IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS Price competition in an oligopoly market with multiple iaa s cloud providers
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - email@example.com-Visit Our Website: www.finalyearprojects.org
Published on: Mar 4, 2016
Transcripts - IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS Price competition in an oligopoly market with multiple iaa s cloud providers
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS
CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401
Visit: www.finalyearprojects.org Mail to:firstname.lastname@example.org
Price Competition in an Oligopoly Market with Multiple
IaaS Cloud Providers
As an increasing number of infrastructure-as-a-service (IaaS) cloud providers
start to provide cloud computing services, they form a competition market to
compete for users of these services. Due to different resource capacities and
service workloads, users may observe different finishing times for their cloud
computing tasks and experience different levels of service qualities as a
result. To compete for cloud users, it is critically important for each cloud
service provider to select an “optimal” price that best corresponds to their
service qualities, yet remaining attractive to cloud users. We characterize the
nature of non-cooperative competition in an IaaS cloud market, with a goal of
capturing how each IaaS cloud provider will select its optimal prices to
compete with the others. One of the possible types of cloud services provided
by today’s cloud providers, such as Amazon EC2 and Rackspace, is referred
to as infrastructure as a service (IaaS). Since a user’s cloud service demand
may be satisfied by any of these IaaS cloud providers, a rational user will
choose the one that maximizes its own net reward, i.e., its utility obtained by
choosing the IaaS cloud service minus its payment.In this More specifically,
we present an in-depth analytical study on the monopoly, duopoly, and
oligopoly markets, in which multiple IaaS cloud providers are competing
with one another. Since the pricing strategy of a cloud provider depends on
its competitors, we take a game theoretic perspective to study the strategic
Existing clouds focus on the provision of web services targeted to developers,
such as Amazon Elastic Compute Cloud (EC2), or the deployment of servers,
such as Go Grid.
There are two major challenges when trying to define an optimal pricing
scheme for the cloud caching service.
The first is to define a simplified enough model of the price demand
dependency, to achieve a feasible pricing solution, but not oversimplified model
that is not representative
Closely related to cloud computing is research on accounting in wide-area
networks that offer distributed services. Mariposa discusses an economy for
querying in distributed databases.
A static pricing scheme cannot be optimal if the demand for services has
deterministic seasonal fluctuations.
Static pricing results in an unpredictable and, therefore, uncontrollable
behavior of profit.
Dynamic pricing can lead to customer alienation. If customers realize they
paid higher prices than others for the same solution, they may demand their
money back or spread negative messages in the marketplace.
Optimal resource allocation for cloud users in VM-based IaaS clouds, with full
awareness of different prices charged by cloud providers.
We first consider the case of a duopoly cloud market, in which two IaaS cloud
providers compete with each other, with a similar game theoretic analysis as
the monopoly case.
Dynamic pricing often is referred to as discriminatory pricing because it allows
you to maximize profits with each customer.
This approach is common in event promotions: If initial demand is low, facility
or event managers work to sell off open seats to generate whatever revenue is
Existing papers were concerned with the problem of how optimal pricing in the
cloud can be achieved.
Strength of dynamic pricing is the ability to adjust prices for service projects or
products based on the time and costs involved or fluctuating demand.
A novel demand-pricing model designed for cloud caching services and the
problem formulation for the dynamic pricing scheme that maximizes profit and
incorporates the objective for user satisfaction.
An efficient solution to the pricing problem, based on non-linear programming,
adaptable to time changes.
A correlation measure for cache structures that is suitable for the cloud cache
pricing scheme and a method for its efficient computation.
An experimental study which shows that the dynamic pricing scheme out-performs
any static one by achieving 2 orders of magnitude more profit per time
SYSTEM : Pentium IV 2.4 GHz
HARD DISK : 40 GB
RAM : 256 MB
Operating system : Windows 7
IDE : Microsoft Visual Studio 2010
Database : Sql server 2005
Coding Language : C#.NET.