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International Journal Of Engineering And Computer Science ISSN:2319-7242
Volume 4 Issue 1 January 2015, Page No. 9966-9970
Double Threshold Based Load Balancing Approach by Using VM
Migration for the Cloud Computing Environment
Rajyashree1, Vineet Richhariya2
1
Lakshmi Narain College of Technology, Bhopal, India
rajyashree.singh.1@gmail.com
2
Lakshmi Narain College of Technology, Bhopal, India
vineetrich100@gmail.com
Abstract: Cloud computing is emerging as a new paradigm for next generation computing in the field of computer science and information
technology because of their attractive services such as adaptive, online, value added and pay as use scheme. Virtualization is the core
technology in the cloud computing, which allow the sharing of physical resources. With the help of virtualization single physical device can
be share by the multiple users. So it increased the resource utilization. Cloud is a very large in size and having complex structure. Cloud
resources are heterogeneous and geographically distributed and the resource demanded by the user may change dynamically on runtime. So
the resource management and the resource scheduling in such a large-scale distributed environment is a very challenging task. In cloud
environment each data center can contain number of host, so there may be a situation when some hosts are overloaded and some are
underloaded. To avoid this situation we proposed a double threshold based load balancing approach, where threshold is decided based on
the utilization. This approach motivated by the fact that overloaded situation can’t be avoided, but can be control by assigning the dynamic
threshold. For assigning the dynamic threshold we monitored host utilization for each 20 Second and assign the threshold based on the
utilization in the previous interval. Experiment result shows that our approaches reduce the energy consumption and minimize the number
of migration
Keywords: Virtual machine, Physical machine, Server consolidation, VM migration, Hot spot mitigation.
1. Introduction
Cloud computing [1, 2] emerging as a new paradigm for nextgeneration computing in the field of computer science and
information technology because of their attractive services such
as easy to use, online, on demand and pay as use scheme.
Cloud is a business model, which the on demand services to the
user. User can access these services any time at anywhere in the
world. Cloud support three types of services i.e. Software as a
Services (SaaS), Platform as a Services (PaaS) and
Infrastructure as a Services (IaaS) [3, 4]. It can be deployed in
three different way i.e. Private cloud, Public cloud and Hybrid
cloud [3, 4]. Private cloud is more secure than the public cloud.
Virtualization [5, 6] is the core technology in the cloud, which
allows the sharing of the physical resources. With the help of
virtualization single physical device can be share by the
multiple users. When any user demands for the resources
hypervisor or virtual machine monitor (VMM) create a VM
and bind the requested resources with the VM. Virtualization
can be classified in two types i.e. Full virtualization and
paravirtualization. Full virtualization is a technique in which a
complete installation of one machine is run on another
machine. In full virtualization, the entire system is emulated
(BIOS, drive, and so on), but in paravirtualization, its
management module operates with an operating system that has
been adjusted to work in a virtual machine. Paravirtualization
typically runs better than the full virtualization model, simply
because in a fully virtualized deployment, all elements must be
emulated. Number of VM can be created in each host and each
VM behave like a physical machine.
Figure-1 Cloud service model
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of the resources. Priorities are assigned based on the size of
resources required in each dimension and volume. Higher
priority are assign to the VM which having a higher resource
requirement. This algorithm balance the resource but it will
increase the waiting time for the VM which having a lower
priority.
Figure- 2 Virtualization
Resources in the cloud are heterogeneous and geographically
distributed. Furthermore resource demand by the user can
change dynamically at run time. So the resource management
and the resource scheduling in such a large-scale distributed
environment is a very challenging task. Resources management
is a core function of any dynamic systems, it requires some
complex policies and decisions for the management of multi
dimension objective such as CPU, memory and network
bandwidth. For the proper utilization of the resources an
efficient load balancing strategy are required. Load balancing
approach can be static or dynamic. In the static approach fixed
threshold are used that can not changed with time to define a
percentage of resources that can be used. While in the dynamic
load balancing approach threshold can be changed with time.
Static load balancing approach is not suitable for the cloud,
where user request can change with time. Generally lower and
upper threshold are used to define the underloaded and
overloaded host respectively. So in this paper we proposed
double threshold based dynamic load balancing approach. If
load on the host is below the upper threshold all VM running
on that host are move to the other host, which is known as a
server consolidation. If load on the host is greater than the
upper threshold then our host is overloaded therefore some VM
has to be migrated. VM migration [7, 8] techniques are used in
the load balancing. VM migration is a technique where VM are
moves from one host to the another host.
Mayank Mishra et al. [11], Proposed a method for placing the
VM which is based on Vector theory. They are using resource
vectors TCV, RUV, RCV and RRV in the 3-D space which we
will use for making different VM placement decisions. One of
our prime goals while placing VM is to make the resource
utilization of PMs as balanced (along each resource dimension)
as possible, i.e., the RUV of a PM should be as closely aligned
to the TCV as possible. This would require that we have a way
of finding complementary VM for a PM. They proper balance
the resources but not focus to the server consolidation. So
energy consume by the data center is high.
