Cost Effective Selection of Data Center in Cloud Environment
Manoranjan Dash1, Amitav Mahapatra2 & Narayan Ranjan Chakraborty3
1
Institute of Business & Computer Studies, Siksha O Anusandhan University, Bhubaneswar, Odisha
2
College of Engineering and Technology, Bhubaneswar, Odisha
3
Department of CSE, Daffodil International University, Bangaldesh
E-mail: manoranjanibcs@gmail.com1
Abstract – Cloud computing today has now been rising as
new technologies and new business models. The increasing
cloud computing services offer great opportunities for clients
to find the maximum service and finest pricing, which
however raises new challenges on how to select the best
service out of the huge group. Cloud computing employs a
variety of computing resources to facilitate the execution of
large-scale tasks. Therefore, to select appropriate node for
executing a task is able to enhance the performance of largescale cloud computing environment. It is time-consuming for
consumers to collect the necessary information and analyze
all service providers to make the decision. This is also a
highly demanding task from a computational perspective,
because the same computations may be conducted repeatedly
by multiple consumers who have similar requirements. Load
balancing is the process of distributing the load among
various nodes of a distributed system to improve both
resource utilization and job response time .Load balancing
ensures that all the processor in the system. or every node in
the network does approximately an equal amount of work at
any instant of time. CloudAnalyst is a tool that helps
developers to simulate large-scale Cloud applications with
the purpose of understanding performance of such
applications under various deployment configurations. The
simulated results provided in this paper based on the
scheduling algorithm Throttled load balancing policy across
VM’s in a single data center and is being compared with
round robin scheduling algorithm to estimate response time ,
processing time .
cloud computing in determining whether to adopt it as
an IT strategy. The availability of advance processors
and communication technology has resulted the use of
interconnected, multiple hosts instead of single highspeed processor which incurs cloud computing. In case
of Cloud computing services can be used from diverse
and widespread resources, rather than remote servers or
local machines. There is no standard definition of Cloud
computing. Generally it consists of a bunch of
distributed servers known as masters, providing
demanded services and resources to different clients
known as clients in a network with scalability and
reliability of datacenter. Cloud computing is
revolutionizing the way IT resources are managed and
provisioned. Cloud computing is on demand service in
which shared resources, information, software and other
devices are provided according to the client requirement
at specific time. Cloud computing is an evolving
paradigm with changing definitions, it is defined as a
virtual infrastructure which provides shared information
and communication technology services, via an internet
i.e. cloud. Cloud computing provides a computer user
access to Information Technology (IT) services (i.e.,
applications, servers, data storage) without requiring an
understanding of the technology or even ownership of
the infrastructure. Cloud Computing is getting advanced
day by day. Cloud service providers are willing to
provide services using large scale cloud environment
with cost effectiveness. Also, there are some popular
large scaled applications like social-networking and
ecommerce. These applications can benefit to minimize
the costs using cloud computing. Cloud computing is
modeled to provide service rather than a product.
Services like computation, software, data access and
storage are provided to its user without its knowledge
about physical location and configuration of the server
which is providing the services. Cloud works on the
principle of virtualization of resources with on-demand
and pay-as–you go model policy. .
Keywords – Cloud computing, round robin and throttled
algorithms, load balancing.
I.
INTRODUCTION
Cloud computing is a distributed computing
paradigm that focuses on providing a wide range of
users with distributed access to scalable, virtualized
hardware and/or software infrastructure over the
internet. Despite this technical definition cloud
computing is in essence an economic model for a
different way to acquire and manage IT resources. An
organization needs to weigh cost, benefits and risks of
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Special Issue of International Journal on Advanced Computer Theory and Engineering (IJACTE)
memories for their resources to scale with increased
demands. This service is optional and depends on the
clients business needs. So load balancing serves two
important needs, primarily to promote availability of
Cloud resources and secondarily to promote
performance. In order to balance the requests of the
resources it is important to recognize a few major goals
of load balancing algorithms:
a)
Cost effectiveness: primary aim is to achieve an
overall improvement in system performance at a
reasonable cost.
