WJCAT2-13707877

World Journal of Computer Application and Technology 4(3): 31-37, 2016 http://www.hrpub.org
DOI: 10.13189/wjcat.2016.040302
A Study of a New Dynamic Load Balancing
Approach in Cloud Environment
Sanjay Chakraborty*
, Nilotpal Choudhury
Department of Computer Science & Engineering, Institute of Engineering & Management, Kolkata, India
Copyright©2016 by authors, all rights reserved. Authors agree that this article remains permanently open access under the
terms of the Creative Commons Attribution License 4.0 International License
Abstract Distributing workloads across multiple
computing resources are one of the major challenges in a
cloud computing environment. This paper is being discussed
over the basic obstacles of load balancing in cloud
environment. The paper looks beyond the problems faced by
the cloud system to overcome those through probable
improvised techniques. This is a paper over solving the
problems exist in the present days by logically analyzing and
presenting in an algorithmic format. This approach is mainly
focused on an effective job queue making strategy which is
suitably allocated the various jobs to CPUs based on their
priority or without priority. It also deals with some of the
major problems of load balancing in cloud environment like,
timeout. Finally, it shows how this approach is fitted in
famous AWS and GAE cloud architecture partially. This
article will provide the readership an overview of various
load balancing problems in cloud environment while also
simulating further interest to pursue more advanced research
in it.
Keywords Cloud, Load Balancing, Migration, Job
Sequence, Timeout, Super Node
1. Introduction
Cloud is a large distributed computing system which
shares resources, software and information on-demand, like
public utility to a large number of users. Cloud computing is
an evolution of evolution of Virtualization, Utility
computing, Software-as-a-Service (SaaS),
Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service
(PaaS). Load balancing is the process of reassigning the total
loads to the individual nodes of the collective system to make
the best response time and also good utilization of the
resources. Cloud system is the collection of many
heterogeneous systems [5,6]. The systems are actually
consists of servers and clients. Clients’ request for the
resources must be provided as soon as possible. The CPU of
the server must process the client’s request without too much
delay. CPU located in different places have different task
loads. Again every CPU has different speed. So the task
completion rate of every CPU changes. To make the system
efficient, the system must have prepare a process where the
CPUs work in such way that the collective speed of the
system is increased. So, one of the important issues in cloud
computing is to balance these loads. And it is too much
difficult to manage cloud computing without balancing loads.
There are several other resources which will be load
balanced, like
 Network edges and facilities such as DNS,FTP, and
HTTP
 Making Connections through intelligent switches
 Processing through computer system task
 Storage resources right of entry to application
instances.
The rest of the paper is organized as follows: Section 1.1
discusses about an overall architectural components and
various architectures of cloud system. In the 1.2 and 2
sections, various difficulties of cloud systems including load
balancing are discussed and a solution based approach is
provided in section 3.Section 4 discusses about the QTS
service in cloud. Finally, section 5 highlights some research
issues and section 6 gives the conclusion of this paper.
1.1. Cloud Architecture
Cloud service models are commonly divided into SaaS,
PaaS, IaaS and Data as a Service (DaaS) that exhibited by a
given cloud infrastructure [16, 17, 18].
A. Software as a Service (SaaS)
Cloud consumers release their applications in a hosting
environment, which can be accessed through networks from
various clients (e.g. Web browser, PDA, etc.) by application
users. Cloud consumers do not have control over the cloud
infrastructure that often employs multi-tenancy system
architecture, namely, different cloud consumers' applications
are organized in a single logical environment in the SaaS
cloud to achieve economies of scale and optimization in