T. Wood et al. [12], proposed an approach for the hot spot
mitigation know as sandpiper. Sandpiper use black and gray
box approach to monitor the host. They use the Xen
hypervisor. The monitoring engine is responsible for tracking
the processor, network and memory usage of each virtual
server. It also tracks the total resource usage on each physical
server by aggregating the usages of resident VMs. Problem
with this approach is that they only consider the cpu load to
calculate the load on host.
A. Beloglazov et al. [13], proposed an energy efficient load
balancing approach. They argue that average power consumed
by an idle server is 70% of power consumed by fully utilized
server. So power consumed by the data center can be
controlled by the proper load balancing approach. They used
fixed lower and upper threshold with the difference of 40
between lower and upper threshold. So if lower threshold is 30
than upper threshold is 70. This approach reduced the number
of migration but main problem with this approach is that they
used the fixed value of lower and upper threshold.
2. Literature Survey
3. Proposed Work
Paul et al. [9] proposed an algorithm which is focus on how to
utilize resource efficiently in cloud computing and gain
maximum profits. They treated task scheduling as a general
assignment problem to find the minimal cost. For this purpose,
they proposed a credit based scheduling algorithm, which
evaluate the entire group of tasks in the task queue and find the
minimal completion time of all tasks. The proposed scheduling
method considers the scheduling problem as an assignment
problem in mathematics where the cost matrix gives the cost of
a task to be assigned to a resource. Main aim of this algorithm
is to assign the resources for which the corresponding
completion time of all jobs is the minimum.
Problem with this algorithm is that it only considers the
probability of a resource to be free soon after executing a task
so that it will be available for the next waiting, but processing
time of a job are not considered.
A cloud environment consists of data center, VM and host.
Each data center can have multiple hosts and each host can run
number of VM. When user demands for the resources VMM
create a VM and assign to the user. VMM is a main part of the
virtualization, which handle all VM related task. So VM
creation, deletion and scheduling all are done by the VMM. It
is also responsible for the monitoring of the resources such as
CPU, RAM used by the VM and PM. Resource utilization can
be increased by the virtualization but for the proper utilization
of the resource an efficient load balancing approach is required
that take the decision according to the situation. Here we
proposed double threshold based dynamic load balancing
approach, where thresholds are calculated based on the host
utilization. If load on the host is below the upper threshold all
VM running on that host are move to the other host, which is
known as a server consolidation. If load on the host is greater
than the upper threshold then our host is overloaded therefore
some VM has to be migrated. When the load on the host is
below / above the threshold then VM migration strategy are
used. It is a technique where VM is move from one host to
another host. During the migration VM is suspended for a few
times, that will decrease the performance of the system. So an
Subramanian S. et al. [10] proposed apriority based algorithm
for the VM scheduling. In this approach VM are scheduled
according to the priority. They assigned some priority for each
job, which varies dynamically based on their capacity and load
factor. This dynamic priority concept leads to better utilization
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effective load balancing approach should minimize the number
of migration. Main objective of our approach is to reduce the
number of migration.
T1 =
T2 =
Four steps are involved in the VM migration.
i. Calculate load on the PM and VM.
T3 =
ii. Calculate the upper and lower threshold to find the
overloaded and undreloaded condition.
temp ← (T1 + T2 + T3 )/3
iii. Select the best VM for the migration
Tupper = 1- x*temp
iv. Select the best host to place the selected VM
3
Tupper = 1-5*temp/100
Load Calculation for the Physical and
Virtual Machine
We consider three parameter i.e. CPU, memory and
bandwidth for the load calculation. So each VM have it
own CPU, memory and bandwidth. Load on the VM can
be calculated as
VMcpu =
Tlower =0.03
Where n is the number of VM in the host and x is the
percentages of the temp. Based on the experiment x=.05 is the
suitable value, which maintain the tradeoff between the number
of migration and resources wastages. Threshold for the next
interval is calculated based on the previous history i.e.
threshold for the t2 interval depends on the host utilization in
the t1 interval
VMbw=
3.2 Virtual Machine Selection
VMram=
Load on the VM is depends on the CPU utilization. So load of
the VM is directly proportional to CPU utilization and define
as a
VMload = =
Total load on the host is the total load of the VM running into
that host. If there are n VM on pth host then average load on the
pth host can be calculated by the given equation
PMload =
3.1 Lower and Upper Threshold Calculation
Two thresholds i.e. lower and upper are used to define the
overloaded and underloaded host. These thresholds can be
Static and dynamic. In the static threshold lower and upper
thresholds are fixed and they not changed with time, while in
the dynamic threshold, lower and upper thresholds are changed
with time.