Fig.1: View of the Cloud Computing Environment
b) Scalability and flexibility: the distributed system in
which the algorithm is implemented may change in
size or topology. So the algorithm must be scalable
and flexible enough to allow such changes to be
handled easily.
II.
c)
LOAD BALANCING IN CLOUD COMPUTING:
Load balancing in clouds is a mechanism that
distributes the excess dynamic local workload evenly
across all the nodes. It is used to achieve a high user
satisfaction and resource utilization ratio, making sure
that no single node is overwhelmed, hence improving
the overall performance of the system. Proper load
balancing can help in utilizing the available resources
optimally,
thereby
minimizing
the
resource
consumption. It also helps in implementing fail-over,
enabling scalability, avoiding bottlenecks and overprovisioning, reducing response time etc.
III.
Priority: prioritization of the resources or jobs need
to be done on before hand through the algorithm
itself for better service to the important or high
prioritized jobs in spite of equal service provision
for all the jobs regardless of their origin.
DISTRIBUTED LOAD BALANCING ALGORITHM FOR
CLOUD
A. Round Robin Algorithm
Round robin algorithm is random sampling based. It
means it selects the load randomly in case that some
server is heavily loaded or some are lightly loaded.
B. Throttled Load Balancing Algorithm
Throttled algorithm is completely based on virtual
machine. In this client first requesting the load balancer
to check the right virtual machine which access that load
easily and perform the operations which is give by the
client or user. In this algorithm the client first requests
the load balancer to find a suitable Virtual Machine to
perform the required operation
C. VectorDot
Singh et al. proposed a novel load balancing
algorithm called VectorDot. It handles the hierarchical
com-plexity of the data-center and multidimensionality
of resource loads across servers, network switches, and
storage in an agile data center that has integrated server
and storage virtualization technologies. VectorDot uses
dot product to distinguish nodes based on the item
requirements and helps in removing overloads on
servers, switches and storage nodes.
Fig. 2 : Load Balancing in Cloud Computing
Load balancing is the process of distributing the
load among various resources in any system. Thus load
need to be distributed over the resources in cloud-based
architecture, so that each resources does approximately
the equal amount of task at any point of time. Basic
need is to provide some techniques to balance requests
to provide the solution of the application faster. Cloud
vendors are based on automatic load balancing services,
which allow clients to increase the number of CPUs or
D. Compare and Balance
Y. Zhao et al. addressed the problem of intra-cloud
load balancing amongst physical hosts by adaptive live
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Special Issue of International Journal on Advanced Computer Theory and Engineering (IJACTE)
migration of virtual machines. A load balancing model
is designed and implemented to reduce virtual
machines’ migration time by shared storage, to balance
load amongst servers according to their processor or IO
usage, etc. and to keep virtual machines’ zero-downtime
in the process. A distributed load balancing algorithm
COMPARE AND BAL-ANCE is also proposed that is
based on sampling and reaches equilibrium very fast.
This algorithm assures that the migration of VMs is
always from high-cost physical hosts to low-cost host
but assumes that each physical host has enough memory
which is a weak assumption.
Performance is used to check the efficiency of the
system. This has to be improved at a reasonable cost,
e.g., reduce task response time while keeping acceptable
delays.
IV.
INTRODUCTION TO CLOUD ANALYST
GUI Package - It is responsible for the graphical user
interface, and acts as the front end controller for the
application, managing screen transitions and other UI
activities.
Simulation - This component is responsible for holding
the simulation parameters, creating and executing the
simulation.
E. Event-driven
V. Nae et al. presented an event-driven load
balancing algorithm for real-time Massively Multiplayer
Online Games (MMOG). This algorithm after receiving
capacity events as input, analyzes its components in
context of the resources and the global state of the game
session, thereby generating the game session load
balancing actions. It is capable of scaling up and down a
game session on multiple resources according to the
variable user load but has occasional QoS breaches.
