Dynamic Cloud Pricing for Revenue Maximization
Abstract
• We study the infinite horizon dynamic pricing problem for an infrastructure
cloud provider in the emerging cloud computing paradigm. The cloud
provider, such as Amazon, provides computing capacity in the form of
virtual instances and charges customers a time-varying price for the period
they use the instances. The provider’s problem is then to find an optimal
pricing policy, in face of stochastic demand arrivals and departures, so that
the average expected revenue is maximized in the long run. We adopt a
revenue management framework to tackle the problem. Optimality
conditions and structural results are obtained for our stochastic formulation,
which yield insights on the optimal pricing strategy. Numerical results
verify our analysis and reveal additional properties of optimal pricing
policies for the infinite horizon case.
Introduction
• In our previous work, we addressed the problem in a finite horizonsetting. In this work we extend the analysis to consider the infinitehorizon setting, where the optimal price is only a function of thesystem utilization and not a function of time.
• The objective is to maximize the average expected revenue rate inthe limit as time goes to infinity.
• Our contributions in this paper are two-fold. First, we present aninfinite horizon stochastic dynamic program for the revenuemaximization problem in the cloud, with stochastic demand arrivalsand departures. We characterize optimality conditions for theproblem and prove important structural results, such as the mono-tonicity of optimal price and relative rewards. Second, we conductnumerical studies to verify our analysis. The results also revealseveral interesting observations regarding the interplay between thedegree of demand dynamics and the optimal pricing policy.Dynamic pricing is more important and rewarding when theexpected dynamics is significant compared with the system capacity.
EXISTING SYSTEM
• Though static pricing is the dominant strategy today, dynamic
pricing emerges as an attractive alternative to better cope with
unpredictable customer demand.
• The motivation is intuitive and simple:
• Pricing should be leveraged strategically to influence demand to
better utilize unused capacity, and generate more revenue.
• Indeed, Amazon EC2 has introduced a “spot pricing” feature, where
the spot price for a virtual instance is dynamically updated to match
supply and demand as claimed in.
DISADVANTAGES OF EXISTING SYSTEM
• A provider naturally wishes to set a higher price to get a higher profit
margin; yet in doing so, it also bears the risk of discouraging demand in the
future.
• It is nontrivial to balance this intrinsic tradeoff with perishable capacity
and stochastic demand.
PROPOSED SYSTEM
• Cloud computing poses new challenges to solving revenue maximizationproblems. First, little is known about how the spot price is adjusted, andwhat factors are considered in the pricing algorithm, by a real-worldprovider such as Amazon.
• Also, little is known about demand statistics, and how demand reacts toprice changes. In fact, though Amazon publishes its spot price history, veryfew insights are gained on important aspects related to modeling of themarket.
• Second, for a cloud provider, revenue not only depends on the number ofcustomers, but also on the duration of usage.
• Thus, not only the arrival but also the departure of demand is stochastic,and have to be taken into account when collecting revenue. This clearlyadds to the modeling complexity.
• we consider the scenario where the cloud provider with fixed capacityupdates the spot price according to market demand in this paper.
• Our second contribution is that we formulate the revenue maximization
problem as a finite-horizon stochastic dynamic program, with stochastic
demand arrivals and departures. We characterize optimality conditions for
the stochastic problem and prove important structural results.
• We also extend our model to the case with non-homogeneous demand. We
conduct an asymptotic analysis on this more general but difficult problem.
• We prove a surprising result that when the demand arrival and departure
rates are linear with system utilization, i.e., number of existing instances,
the optimal price is only a function of time and is independent of the
system utilization
ADVANTAGES OF PROPOSED SYSTEM
• The optimal pricing policy exhibits time and utilization monotonicity, and
the optimal revenue has a concave structure.
• The fundamental tradeoff between pricing to the future to attract more
revenue from future demand, and pricing to the present to extract more
revenue from existing customers.
Conclusion
• In this paper, we presented an infinite horizon revenue maximization
framework to tackle the dynamic pricing problem in an infrastructure
cloud. The technical challenge compared to previous pricing work is that
prices are charged on a usage time basis, and as a result the demand
departure process has to be explicitly modelled. An average reward
dynamic program is formulated for the infinite horizon case. Its optimality
conditions and structural results on optimal pricing policies were presented.
We showed that the relative rewards as well as the optimal price exhibit
mono-tonicity, which is resonant with previous results [6], [10]. We also
conducted numerical studies to verify the analysis, and illustrated the
importance of dynamic pricing especially in the strong demand dynamics
scenarios.
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
• Programming Language : JAVA
• Java Version : JDK 1.6 & above.
• Back end :MY SQL
Modules
Class diagram
user
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+registration()+view profile()+give rating()+view rating()+logout()
admin
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+add category()+add service()+rating()+logout()
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user
register
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view profile
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admin
Deployment diagram home
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Activity diagram
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useradmin
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Sequence diagram
user system admin
register
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login
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Collaboration diagram
adminuser system
1: register2: login
3: login
4: view profile
5: add category
6: give rating
7: add service
8: view rating
9: rating
10: logout
11: logout
State chart diagram
home
user admin
login
view profile
give rating
add category
add service
rating
view rating
logout
start
stop
Component diagram
home
register nodereceiver
sender
sending
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from before
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