alternative network service abstractions
Proceedings of the 2012 ACM workshop on Capacity sharing
CrowdMAC: a crowdsourcing system for mobile access
Proceedings of the 13th International Middleware Conference
Sprinkler: distributed content storage for just-in-time streaming
Proceeding of the 2013 workshop on Cellular networks: operations, challenges, and future design
Virtualizing the access network via open APIs
Proceedings of the ninth ACM conference on Emerging networking experiments and technologies
A survey of smart data pricing: Past proposals, current plans, and future trends
ACM Computing Surveys (CSUR)
| Hi-index | 0.00 |
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 time-sensitivity 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.