Service Adoption and Pricing of Content Delivery Network (CDN) Services

  • Authors:

  • Kartik Hosanagar;John Chuang;Ramayya Krishnan;Michael D. Smith

  • Affiliations:

  • Operations and Information Management, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104;School of Information Management and Systems, University of California at Berkeley, Berkeley, California 94720;H. John Heinz III School of Public Policy and Management, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213;H. John Heinz III School of Public Policy and Management, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

  • Venue:
  • Management Science
  • Year:
  • 2008

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Abstract

Content delivery networks (CDNs) are a vital component of the Internet's content delivery value chain, servicing nearly a third of the Internet's most popular content sites. However, in spite of their strategic importance, little is known about the optimal pricing policies or adoption drivers of CDNs. We address these questions using analytic models of CDN pricing and adoption under Markovian traffic and extend the results to bursty traffic using numerical simulations. When traffic is Markovian, we find that CDNs should provide volume discounts to content providers. In addition, the optimal pricing policy entails lower emphasis on value-based pricing and greater emphasis on cost-based pricing as the relative density of content providers with high outsourcing costs increases. However, when traffic is bursty and content providers have varying levels of traffic burstiness, volume discounts may be suboptimal and may even be replaced by volume taxes. Finally, when there is heterogeneity in burstiness across content providers, a pricing policy that accounts for both the mean and variance in traffic such as percentile-based pricing is more profitable than traditional volume-based pricing (metering bytes delivered in a given time window). This finding is in contrast to the current practices of many CDN firms that use traditional volume-based pricing.