A theory of cloud bandwidth pricing for video-on-demand providers

Current-generation cloud computing is offered with usage-based pricing, with no bandwidth capacity guarantees, which is however unappealing to bandwidth-intensive applications such as video-on-demand (VoD). We consider a new type of service where VoD providers, such as Netflix and Hulu, make reservations for bandwidth guarantees from the cloud at negotiable prices to support continuous media streaming. We argue that it is beneficial to multiplex such bandwidth reservations in the market using a profit-making broker while controlling the performance risks. We ask the question-in such a market, how much should each VoD provider pay for bandwidth reservation? We prove that the market has a unique Nash equilibrium where the bandwidth reservation price for a VoD provider critically depends on its demand correlation to the market. Real-world traces verify that our theory can significantly lower the market price for cloud bandwidth reservation.

[1]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[2]  Helen J. Wang,et al.  SecondNet: a data center network virtualization architecture with bandwidth guarantees , 2010, CoNEXT.

[3]  Rittwik Jana,et al.  Exploiting virtualization for delivering cloud-based IPTV services , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[4]  Timothy Roscoe,et al.  Resource overbooking and application profiling in shared hosting platforms , 2002, OSDI '02.

[5]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[6]  Chuan Wu,et al.  UUSee: Large-Scale Operational On-Demand Streaming with Random Network Coding , 2010, 2010 Proceedings IEEE INFOCOM.

[7]  Meng Wang,et al.  Consolidating virtual machines with dynamic bandwidth demand in data centers , 2011, 2011 Proceedings IEEE INFOCOM.

[8]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[9]  George E. P. Box,et al.  Time Series Analysis: Box/Time Series Analysis , 2008 .

[10]  Baochun Li,et al.  Demand forecast and performance prediction in peer-assisted on-demand streaming systems , 2011, 2011 Proceedings IEEE INFOCOM.

[11]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[12]  Ibrahim Matta,et al.  Describing and forecasting video access patterns , 2011, 2011 Proceedings IEEE INFOCOM.

[13]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[14]  Baochun Li,et al.  Quality-assured cloud bandwidth auto-scaling for video-on-demand applications , 2012, 2012 Proceedings IEEE INFOCOM.

[15]  Charlene Xie,et al.  Generalized Autoregressive Conditional Heteroskedasticity in Credit Risk Measurement , 2009, 2009 International Conference on Management and Service Science.

[16]  Baochun Li,et al.  Understanding demand volatility in large VoD systems , 2011, NOSSDAV '11.

[17]  Hitesh Ballani,et al.  Towards predictable datacenter networks , 2011, SIGCOMM 2011.