20120140504025

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 230-238 © IAEME
230
LOAD BALANCING IN CLOUD USING PROCESS MIGRATION
Dr. Narayan A. Joshi
Associate Professor, CSE Department
NIRMA University
Ahmedabad, Gujarat, India.
ABSTRACT
Advancements in processing, storage and networking technologies have stimulated
enhancements and acceptance of virtualization and cloud computing technologies. Due to rapid
innovations and availability of the state of the art computing technology and growing market
competition, it has become essential for service providers to serve the cloud based services in
proficient manner. The day by day growing demands for cloud-based resources have made the
resource allocation task very challenging for the cloud service providers to keep up with the SLAs.
To earn customer satisfaction and more revenue, the service providers are forced to efficiently
organize and manage the cloud infrastructural resources. The concept of load sharing can be a
solution for optimized resource allocation in such scenarios. The paper describes a load balancing
algorithm by means of the process migration mechanism. The behavior of suggested implementation
is shown in the paper.
Keywords: Cloud Computing, Virtualization, Load Balancing, Load Sharing, Process Migration.
1. INTRODUCTION
The developments in microelectronic semiconductor technology have given rise to the
availability of high-speed inexpensive storage and processing units. In addition to such evolutions,
the improvements in internetworking technology have become one of the major driving forces in the
innovation of not only loosely-coupled distributed systems but also the virtualization and cloud
computing technologies [9]. Moreover, it is seen that the loosely-coupled distributed systems and
cloud computing technologies have been widely accepted by the IT solution providers for
implementation of modern computing solutions. Several key features including tolerance, higher
scalability and better reliability, better resource utilization and sharing, flexibility, on-demand
INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING
AND TECHNOLOGY (IJARET)
ISSN 0976 - 6480 (Print)
ISSN 0976 - 6499 (Online)
Volume 5, Issue 4, April (2014), pp. 230-238
© IAEME: www.iaeme.com/ijaret.asp
Journal Impact Factor (2014): 7.8273 (Calculated by GISI)
www.jifactor.com
IJARET
© I A E M E

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availability are the motivating factors behind acceptance of the distributed and cloud computing
environments.
In general, the cumulative computational capability of the interconnected workstations and
the virtual machines across the distributed and the cloud computing environment, theoretically
appears much larger than the capability of an individual virtual machine or an isolated computer
system. However, it is common that in a distributed and cloud computing environments with a
number of networked virtual machines and workstations, some of them may be overloaded while
other of them may be left almost idle which resulting into wastage of the computing power that goes
unused. This means, there are chances that some of the nodes are heavily loaded, while others are left
under loaded; which in effect causes utilization of only a fraction of overall theoretical computing
power. However, performance gains and the optimum resource utilization may be achieved by
transferring processes from the heavily loaded virtual machines to the lightly loaded or idle virtual
machines known as load balancing [10]
Section 2 of the paper describes related work and existing approaches towards load balancing
in cloud and distributed systems. Section 3 focuses on the process migration mechanism. The
suggested load balancing mechanism is described in section 4. Section 5 focuses on the outcomes of
the algorithm suggested in the section 4. Some of the implementation issues and assumptions are
mentioned in section 6. Finally, the concluding remarks and further scope of research are
summarized in section 7.
2. RELATED WORK
The cloud computing environment comprises of the building blocks such as – data centers,
distributed servers and users. Well-designed implementation and usage of cloud computing
environments may help in solving several having different operational and behavioral demands such
as- on-demand availability, flexibility, fault tolerance, scalability, reduced cost of ownership and
effective utilization and sharing of resources [4][13]. However, sometimes the implementation of
cloud computing environments using virtualization technology may result into performance reduction
for the processor centric and memory crunching computational jobs [15]. The resource management
mechanisms sometimes also associate with the load balancing mechanisms not only to improve
utilization and response time but also to avoid situations where some of the virtual machines are
overloaded while others are lightly loaded or sitting idle.
Accordingly, the load balancing mechanism sees that all the networked virtual machines
perform nearly the equal amount of jobs [6], [14]. Difficulties faced in joining different cloud
computing environments are discussed in [2]. The load balancing work suggested in [3], is based on
the number of connections. It determines the nodes having least number of connections first and then
sends requests to such nodes. Reference [1] has proposed an improved implementation of [3]; he has
focused on session switching at the application layer. Additionally, it calculates the connection time
between the client and the node. Availability of unlimited virtual resources is an important
characteristic of cloud-based infrastructures. However, it may always not be feasible for providers to
guarantee the allocation of all the demanded resources [8]. They have suggested significant work
showing reduction in SLA violation in federated clouds environment. Reference [17] suggests a load
balancing technique which collects the workload information of nodes using the ants behavior and
accordingly assigns the job-request to appropriate node. An effort has been suggested in [7] has to
solve the synchronization issue of [17]. The suggested work is based on committing suicide by an ant
however. The work suggested in [13], aims to provide dynamic load balancing by reducing
duplication of data by suggesting the Index Name Server algorithm. The work presented in [14]
suggests load balancing efforts by allocating the job requests to the node which is having the least
number of connections. Moreover, [11] and [10] present work related to centralized and distributed