UserBase - This component models a user base and
generates traffic representing the users.
DataCenterController - This component controls the
data center activities.
Internet - This component models the Internet and
implements the traffic routing behavior.
InternetCharacteristics - This component maintains the
characteristics of the Internet during the simulation,
including the latencies and available bandwidths
between regions, the current traffic levels, and current
performance level information for the data centers.
Metrics for Load Balancing In Clouds
Various metrics considered in existing load
balancing techniques in cloud computing are discussed
below
VmLoadBalancer - This component models the load
balance policy used by data centers when serving
allocation requests. Default load balancing policy uses a
round robin algorithm, which allocates all incoming
requests to the available virtual machines in round robin
fashion without considering the current load on each
virtual machine. Additionally, it is possible application
of a throttled load balancing policy that limits the
number of requests being processed in each virtual
machine to a throttling threshold. If requests are
received causing this threshold to be exceeded in all
available virtual machines, then the requests are queued
until a virtual machine becomes available.
CloudAppServiceBroker This component models the
service brokers that handle traffic routing between user
bases and data centers. The default traffic routing policy
is routing traffic to the closest data center in terms of
network latency from the source user base.
Throughput is used to calculate the no. of tasks whose
execution has been completed. It should be high to
improve the performance of the system.
Overhead Associated determines the amount of
overhead involved while implementing a load-balancing
algorithm. It is composed of overhead due to movement
of
tasks,
inter-processor
and
inter-process
communication. This should be minimized so that a load
balancing technique can work efficiently.
Fault Tolerance is the ability of an algorithm to perform
uniform load balancing in spite of arbitrary node or link
failure. The load balancing should be a good faulttolerant technique.
Migration time is the time to migrate the jobs or
resources from one node to other. It should be
minimized in order to enhance the performance of the
system.
V.
Response Time is the amount of time taken to respond
by a particular load balancing algorithm in a distributed
system. This parameter should be minimized.
PERFORMANCE ANALYSIS
We had used the cloud analyst tool to evaluate the
algorithms round robin, and throttled algorithm for the
case closest data center by using g user base (1-3) with
different regions & data centers (1-4) with different
virtual machine monitors.
Resource Utilization is used to check the utilization of
re-sources. It should be optimized for an efficient load
balancing.
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Special Issue of International Journal on Advanced Computer Theory and Engineering (IJACTE)
A. User Base
VI.
RESULT
The design model use the user base to represent the
single user but ideally a user base should be used to
represent a large numbers of users for efficiency of
simulation.
Fig. 3 : Simulation Configuration
B. Datacenter
Datacenter manages the data management activities
virtual machines creation and destruction and does the
routing of user requests received from user base via the
internet to virtual machines.
After performing the simulation the result computed
by cloud analyst is shown in following below figures.
We have used the above defined configuration for each
load balancing policy one by one and depending
(Response Time and Processing Cost Using Round
Robin)
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Special Issue of International Journal on Advanced Computer Theory and Engineering (IJACTE)
VIII.
(Response Time and Processing Cost Using Throttled )
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Fig. 4 : Comparison of Load Balancing Algorithm)
Comparing with the table and graph, overall
response time and data centre processing time is
improved. It is also seen that the virtual machine cost
and data transfer time in the round robin algorithm is
much better when compared to throttled algorithms.
VII.
CONCLUSIONS
The response time and data transfer cost is a
challenge of every cloud engineer to build up the
products that can increase the business performance in
the cloud industry adopted sector. Several strategies lack
efficient scheduling and load balancing resource
allocation techniques leading to increased operational
cost and give customer dissatisfaction. The paper tries to
give a bird’s eye view on enhanced strategies through
improved job and load balancing resource allocation
techniques.
REFERENCES
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