Objective
To handle multiple
objectives like power and performance during VM placement. And minimizing both
underprovisioning and overprovisioning problems under the demand and price
uncertainty in cloud computing environments
Abstract
The recent emergence of
public cloud offerings, surge computing -outsourcing tasks from an internal
data center to a cloud provider in times of heavy load- has become more
accessible to a wide range of consumers. Deciding which workloads to outsource
to what cloud provider in such a setting, however, is far from trivial. The
objective of this decision is to maximize the utilization of the internal data
center and to minimize the cost of running the outsourced tasks in the cloud,
while fulfilling the applications' quality of service constraints. We examine
this optimization problem in a multi-provider hybrid cloud setting with
deadline-constrained and preemptible but non-provider-migratable workloads that
are characterized by memory, CPU and data transmission requirements. Linear
programming is a general technique to tackle such an optimization problem.
At present, it is however
unclear whether this technique is suitable for the problem at hand and what the
performance implications of its use are. We therefore analyze and propose a
binary integer program formulation of the scheduling problem and evaluate the
computational costs of this technique with respect to the problem's key
parameters. We found out that this approach results in a tractable solution for
scheduling applications in the public cloud, but that the same method becomes
much less feasible in a hybrid cloud setting due to very high solve time
variances.
Existing System:
Existing works on
autonomic management systems for virtualized server environments tackle the
allocation and placement of virtual servers from different perspectives. Many
papers differ from our work insofar as they either focus on one specific type
of applications or they do not consider the problem of dynamic provisioning or
the possibility of resource contention between several applications with
independent performance goals. For Consider an IT environment where we are
given a set of VMs that need to be placed among a set of physical servers in
the data center. Let the set of VMs in the environment be denoted by V = {V1,
V2, . . . V|V|}. Each VM requires a
certain amount of CPU resource on the machine where it is placed.
Disadvantage:
·
No control over the business
assets (data!). The main assets in every company are its data files with
valuable customer information.
·
Risk of data loss due to
improper backups or system failure in the virtualized environment.
·
High cost and loss of control.
Proposed System:
The
proposed OCRP algorithm is presented.
- Minimize the Expected Resource provisioning cost.
- Reduce on demand and oversubscribed cost.
- Consider multivariate uncertainly.
- Meet the decision maker’s risk preference make decision makers are risk averse.
In order to provision resources for a workflow
application, the resource requirements of the
Application must be formally specified. In addition
to the individual characteristics of the resources, such as CPU architecture,
memory and disk space, the specification must include the number of resources required
When the resources will be needed, and How long the resources will be used. Developing such a specification involves a
cost/performance tradeoff for the application developer. Acquiring extra
resources can improve performance by providing more opportunities for
concurrent execution, but these results in higher allocation costs and has the
potential to leave some resources underutilized.
Advantage:
·
A flexible, scalable
infrastructure management platform has been architected and a prototype implemented
·
Measurement of resource usage
and end user activities lies hands of the cloud service provider.
·
Opaque cost structure due to
highly flexible usage of cloud services.
·
Stable of cost structure.
Hardware Requirements
- Main Processor : 2GHz
- Ram : 512 MB (min)
- Ports : 1 Serial Port
- Hard Disk : 80 GB
Software Requirements
·
Language
:
Java
·
Web Server
: Tomcat 6
·
Operating System :
Windows XP
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