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load balancing mechanisms for homogeneous networked systems. A resource aware load balancing
work which provides better results using exponential smoothing forecast is presented in [16].
However, the discussion made so far shows that there is a scope for the betterment in the existing
work. Here we focus on efficient resource allocation using load balancing by means of the process
migration mechanism. Though there are huge scopes in adoption of cloud based computing services,
there is a lack of standardization in the development and application of process migration mechanism
and open source cloud computing services.
3. PROCESS MIGRATION
The issue of efficient resource utilization in cloud computing environments can be addressed
by distributing workload of a virtual machine among other virtual machines in the cloud using
process migration. The implementation of process migration technique may result into efficient
overall system utilization by making use of the idle virtual machines [12]. The technique involves
transfer of a process from one virtual machine to another virtual machine in the cloud. The dynamic
process migration technique involves critical operations such as collecting the current state
information of the desired process running on the source virtual machine, move the collected
information to the desired destination virtual machine, restore the process state on the destination
virtual machine and resume the restored process for execution [5].
4. MECHANISM
The architecture of suggested load balancing algorithm and comprising modules are
described here and depicted in figure1.
The cloud provider maintains a dedicated virtual machine or a physical machineand
designates it as a ServerVM; for simplicity and cost-cutting reasons, the cloud controller itself may
be made to act as a ServerVM. The virtual machines in the cloud other than the ServerVM which
have signed the contract for availing load balancing facility provided by the cloud service provider,
we name them as member virtual machines or memberVMs. The ServerVM is assigned a role to
handle and retain the workload measurements of the member virtual machines. Additionally, the
cloud service provider reserves one more virtual machine or physical machine, say BalancerVM,
which is dedicated to the job of balancing the workload of member virtual machines. In order to
carry out the assigned role, the BalancerVM determines the overloaded member virtual machines,
the idle member virtual machines (or the under loaded member virtual machines) and with the help
of the VMProcessMigrator module, it performs load balancing between the overloadedVM and the
underloadedVM.
Moreover, the suggested algorithm facilitates the overloaded member virtual machine to take
load balancing decisions by itself instead of relying upon the load balancing decisions given by the
BalancerVM; the overloaded memberVM may make load balancing requests to other under loaded
memberVMs in the cloud. However, the algorithm prevents the underloadedVM from receiving
consecutive load balancing requests, thereby preventing such memberVMs from spontaneous
overstretching. Sometimes, it may inadvertently happen that the BalancerVM consecutively picks the
same underloadedVM as a destination for redirecting the surplus workload from multiple overloaded
memberVMs. Suppose, at time T1 the BalancerVM determines the same virtual machine VMi as
underloadedVM and therefore it may select the VMi as an endpoint for load balancing. Because of
(i) the latency in process migration and (ii) the process scheduling policy on VMi, the migrated
process may resume on VMi after some time say T2. Here, T2 includes the waiting time in ready
queue say Tr and the time taken for actual migration say Tm i.e. T2 = Tr + Tm. As a result, during
the transitional time (i.e. T2 – T1), the VMi will appear as an underloadedVM to not only the

