A PROFIT MAXIMIZATION SCHEME WITH GUARANTEED QUALITY OF SERVICE IN CLOUD COMPUTING

A PROFIT MAXIMIZATION SCHEME WITH GUARANTEED QUALITY OF
SERVICE IN CLOUD COMPUTING
Abstract: An effective and efficient way to provide computing resources and services to
customers on demand, cloud computing has become more and more popular. From cloud service
providers’ perspective, profit is one of the most important considerations, and it is mainly
determined by the configuration of a cloud service platform under given market demand.
However, a single long-term renting scheme is usually adopted to configure a cloud platform,
which cannot guarantee the service quality but leads to serious resource waste. In this paper, a
double resource renting scheme is designed firstly in which short-term renting and long-term
renting are combined aiming at the existing issues. This double renting scheme can effectively
guarantee the quality of service of all requests and reduce the resource waste greatly. Secondly, a
service system is considered as an M/M/m+D queuing model and the performance indicators that
affect the profit of our double renting scheme are analyzed, e.g., the average charge, the ratio of
requests that need temporary servers, and so forth. Thirdly, a profit maximization problem is
formulated for the double renting scheme and the optimized configuration of a cloud platform is
obtained by solving the profit maximization problem. Finally, a series of calculations are
conducted to compare the profit of our proposed scheme with that of the single renting scheme.
The results show that our scheme can not only guarantee the service quality of all requests, but
also obtain more profit than the latter.
Existing System:
In Many existing research they only consider the power consumption cost. As a major difference
between their models and ours, the resource rental cost is considered in this paper as well, since
it is a major part which affects the profit of service providers. The traditional single resource
renting scheme cannot guarantee the quality of all requests but wastes a great amount of
resources due to the uncertainty of system workload. To overcome the weakness, we propose a
double renting scheme as follows, which not only can guarantee the quality of service
completely but also can reduce the resource waste greatly.

Proposed System:
In this section, we first propose the Double-Quality- Guaranteed (DQG) resource renting scheme
which combines long-term renting with short-term renting. The main computing capacity is
provided by the long-term rented servers due to their low price. The short-term rented servers
provide the extra capacity in peak period
Advantages:
In proposed system we are using the Double-Quality-Guaranteed (DQG) renting scheme can
achieve more profit than the compared Single-Quality-Unguaranteed (SQU) renting scheme
in the premise of guaranteeing the service quality completely.
Problem Statement: A profit maximization function is defined to find an optimal combination
of the server size R and the queue capacity K such that the profit is maximized. However, this
strategy has further implications other than just losing the revenue from some services, because it
also implies loss of reputation and therefore loss of future customers. In , Cao et al. treated a
cloud service platform as an M/M/m model, and the problem of optimal multiserver
configuration for profit maximization was formulated and solved. This work is the most relevant
work to ours, but it adopts a single renting scheme to configure a multiserver system, which
cannot adapt to the varying market demand and leads to low service quality and great resource
waste. To overcome this weakness, another resource management strategy is used in , which is
cloud federation. Using federation, different providers running services that have complementary
resource requirements over time can mutually collaborate to share their respective resources in
order to fulfill each one’s demand . However, providers should make an intelligent decision
about utilization of the federation (either as a contributor or as a consumer of resources)
depending on different conditions that they might face, which is a complicated problem.

Scope: In this paper, we only consider the profit maximization problem in a homogeneous cloud
environment, because the analysis of a heterogenous environment is much more complicated
than that of a homogenous environment. However, we will extend our study to a heterogenous
environment in the future.
Architecture:
Implementation Of Modules:
1. Cloud computing,

2. queuing model.
3. Business Service Module
4. Cloud customer Module.
5. Infrastructure Service Provider Module.
Cloud Computing:
Cloud computing describes a type of outsourcing of computer services, similar to the way in
which the supply of electricity is outsourced. Users can simply use it. They do not need to worry
where the electricity is from, how it is made, or transported. Every month, they pay for what they
consumed. The idea behind cloud computing is similar: The user can simply use storage,
computing power, or specially crafted development environments, without having to worry how
these work internally. Cloud computing is usually Internet-based computing. The cloud is a
metaphor for the Internet based on how the internet is described in computer network diagrams;
which means it is an abstraction hiding the complex infrastructure of the internet. It is a style of
computing in which IT-related capabilities are provided “as a service”, allowing users to access
technology-enabled services from the Internet ("in the cloud")without knowledge of, or control
over the technologies behind these servers.
Queuing model:
we consider the cloud service platform as a multiserver system with a service request queue. The
clouds provide resources for jobs in the form of virtual machine (VM). In addition, the users
submit their jobs to the cloud in which a job queuing system such as SGE, PBS, or Condor is
used. All jobs are scheduled by the job scheduler and assigned to different VMs in a centralized
way. Hence, we can consider it as a service request queue. For example, Condor is a specialized
workload management system for computeintensive jobs and it provides a job queueing
mechanism, scheduling policy, priority scheme, resource monitoring, and resource management.
Users submit their jobs to Condor, and Condor places them into a queue, chooses when and

where to run them based upon a policy. An M/M/m+D queueing model is build for our
multiserver system with varying system size. And then, an optimal configuration problem of
profit maximization is formulated in which many factors are taken into considerations, such as
the market demand, the workload of requests, the server-level agreement, the rental cost of
servers, the cost of energy consumption, and so forth. The optimal solutions are solved for two
different situations, which are the ideal optimal solutions and the actual optimal solutions.
Business Service Providers Module:
Service providers pay infrastructure providers for renting their physical resources, and charge
customers for processing their service requests, which generates cost and revenue, respectively.
The profit is generated from the gap between the revenue and the cost.In this module the service
providers considered as cloud brokers because they can play an important role in between cloud
customers and infrastructure providers ,and he can establish an indirect connection between
cloud customer and infrastructure providers.
Infrastructure Service Provider Module: In the three-tier structure, an infrastructure provider
the basic hardware and software facilities. A service provider rents resources from infrastructure
providers and prepares, a set of services in the form of virtual machine (VM). Infrastructure
providers provide two kinds of resource renting schemes, e.g., long-term renting and short-term
renting. In general, the rental price of long-term renting is much cheaper than that of short-term
renting.
Cloud Customers: A customer submits a service request to a service provider which delivers
services on demand. The customer receives the desired result from the service provider with
certain service-level agreement, and pays for the service based on the amount of the service and
the service quality.

Conclusion: Maximize the profit of service providers, this paper has proposed a novel Double-
Quality-Guaranteed (DQG) renting scheme for service providers. This scheme combines short-
term renting with long-term renting, which can reduce the resource waste greatly and adapt to the
dynamical demand of computing capacity. An M/M/m+D queueing model is build for our
multiserver system with varying system size. And then, an optimal configuration problem of
profit maximization is formulated in which many factors are taken into considerations, such as
the market demand, the workload of requests, the server-level agreement, the rental cost of
servers, the cost of energy consumption, and so forth. The optimal solutions are solved for two
different situations, which are the ideal optimal solutions and the actual optimal solutions. In
addition, a series of calculations are conducted to compare the profit obtained by the DQG
renting scheme with the Single-Quality-Unguaranteed (SQU) renting scheme. The results show
that our scheme outperforms the SQU scheme in terms of both of service quality and profit.

A PROFIT MAXIMIZATION SCHEME WITH GUARANTEED QUALITY OF SERVICE IN CLOUD COMPUTING

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A PROFIT MAXIMIZATION SCHEME WITH GUARANTEED QUALITY OF SERVICE IN CLOUD COMPUTING