On Pricing Algorithms for Batched Content Delivery Systems 

Srinivasan Jagannathan{1}, Jayanth Nayak{2}, Kevin Almeroth{1}, Markus 
Hofmann{3}

Department of Computer Science{1}, Department of Electrical and Computer 
Engineering{2} 
University of California, Santa Barbara, CA~93106-5110
{jsrini,almeroth}@cs.ucsb.edu, jayanth@ece.ucsb.edu

Networking Software Research Department{3}
Bell Laboratories, Holmdel, NJ 07733-3030
hofmann@bell-labs.com



ABSTRACT:  Businesses offering video-on-demand (VoD) and e-CD sales are
growing in the Internet. Batching of requests coupled with a one-to-many
delivery mechansim such as multicast can increase scalability and
efficiency of resource utilization. There is very little insight into
pricing such services in a manner that utilizes network and system
resources efficiently as well as maximizes the expectation of revenue. In
this paper, we investigate simple, yet effective mechanisms to price
content in a batching context. We observe that if customer behavior is
well understood and temporally invariant, a fixed pricing scheme can
maximize expectation of revenue if there are infinite resources. However,
with constrained resources and potentially unknown customer behavior, only
a dynamic pricing algorithm can maximize expectation of revenue. We
formulate the problem of pricing as a constrained optimization problem and
show that maximizing the expectation of revenue can be intractable even
when the customer behavior is well known. Since customer behavior may not
be well known in an Internet setting, we develop a model to understand
customer behavior online and a pricing algorithm based on this model.
Using simulations, we characterize the performance of this algorithm and
other simple and deployable pricing schemes under different customer
behavior and system load profiles. Based on our characterization, we
propose a pricing scheme that combines the best features of the different
pricing schemes and analyze its performance.