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BalancerVM but also the other memberVMs; which in turn may encourage them to pick the VMi as
an endpoint for their further load balancing decisions; thereby causing overburdening of VMi which
was initially considered as an underloadedVM. The algorithm therefore assigns every memberVM
one of the three state flag values ACTIVE, PASSIVE and INACTIVE [11].
Figure 1: Architecture of Load Balancing in Cloud using Process Migration
The algorithm suggested here extends the work suggested in [10] & [11] in the context of
virtualization and cloud computing, and comprises of the following foremost steps:
1. Implement a module VMPortal which resides and continuously executes on participating
memberVM, periodically determines the workload information of the virtual machine, and
periodically sends the capacity vector and the workload details to the ServerVM.
2. The ServerVM module continuously executes and receives the capacity vectors and workload
information which are periodically sent by the VMPortal residing on all memberVMs in the
cloud. The module periodically updates the availability status of the memberVMs in its
repository to ACTIVE, PASSIVE or INACTIVE. One of its thread periodically (time interval
Tp) toggles the current status into ACTIVE state from PASSIVE state (i.e. PASSIVE-

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>ACTIVE) of the VM which was earlier (i.e. before time interval Tp) picked up as an endpoint
for load balancing by the BalancerVM module.
3. The BalancerVM module runs in collaboration with the ServerVM module: it determines the
ACTIVE overloaded and ACTIVE under loaded virtual machines on the basis of the workload
information and the capacity vectors available, instructs the overloadedVM VMo to transfer
some of its workload to the underloadedVM VMu. Following the dispatch of migration
instruction, it immediately updates the status of the underloadedVM VMu to PASSIVE state.
4. The VMRequestDispatcher module runs on the memberVMs and periodically determines
whether the virtual machine has become overloaded or not. If the virtual machine is determined
as overloaded, then depending upon the SLA signed with the cloud service provider, it
dispatches load balancing request to either the BalancerVM or the peer memberVMs in the
cloud. In case of the request pertaining to the BalancerVM, the BalancerVM may indicate to it
about the underloadedVM as an endpoint to which it may transfer the workload. Otherwise, it
waits for the reply from the peer memberVMs. In case of an affirmative reply, it performs load
balancing by making process migration with the help of VMProcessMigrator module;
otherwise, it keeps on doing round robin policy for in turn memberVMs in the cloud.
5. The VMRequestListener module also runs on the peer memberVMs; it listens for incoming
requests for load balancing sent by the VMRequestDispatcher module. After receiving the
request, the module decides whether to accept it or not and replies the same to the source
VMRequestDispatcher module. If it decides to accept the load balancing request, then it toggles
its status as PASSIVE and informs the updated status to the ServerVM also.
6. The workload shifting workis carried out in the form of process migration from the
overloadedVM to the underloadedVM,via the send_migartion_order() function which is
performed by the VMProcessMigrator module [10], the module resides on all participating
member VMs in the cloud.
The outline of the BalancerVM module includeing some of the foremost data structures and
functionsare described here.
Module BalancerVM
{
struct VMCapacityVector {
int nCPU,
float ghz,
long tot_ram, free_ram,
long free_disk_space;
int bandwidth;
};
enum vm_stat {INACTIVE=0, ACTIVE=1, PASSIVE=2};
struct VMLoad {
char ip_address[16]; //IP address associated with the VM
double load; //VM’s current load history
int current_status; // VM is ACTIVE or PASSIVE or INACTIVE
unsigned long time_of_passive_set; //time when current status is set to PASSIVE
…

