NDLI: Pricing cloud bandwidth reservations under demand uncertainty

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Pricing cloud bandwidth reservations under demand uncertainty

Content Provider	ACM Digital Library
Author	Li, Baochun Feng, Chen Niu, Di
Abstract	In a public cloud, bandwidth is traditionally priced in a pay-as-you-go model. Reflecting the recent trend of augmenting cloud computing with bandwidth guarantees, we consider a novel model of cloud bandwidth allocation and pricing when explicit bandwidth reservation is enabled. We argue that a tenant's utility depends not only on its bandwidth usage, but more importantly on the portion of its demand that is satisfied with a performance guarantee. Our objective is to determine the optimal policy for pricing cloud bandwidth reservations, in order to maximize social welfare, i.e., the sum of the expected profits that can be made by all tenants and the cloud provider, even with the presence of demand uncertainty. The problem turns out to be a large-scale network optimization problem with a coupled objective function. We propose two new distributed solutions --- based on chaotic equation updates and cutting-plane methods --- that prove to be more efficient than existing solutions based on consistency pricing and subgradient methods. In addition, we address the practical challenge of forecasting demand statistics, required by our optimization problem as input. We propose a factor model for near-future demand prediction, and test it on a real-world video workload dataset. All included, we have designed a fully computerized trading environment for cloud bandwidth reservations, which operates effectively at a fine granularity of as small as ten minutes in our trace-driven simulations.
Starting Page	151
Ending Page	162
Page Count	12
File Format	PDF
ISBN	9781450310970
DOI	10.1145/2254756.2254776
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-06-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Bandwidth pricing Cloud computing Distributed optimization Prediction Time series
Content Type	Text
Resource Type	Article

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