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Dynamic Cloud Pricing for Revenue Maximization
Abstract
In cloud computing, a provider leases its computing resources in the form of virtual machines to
users, and a price is charged for the period they are used. Though static pricing is the dominant
pricing strategy in today's market, intuitively price ought to be dynamically updated to improve
revenue. The fundamental challenge is to design an optimal dynamic pricing policy, with the
presence of stochastic demand and perishable resources, so that the expected long-term revenue
is maximized. In this paper, we make three contributions in addressing this question. First, we
conduct an empirical study of the spot price history of Amazon, and find that surprisingly, the
spot price is unlikely to be set according to market demand. This has important implications on
understanding the current market, and motivates us to develop and analyze market-driven
dynamic pricing mechanisms. Second, we adopt a revenue management framework from
economics, and formulate the revenue maximization problem with dynamic pricing as a
stochastic dynamic program. We characterize its optimality conditions, and prove important
structural results. Finally, we extend to consider a nonhomogeneous demand model.
Existing System
In cloud computing, a provider leases its computing resources in the form of virtual machines to
users, and a price is charged for the period they are used. Though static pricing is the dominant
pricing strategy in today's market, intuitively price ought to be dynamically updated to improve
revenue. The fundamental challenge is to design an optimal dynamic pricing policy, with the

2. presence of stochastic demand and perishable resources, so that the expected long-term revenue
is maximized.
Proposed System
In this paper, we make three contributions in addressing this question. First, we conduct an
empirical study of the spot price history of Amazon, and find that surprisingly, the spot price is
unlikely to be set according to market demand. This has important implications on understanding
the current market, and motivates us to develop and analyze market-driven dynamic pricing
mechanisms. Second, we adopt a revenue management framework from economics, and
formulate the revenue maximization problem with dynamic pricing as a stochastic dynamic
program. We characterize its optimality conditions, and prove important structural results.
Finally, we extend to consider a nonhomogeneous demand model.
System Configuration:-
Hardware Configuration:-
 Processor - Pentium –IV
 Speed - 1.1 Ghz
 RAM - 256 MB(min)
 Hard Disk - 20 GB
 Key Board - Standard Windows Keyboard
 Mouse - Two or Three Button Mouse
 Monitor - SVGA
Software Configuration:-
 Operating System : Windows XP
 Coding Language : net, C#.net
 Tool : Visual Studio 2010