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};
void BalancerVM()
{
struct VMLoad* p_overloadedVM; //represents overloaded VM
struct VMLoad* p_underloadedVM; //represents under loaded VM
int seep_t=10; //indicates sleeping time interval for this function
while(1) //run continuously
{
sleep(sleep_t);
//find out the underloadedVM in the cloud
p_underloadedVM = find_underloaded_VM();
if(!p_underloadedVM) //if no underloadedVM is available
continue;
//find out the overloadedVM in the cloud
p_overloadedVM = find_overloaded_VM();
if(!p_overloadedVM)
continue;
//verify the capacities of the source and destination
if(capacity_vector of underloadedVM >= capacity_vector of overloadedVM)
{
//begin transaction by setting semaphore
//toggle status of underloadedVM
p_underloadedVM->status = PASSIVE;
/* also askthe selected underloadedVM to update its status */
//process migration from overloadedVM to underloadedVM
send_migration_order(p_overloadedVM, p_underloadedVM);
//end transaction by resetting semaphore
}
}
…
}//end of funciton
…
} //end of module
5. OUTCOMES
The behavior of the suggested load balancing algorithm is discussed here:
The figure 2 depicts the snapshot representing the overloaded virtual machine having
workload 1.240 at IP 169.254.254.49.

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Figure 2: Before load balancing
The figure 3 depicts the snapshot representing reduction in the workload of the virtual
machine 169.254.254.49 after application of the suggested load balancing algorithm which migrates
its workload to the underloadedVM 169.254.254.50.
An important advantage of the suggestedload balancing technique for cloud environment is
that the proposed algorithm performs the task of load sharing by shifting the desired process instead
of the whole VM among virtual machines by means of the process migration mechanism. Thereby
the technique avoids the need of migration of an entire virtual machine among the physical nodes in
the cloud. Moreover, if desired, the suggested load balancing mechanism may also act across
multiple physical nodes in the cloud environment, thereby allowing process migration from one
physical node to another physical node.
Figure 3: After load balancing using process migration

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6. IMPLEMENTATION ISSUES
The suggested algorithm takes the service of process migration mechanism to carry out the
transfer of workload between the member virtual machines. A coupleof issues need to be addressed
on the aspect of implementation of process migration mechanism in the cloud computing
environment.
One of the implementation issue is fault tolerance. The algorithm assumes the availability of
the dedicated controlling systems including the ServerVM and the BalancerVM. However, the
suggested steps have taken care about the issue of fault tolerant load balancing – unavailability of the
ServerVM and the BalancerVM will not at all bring down the load balancing mechanism, instead the
peer member virtual machines will keep on doing load shifting themselves without requiring
consultation from the ServerVM and the BalancerVM.
Another implementation issue is security in the process migration mechanism and the cloud
computing environment. The source and destination virtual machines need to have very strong
confidence on each other. Here we assume that the source virtual machine trusts that the destination
virtual machine will not disturb the execution of migrated process. Vice versa, the destination virtual
machine also assumes that the migrated process sent by the source virtual machine is trustable and
not harmful for the destination virtual machine environment. However, the suggested mechanism
focuses on the accomplishment of the load balancing functionality by means of the process migration
mechanism, and leaves various security issues including the one discussed above, on the service
level agreements and the practice of signing up for the membership for availing the load balancing
among virtual machines in the cloud computing environment.
7. CONCLUDING REMARKS
A novel idea representing the task of load balancing in cloud computing environment by
means of the process migration mechanism is suggested in this paper. Our future aim is to minimize
the migration latency. Moreover, as justified in the implementation issues section above, future work
may be carried out for redesigning the policy documents which are required for the service level
agreements due to the presence of load balancing facility. Furthermore, we intend to carry out further
work to frame and suggest a policy document which will be required for signing up an additional
agreement for [becoming a member for]availing the load balancing service by means of process
migration from the cloud service provider.
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