Srinivasan Jagannathan

A model for discovering customer value for E-content

There exists a huge demand for multimedia goods and services in the Internet. Currently available bandwidth speeds can support sale of downloadable content like CDs, e-books, etc. as well as services like video-on-demand. In the future, such services will be prevalent in the Internet. Since costs are typically fixed, maximizing revenue can maximize profits. A primary determinant of revenue in such e-content markets is how much value the customers associate with the content. Though marketing surveys are useful, they cannot adapt to the dynamic nature of the Internet market. In this work, we examine how to learn customer valuations in close to real-time. Our contributions in this paper are threefold: (1) we develop a probabilistic model to describe customer behavior, (2) we develop a framework for pricing e-content based on basic economic principles, and (3) we propose a price discovering algorithm that learns customer behavior parameters and suggests prices to an e-content provider. We validate our algorithm using simulations. Our simulations indicate that our algorithm generates revenue close to the maximum expectation. Further, they also indicate that the algorithm is robust to transient customer behavior.

Price issues in delivering E-content on-demand

The explosive increase in Internet bandwidth and usage opens a vista of opportunities to sell multimedia-rich software and services using the Internet. Once e-content is created, the cost of replication is negligible. Customers can download the e-content immediately after online transactions. Alternately, the content provider can stream the content to the customers. A sound business model is necessary for the success of such an enterprise. In this paper, we examine the determinants of revenue for an Internet based on-demand content delivery service. The determinants of revenue are: transaction model, pricing strategy, customer behavior, distribution resources, and competition. We briefly describe each of these factors and discuss how they relate to revenue. Our belief is that by better understanding how these factors affect revenue, content providers can develop services that generate more revenue while also being more compelling to users.

Using tree topology for multicast congestion control

Multicast is a promising technique for mass distribution of streaming media. However, the inherent heterogeneity of the Internet poses several challenges. A major challenge is to develop a congestion control mechanism that is efficient, flexible (to administrative heterogeneity) and deployable. Many approaches using a layered encoding scheme have been proposed to address this problem. In parallel, many tools are being developed which provide a snapshot of network internals. Of particular interest are multicast topology discovery tools. The existence of such tools motivates the possibility of using tree topology information for multicast congestion control. In this paper we seek to understand the benefits of such a mechanism and the challenges in its practical implementation. We develop an algorithm, called TopoSense, which uses topology information and layered streams to control congestion within an administrative domain. Our algorithm presents a new model for multicast congestion control as it does not involve on-router computation as opposed to other approaches which require router support. We evaluate our algorithm using ns, the network simulator. Our results indicate that topology information is very useful in understanding the dynamics of multicast congestion and can be used for efficient traffic management.

A dynamic pricing scheme for e-content at multiple levels-of-service

Businesses selling multimedia rich software or e-content are growing in the Internet. The e-content can be downloaded or streamed immediately after an on-line transaction. Since Internet connection speeds are variable, ranging from dial-up access speeds to broadband speeds, a content provider may provide content at different speeds or levels-of-service. Providers offering content at different service levels face two major challenges: (1) revenue maximization, and (2) resource provisioning. In this article, we discuss how these challenges are inter-related, and develop a formal model for pricing and resource provisioning in content delivery systems. We use simulations to study price and resource utilization dynamics in systems implementing our model. We present simulation results in a variety of scenarios that illustrate the scalability and robustness of our model.

A revenue-based model for making resource investment decisions in IP networks

Capacity planning is a critical task in network management. It identifies how much capacity is needed to match future traffic demand. It directly affects customer satisfaction and revenues. In this work we present a network usage analysis tool called Dynamic Netvalue Analyzer (DNA), which helps alleviate a big problem that network engineers and marketing executives face - making optimal resource investment decisions. Marketing executives have to project customer growth while network engineers have to project traffic volume based on the entire customer population. DNA helps the prediction process by presenting actual network usage data from a business perspective, in a form that is useful to both network engineers and marketing executives. Using these projections, decisions on how to upgrade resources can be made. We show that information from DNA can be used to: (1) quantify revenue earned on each link; (2) quantify return-on-investment on performing a link upgrade; and (3) quantify the loss due to customer dissatisfaction when a link is not upgraded. We also illustrate how these formulations based on business information can be used to improve capacity planning decisions.

On pricing algorithms for batched content delivery systems

Abstract Businesses offering video-on-demand (VoD) and downloadable-CD sales are growing in the Internet. Batching of requests coupled with a one-to-many delivery mechanism such as multicast can increase scalability and efficiency. There is very little insight into pricing such services in a manner that utilizes network and system resources efficiently while also maximizing 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 is unlikely to 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 work, we propose a pricing scheme that combines the best features of the different pricing schemes and analyze its performance.

Pricing and resource provisioning for delivering E-content on-demand with multiple levels-of-service

Businesses selling multimedia rich software or e-content are growing in the Internet. The e-content can be downloaded or streamed immediately after an on-line transaction. Since Internet connection speeds are variable, ranging from dial-up access speeds to broadband speeds, a content provider may provide different levels-of-service (LoS) for the same content. If a provider offers service at different LoS, for example at 56 kbps and 128 kbps, how should the price of the service be set such that the provider makes the most money? In addition, how should the server resources be provisioned among the different service levels? In this paper, we address such pricing and resource provisioning issues for sellinge- content at multiple service levels.