Dynamic threshold is more suitable for the cloud, where
resources required by the VM are changed dynamically. It is
analyze that VM migration possibility increase with the
threshold. That means as the upper threshold increase it will
also increase the possibly of the migration. Most of the work
done only considered the CPU for calculating the load, but
RAM is the most critical element in the system as compare to
the CPU. So for calculating the upper threshold we consider
CPU, RAM and bandwidth with equal weight.
Each host can have number of VM. So which VM is selected
for the migration affect the total migration time and down time?
Down time is the time for which VM not available to the user
and total migration time is time required to transfer the whole
machine. If we select the large VM than it will increase the
total migration time and down time and if we select the small
VM then number of VM have to be migrated. So in our
approach we select the VM which size is greater than or equal
to the difference between the upper threshold and the host
utilization. Sudo code for the VM selection given below
1. Input: hostList, vmList Output: migrationList
2. Arrange each host into decreasing order of their
utilization
3. for each h in hostList do
4. hostUtil ←host.util()
5. bestVmUtill ← Utilization of first VM
6. while hostUtil > host.upThresh do
7. for each vm in vmList do
8. diff ← hostUtil ─ host.upThresh
9. if vm.util() > diff then
10. temp ← vm.util()
11. if temp < bestVmUtill then
12. bestVmUtill ← temp
13. bestVm ←vm
14. else
15. if bestVmUtill = First VM then
16. bestVm ← vm
17. break
18. hostUtil ← hostUtil ─ bestVm.util()
19. migrationList.add(bestVm)
20. vmList.remove(vm)
Algo for the consolidation
1. if hotUtil < lowThresh() then
2. Migrate all VM from the host
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3.3 Select the Host to Place the Selected VM
Target PM Selection is the most critical step in the VM
migration, because it affects the overall performance of the
system. Wrong selection of the PM may increase the number of
VM migration as well as resource wastage. In our approach we
select the VM which is power efficient.
1.
2.
3.
4.
5.
6.
7.
8.
4.
Input: hostList, vmList Output: allocation of VMs
Sort all PM according their utilization
foreach vm in vmList do
foreach host in hostList do
if Host_Load<=H_UTD && Host_Load>=H_LTD
Assign VM to the host where less increment in the power
else
Activate new host and assign VM to that host
Experiment Result
To implement our approach we are using CloudSim simulator
[14]. This simulator is based on the Java and contained the
classes, for all the function which is required to implement the
cloud based approaches. To check the efficiency of our
approach we compare our algorithm with the exiting energy
aware resource allocation approach [13]. We have simulated a
data center 10 PM. Each PM is modeled to have one CPU core
with the performance equivalent to 1000, 8 GB of RAM and 1
TB of storage. Maximum power consume ed by the host is 250
W. So according to the power model [13], a host consumes 175
W with 0% CPU utilization, up to 250 W with 100% CPU
utilization. Each VM requires one CPU core with 250 MIPS,
128 MB of RAM and 1 GB of storage. Each VM runs some
application with 150,000 MI, which required 10 min on the 250
MIPS with 100% utilization. To generate the variable load a
uniform random function is used, which generate the random
value between 1-250. Since requested MIPS by the VM
changed every time, so for each execution it gives the different
number of migration for the same number of physical and
virtual machine. Therefore each experiment has been run 10
times.
Initially we take 10 hosts and 20 VM. To calculate the upper
threshold we execute our approach 20 times for the different
value of X, where is the percentage (X=.03 to .08) and
calculate the average number of migration. We found that
x=.05 is the best value.
Figure-3 For calculating different value of X
To compare the efficiency of our approach, both approach are
executed 7 times and compare the number of migration.
Figure-4 Comparison of number of migration
Total energy consume by the data center is depends on the
number of migration. So we plot the graph between the energy
consumption and number of migration.
Conclusion
Load balancing is a very important task in every system,
because the system performance is totally depends on the load
management. But load balancing in cloud is very challenging
task, due to the resource required by the VM is changed
dynamically. Furthermore resources in the cloud are distributed
dynamically. In this paper we proposed a double threshold
based load balancing approach. In this approach VM migration
approach is used to balance the system. Lower threshold are
use to implement the concept of server consolidation and upper
threshold are use for the load balancing. Experiment result
show that our approach reduced the number of migration as
well as energy consumption.
5. References
[1] M. Armbrust et al., “Above the Clouds: A Berkeley View
of Cloud Computing”. In: EECS Department, University of
California, Berkeley (2009)
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[2] RK Gupta et al., “A Complete Theoretical Review on
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[9] Paul, M. et al. “Task-scheduling in cloud computing using
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[11] Mayank Mishra et al., “On Theory of VM Placement:
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[12] T. Wood et al., “Black-Box and Gray-Box strategies for
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[13] A. Beloglazov et al. “ Energy-aware resource allocation
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