32 A Study of a New Dynamic Load Balancing Approach in Cloud Environment
terms of speed, security, availability, disaster recovery and
maintenance. Examples of SaaS include SalesForce.com,
Google Mail, Google Docs, and so forth.
B. Platform as a Service (PaaS)
PaaS is a development platform supporting the full
“Software Lifecycle” which allows cloud consumers to
develop cloud services and applications (e.g. SaaS) directly
on the PaaS cloud. Hence, the difference between SaaS and
PaaS is that SaaS only hosts completed cloud applications
whereas PaaS offers a development platform that hosts both
completed and in-progress cloud applications. This requires
PaaS, in addition to supporting application hosting
environment, to possess development infrastructure
including programming environment, tools, configuration
management, and so forth. An example of PaaS is Google
AppEngine.
C. Infrastructure as a Service (IaaS)
Cloud consumers directly use IT infrastructures
(processing, storage, networks and other fundamental
computing resources) provided in the IaaS cloud.
Virtualization is extensively used in IaaS cloud in order to
integrate/decompose physical resources in an ad-hoc manner
to meet growing or shrinking resource demand from cloud
consumers. The basic strategy of virtualization is to set up
independent virtual machines (VM) that are isolated from
both the underlying hardware and other VMs. Notice that
this strategy is different from the multi-tenancy model,
which aims to transform the application software
architecture so that multiple instances (from multiple cloud
consumers) can run on a single application (i.e. the same
logic machine). An example of IaaS is Amazon's EC2.
D. Data as a Service (DaaS)
The delivery of virtualized storage on demand becomes a
separate Cloud service - data storage service. Notice that
DaaS could be seen as a special type IaaS. The motivation is
that on-premise enterprise database systems are often tied in
a prohibitive upfront cost in dedicated server, software
license, post-delivery services and in-house IT maintenance.
DaaS allows consumers to pay for what they are actually
using rather than the site license for the entire database. In
addition to traditional storage interfaces such as RDBMS and
file systems, some DaaS offerings provide table-style
abstractions that are designed to scale out to store and
retrieve a huge amount of data within a very compressed
timeframe, often too large, too expensive or too slow for
most commercial RDBMS to cope with. Examples of this
kind of DaaS include Amazon S3, Google BigTable, and
Apache HBase, etc.
1.2. Difficulties of Cloud Systems
Cloud computing can provide infinite computing
resources on demand due to its high scalability in nature,
which fulfills the needs of the large number of customers.
There are several difficulties associated with the services of
cloud computing. They are listed below,
I. Security & Privacy Issues
There are several common security threats in cloud
computing paradigm. This category includes
organizational and technical issues related to keeping
cloud services at an acceptable level of information
security and data privacy. This includes ensuring
security and privacy of sensitive data held by banks,
medical and research facilities [17].
II. Infrastructure
This deals with the issues relating with hardware layer of
cloud services along with the software used to operate this
hardware. Our proposed issue belongs to this category.
III. Data Handling
This category deals with the data storage type of problems
like, data segmentation and recovery, data resiliency, data
fragmentation and duplication, data retrieval, data
provenance, data anonymization and placements etc.
In this paper, we mainly focus on the symmetric
distribution of workloads among processors in cloud system.
It is commonly found that the CPUs are not working with
proper distribution of workloads. Sometimes a set of CPUs
stay idle or less loaded while others are too much loaded.
Though the system has the high functioning CPUs, but this
type of behavior of the system makes the performance too
much degraded from the actual or theoretical value. So
several cloud systems face the challenge of being
maintaining its speed by sharing loads or tasks to the idler or
less busy CPUs.
Figure 1. Basic Cloud Architecture

World Journal of Computer Application and Technology 4(3): 31-37, 2016 33
2. Problems of Load Balancing
For a cloud system the tasks are distributed over the
system and every system has two parts (a) Task Reception
and (b) Task Migration. These two are also known as ‘Node
States Information’ [1]. Here node is actually the system
CPU or server. For a general task given to the node the
expected waiting time is (at node i)
Wi (t) = qi (t).ts (1)
Where, Wi(t) is the waiting time for task, qi is the queue
length of the task and ts is average time or completion of a
task.
Now the load balancing concept says that there are
communicating nodes in between two local domains to
communicate the data with the global domain. These nodes
are actually the nodes which are overlapped in more than one
domains. In this type of load sharing N: Number of nodes,
V= {1, 2… N} a set of nodes in a system.
Here we calculate a maximum load to be executed in the
node and then proceed to share the load to the other nodes.
For the algorithm the node’s speed and task completion
rate are also calculated. But in this instance some possible
drawbacks are not calculated.
Here those drawbacks are,
1) If the communicating node fails then the whole load
balancing will be stopped or harmed partially.
2) For two domains there is a possibility of
communication delay.
3) Communication overhead problem.
This paper focuses on this particular problem of waiting
time of the tasks or jobs. The given approach is to solve
waiting problem as well as simplistic approach to the
communication delay [8, 9, 10, 15].
3. Proposed Approach
The proposed approach for effective load balancing is
divided into three sub-sections, they are as follows,
3.1. Queue Making and Job Making Processes
For every CPU there is a speed limitation. This speed is
function depending on variable like instruction set, cache,
clock speed, bandwidth, generated heat and heat dissipation.
Depending on all these variables the clock speed is
calculated and then the average task completion time is also
calculated.
According to the task completion average time there is a
queue for every CPU. This queue is actually a list of tasks
which also contains a number called “threshold”. This
threshold is a general indicator which tells the task that
beyond this point the tasks have to wait longer than the
expectation. Thus the task is ready to be migrated to the node
which is idler. Using these three processes will increase the
speed of the system. It is also converted the system into a
backup planned system which is adaptive to the speed of the
nodes and also to the queue and server status.
If we try to think that there are n numbers of CPUs and k
number of tasks with their own different characteristics. So
we can get a working algorithm for queue making in the
following way:
a) CPU rearrangement:
Arranging all the available CPUs from high to low in
according to their functional speed.
b) Rearrange Job requests:
Priority basis
i. If there is any priority assigned to the jobs then
the jobs should be rearranged according to the
priority. In this rearranged list the highest priority
job will be in the first position and the least one
at the last position.
ii. If any job is not assigned with priority then the
job is set to execute with the lowest priority and
the priority index is set thus.
Without priority basis:
When there is no priority is assigned for the listed jobs
then the jobs are arranged by their estimated completion time.
The estimated time depends on code of lines, loops, inner
loops and resources required. Jobs are rearranged from
highest to lowest order.
c) CPU allocation:
The rearranged CPU list gets the rearranged job list and
then the CPUs are given the jobs as like first CPU in the list
gets the first job and the last CPU gets the last job until all the
CPUs are occupied then the list of jobs start to allocate
themselves from the start of the CPU list.
d) Completion time estimation:
The speed of the CPU and the required CPU cycle of the
job give the general completion time of the job.
e) Runtime new request allocation:
When a new job is arrived then the job is included to the
initial list of jobs that are not being executed by the CPUs.
Then the list goes through the step (ii) to (iv). This way a new
job is inserted in the existing list of jobs and allocated to the
available CPUs.
The above job making and queue making processes are
represented in the figure2 and figure3 respectively.
3.2. Timeout Chart
For a particular task queue, one task is migrated to a node
but then task is delayed because of longer waiting time.
Again this may happen that searching or migration takes so
long time that it is actually exceeding the first node
completion time. The third scenario may be such that one
task is migrated and processed very quickly but the transfer

