28-08-2014, 10:42 AM
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment Seminar Report
Dynamic Resource Allocation.pdf (Size: 138.04 KB / Downloads: 50)
OBJECTIVE
we present a system that uses virtualization technology to allocate data center
resources dynamically based on application demands and support green computing by
optimizing the number of servers in use
SYNOPSIS
we issue to achieve the goal of management multiple virtualization platforms
and multiple virtual machine migrations across physical machines without disruption method.
We discuss that ensure load balance when multiple virtual machines run on multiple physical
machines. we present a system which is implementation of optimization with Dynamic
Resource Allocation dealing with virtualization machines on physical machines, practice
DRA method in this system. The dynamic results confirmed that the virtual machine which
loading becomes too high it will automatically migrated to another low loading physical
machine without service interrupt. And let total physical machine loading reaching balance.
It is however unclear whether this technique is suitable for the problem at hand and
what the performance implications of its use are. We found out that this approach results in a
tractable solution for scheduling applications in the public cloud. In the same method
becomes much less feasible in a hybrid cloud setting due to very high solve time variances. In
the cloud model is expected to make such practice unnecessary by offering automatic scale
up and down in response to load variation. It also saves on electricity which contributes to a
significant portion of the operational expenses in large data centres. We develop a set of
heuristics that prevent overload in the system effectively while saving energy used. It trace
driven simulation and experiment results demonstrate that our algorithm achieves good
performance.
EXISTING SYSTEM
Existing works on autonomic management systems for virtualized server
environments tackle the allocation and placement of virtual servers from different
perspectives. Virtual machine monitors (VMMs) like Xen provide a mechanism for mapping
virtual machines (VMs) to physical resources. Mapping is largely hidden from the cloud
users. Users with the Amazon EC2 service Example do not know where their VM instances
runs. It’s up to the cloud provider to make sure the underlying physical machines (PMs) have
sufficient resources to meet their needs.
LIMITATIONS
A policy issue remains as how to decide the mapping adaptively so that the resource
demands of VMs are met while the number of PMs used is minimized.
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
We present the design and implementation of an automated resource management
system that achieves a good balance between the two goals. We make the following
contributions. Overload avoidance: The capacity of a PM should be sufficient to satisfy the
resource needs of all VMs running on it. Otherwise, the PM is overloaded and can lead to
degraded performance of its VMs. Green computing: The number of PMs used should be
minimized as long as they can still satisfy the needs of all VMs. Idle PMs can be turned off to
save energy. We develop a resource allocation system that can avoid overload in the system
effectively while minimizing the number of servers used. We introduce the concept of
“skewness” to measure the uneven utilization of a server. By minimizing skewness, we can
improve the overall utilization of servers in the face of multidimensional resource constraints.
We are using cloudsim for implementations.
HARDWARE SPECIFICATION
Main Processor : 2GHz
Ram : 512 MB (min)
Hard Disk : 80 GB
SOFTWARE SPECIFICATION
Language : Java
Web Server : Tomcat 6
Operating System : Windows 7 32 Bit
CloudSim