Lee Rhodes

Dynamic netvalue analyzer - A pricing plan modeling tool for isps using actual network usage data

The current situation in the Internet service provider market shows that Internet service providers (ISPs) are still struggling to find sustainable business models. The challenge for them is to find the right prices and pricing plans for their network services. In order to satisfy business objectives, prices and pricing plans have to be designed such that they are attractive to end-users but also guarantee revenues. In order to achieve that, it is necessary to have a precise understanding of the usage behavior of end-users under different pricing plans. In this paper, we present existing pricing plans in the market, and discus their advantages and shortcomings. As the result of our analysis, we propose a new pricing plan, which is attractive to end-users as well as ISPs. However, in order to smooth the migration of end-users from existing pricing plans to the new plan, marketing and business managers need support. This could be done with the help of a business intelligence tool, which can provide them with detailed information about user behavior. Such a tool for supporting service providers in pricing network services is presented in this paper. This tool, which is called HP OpenView Dynamic Netvalue Analyzer, helps to analyze and model pricing plans and prices. We demonstrate capability of DNA by describing the creation of a tiered, usage-based pricing plan based on real network usage data under a flat-rate pricing plan.

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.

A Stream Processor for Extracting Usage Intelligence From High-Momentum Internet Data

The data streams of the Internet are quite large and present significant challenges to those wishing to analyze these streams on a continuous basis. Opportunities for analysis for a Network Service Provider include understanding subscriber usage patterns for developing new services, network demand flows for network operations and capacity planning functions, and early detection of network security breaches. The conventional analysis paradigm of store first, then analyze later has significant cost and latency issues when analyzing these high-momentum streams. This article presents a deployed architecture for a general purpose stream processor that includes dynamically configurable Capture Models that can be tailored for compact collection of statistics of the stream in real time. The highly configurable flow processing model is presented with numerous examples of how multiple streams can be merged and split based on the requirements at hand.