time for the task is too high to wait or the task. To overcome
all these problems there must a timeout chart available which
will work as a look ahead table and also to take a decision
whether the task should be migrated or not. Now if it is seen
that the total system is not working and it is becoming too
slow then also this timeout chart work as a very good
reference to eliminate or terminate jobs. That is if the job
completion is not executing properly then the chart provides
us the time of termination and then we can decide which jobs
are to be completed within this time and which are to be
terminated. This process also checks the priority and job
types. As if there is no priority set initially then we can have
many different jobs. Among those jobs some are reversible
or have roll back option, others may not have that option. In
that case if a job without rollback option is being executed
then that job will not be terminated before completion.
3.3. Super Node as Proxy Node
All the communicating nodes of the global domain of
cloud are connected to one super node with data base of
required characteristic of these nodes. In such a case, if a
failure happens in one of these nodes then the super node will
provide all the information to make the system work
properly. Here this super node actually a proxy node which
keeps all the copy of the information and updates it in time to
time. But it is not searched in every case as it is part of the
large database. But it is updated without delaying the system
that the system may have a backup plan at the time of failure.
The entire process is shown in figure 4.
4. QTS in Cloud Computing
The term QTS is actually a combination of those three
processes which have been discussed previously. In cloud
environment two mostly available and used cloud
architectures are Amazon Web Services (AWS) [11] and
Google App Engine (GAE) [12]. In the proposed model the
QTS comes in between web server and VaR Analytic Server
(GAE) and EC2 Instance VaR Server (AWS). This is the
proposed approach of the paper to improvise the migration
process so that the performance gets better or the system.
Figure 2. Job making Process

Figure 3. Queue Making Process
Figure 4. Functionality of Super node
Figure 5. Working process of AWS/GAE without QTS

Figure 6. QTS applied in GAE
Figure 7. QTS applied in AWS
5. Future Research Issues
This paper also tries to highlight the changes happening in
the field of load balancing algorithms across the world.
• Maximum resource utilization is one of the main
objectives in any distributed load balancing system.
In future, this load balancing algorithm can be
associated with some optimization techniques (PSO,
ACO, GA etc.) to give more flexibility to
distributing the loads and improving the
performance of the system.
• Besides load balancing, fault tolerant in cloud
system needs to be incorporated in order to remove
security and usage issues and to design the
approach more cost effective and successful.
• The recent cloud load balancing solutions are
focusing on Green cloud topic in load balancing
mechanisms by considering the challenges like:
Reducing the energy and power consumption,
Reducing carbon emission and also reducing cost
customers as a result of energy efficiency topic.
• There must be devised some dynamic load
balancing algorithm which can set the load degree
high and low dynamically. In future research issues,
some sort of fuzzy logic may be associated with
load balancing techniques to make it more flexible.
6. Conclusions
This paper presents the basics of cloud computing along
with research challenges in load balancing. It also focuses on
merits and demerits of the cloud computing. There is the
process of dynamic approach to allocate the jobs to suitable
CPUs. Other features are to check the delay and accordingly
terminating a process as well as migrating them to other
CPUs so that the server can be utilized more efficiently. This
procedure is a theoretical and mathematically evaluated
approach. The paper includes an algorithm to support the
approach. There is also a manually job migration and
allocation through various table and possible dynamic
scenario. Although cloud computing is a new and well
attractive feature in the computer science area it has some of
its own problems. This paper does not try to change any
cloud architecture, it is only an add-on to the existing process
to improvise its filtration and make it more efficient.
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