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Editor-in-Chief: Steven Weber. Website: http://www.comsoc.org/ctn
Living in the era with unprecedented growth in demand for data capacity, Smart Data Pricing (SDP) will play an increasingly vital role in the future of mobile, broadband internet, and data content.
SDP refers to any dynamic context-dependent mechanism used by a service or content provider to set the price charged to an end user in exchange for handling a content (data) request. The context from which the price is computed may incorporate variety of aspects, such as: the request time, user location, application originating the request, the current data usage pattern on the network, the overall level of network congestion, the type of data being requested, or any other potentially relevant aspect of the content request.
September ComSoc Technology News (CTN) special issue summarizes three exciting papers from the 2014 SDP Workshop at IEEE INFOCOM.
Smart Data Pricing: To Share or Not to Share
Authors: Yue Jin (Bell Labs, Alcatel-Lucent, Ireland) and Zhan Pang, Lancaster University, UK
A shared data plan is one where the usage price and/or quota for a single account covers data consumed across multiple devices and/or multiple users. “Family plans” currently advertised by service providers are a representative example, and offer an appealing (to some) alternative to the conventional model of one data plan per device. It is natural to ask under what conditions a service provider finds profit in offering such plans to its customer base.
This paper compares “bundled” (shared) data plans with “partitioned” data plans in a simplified market environment with a single (monopolist) service provider, servicing an idealized population of independent users, each of which owns two wireless devices (say, a smart phone and a tablet). Read more.
Joint Pricing and Proactive Caching for Data Services: Global and User-centric Approaches
Autors: John Tadrous, Atilla Eryilmaz, and Hesham EL Gamal, The Ohio State University, USA
A vexing problem for the service provider is the (typically) high ratio of daily peak demand to average demand, especially in the context of streaming media. The service provider must provision sufficient resources in order to handle the peak demand (at potentially significant cost), but these resources then sit idle during off peak times (with corresponding reduced profits).
An appealing solution to lower this ratio is for the service provider to prefetch content during off peak times in order to reduce the network load during peak times. In order for this prefetching to be effective, the provider must either have a very good characterization of user streaming preferences in each time period, or must be able to incentivize the user to view the cached material. These two options are by no means exclusive: the provider can prefetch content based on user preference profiles, as well as set prices for streaming to incentivize users to view cached content. This paper studies the optimal caching and pricing policies in this setting. Read more.
Congestion-Aware Internet Pricing for Media Streaming
Autors: Di Niu, University of Alberta; Baochun Li, University of Toronto
Taking inspiration from proposed congestion-dependent road pricing policies where a vehicle is charged in proportion to the distance it travels along the road, the authors in this paper propose a new metric for congestion-sensitive pricing of streaming media content over a network. In particular, the ISP computes the product of the (instantaneous) streaming media rate (the application throughput) times the (instantaneous) packet transmission delay on each link.
The overall cost to the content consumer is computed by summing this cost over all links between the content source and destination. A little thought shows that this metric is equivalent to computing the so-called bandwidth-delay product on each link. The bandwidth delay product of a flow of packets over a link is the product of the rate at which packets are transmitted onto the link times the end-to-end delay for a packet on that link, and equals the amount of data from the flow on the link at any point in time. The proposed pricing mechanism is congestion dependent, since the transmission delay on the link is increasing in the link congestion level. Read more.