ABSTRACT:
Cloud computing allows business customers to scale
up and down their resource usage based on needs. Many of the touted gains in
the cloud model come from resource multiplexing through virtualization
technology. In this paper, 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.
We introduce the concept of “skewness” to measure the unevenness in the
multidimensional resource utilization of a server. By minimizing skewness, we
can combine different types of workloads nicely and improve the overall
utilization of server resources. We develop a set of heuristics that prevent
overload in the system effectively while saving energy used. Trace driven
simulation and experiment results demonstrate that our algorithm achieves good
performance.
EXISTING SYSTEM:
Virtual machine monitors (VMMs) like Xen
provide a mechanism for mapping virtual machines (VMs) to physical resources.
This mapping is largely hidden from the cloud users. Users with the Amazon EC2
service, for example, do not know where their VM instances run. It is up to the
cloud provider to make sure the underlying physical machines (PMs) have
sufficient resources to meet their needs. VM live migration technology makes it
possible to change the mapping between VMs and PMs While applications are
running. The capacity of PMs can also be heterogeneous because multiple
generations of hardware coexist in a data center.
DISADVANTAGES
OF EXISTING SYSTEM:
·
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.
·
This is challenging when the resource
needs of VMs are heterogeneous due to the diverse set of applications they run
and vary with time as the workloads grow and shrink. The two main disadvantages
are overload avoidance and green computing.
PROPOSED SYSTEM:
In this paper, we present the design and
implementation of an automated resource management system that achieves a good
balance between the two goals. Two goals are overload avoidance and green
computing.
1.
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.
2.
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.
ADVANTAGES
OF PROPOSED SYSTEM:
We make the following contributions:
v We
develop a resource allocation system that can avoid overload in the system
effectively while minimizing the number of servers used.
v 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.
v We
design a load prediction algorithm that can capture the future resource usages
of applications accurately without looking inside the VMs. The algorithm can
capture the rising trend of resource usage patterns and help reduce the
placement churn significantly.
SYSTEM ARCHITECTURE:
MODULES
Ø VM
SCHEDULER
Ø PREDICTOR
Ø HOSTSPOT
SOLVER
Ø COLDSPOT
SOLVER
Ø MIGRATION
LIST
MODULES
DESCRIPTION
VM SCHEDULER
VM Scheduler run and
invoked periodically receives from the user LNM (Local Node Manager)the
resource demand history of VMs(virtual machines), the capacity and the load
history of PMs(personal machine), and the current layout of VMs on PMs. Then it
can forward the request to predictor
PREDICTOR
The predictor predicts
the future resource demands of VMs and the future load of PMs based on past
statistics. We compute the load of a PM by aggregating the resource usage of
its VMs. The details of the load prediction algorithm will be described in the
next section. The LNM at each node first attempts to satisfy the new demands
locally by adjusting the resource allocation of VMs sharing the same VMM. Xen
can change the CPU allocation among the VMs by adjusting their weights in its
CPU scheduler. The MM Alloter on domain 0 of each node is responsible for
adjusting the local memory allocation
HOSTSPOT SOLVER
The hot spot solver in
our VM Scheduler detects if the resource utilization of any PM is above the hot
threshold (i.e., a hot spot). If so, some VMs running on them will be migrated
away to reduce their load. Then it can give the request to coldspot solver
COLDSPOT SOLVER
The cold spot
solver checks if the average utilization of actively used PMs (APMs) is below
the green computing threshold. If so, some of those PMs could potentially be
turned off to save energy. It identifies the set of PMs whose utilization is
below the cold threshold (i.e., cold spots) and then attempts to migrate away
all their VMs then it forward request to migration list
MIGRATION LIST
When migration list can receive the request from coldspot solver and it can
compiles list of VMs and migration list can passes it response to the Usher
CTRL (user controller) for executionSYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
ü Processor - Pentium –IV
ü Speed - 1.1
Ghz
ü RAM - 256
MB(min)
ü Hard
Disk - 20
GB
ü Key
Board - Standard
Windows Keyboard
ü Mouse - Two
or Three Button Mouse
ü Monitor - SVGA
SOFTWARE CONFIGURATION:-
ü Operating System :
Windows XP
ü Programming Language : JAVA
ü Java Version : JDK 1.6 & above.
REFERENCE:
Zhen Xiao, Senior Member, IEEE, Weijia Song, and Qi
Chen-“Dynamic Resource Allocation Using Virtual Machines for Cloud Computing
Environment”- IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED
SYSTEMS, VOL. 24, NO. 6, JUNE 2013.
SEE THE PROJECT OUTPUT HERE
