Hybrid Based Resource Provisioning in Cloud

International Journal of Computer Applications Technology and Research
Volume 3– Issue 12, 778 - 781, 2014, ISSN:- 2319–8656
www.ijcat.com 778
Hybrid Based Resource Provisioning in Cloud
N.Karthika
Vivekanandha College of
Engineering For Women
Tiruchengode, India
K.Prabhakar
Tiruchengode, India
R.Sangeetha
Tiruchengode, India
Abstract: The data centres and energy consumption characteristics of the various machines are often noted with different capacities.
The public cloud workloads of different priorities and performance requirements of various applications when analysed we had noted
some invariant reports about cloud. The Cloud data centres become capable of sensing an opportunity to present a different program.
In out proposed work, we are using a hybrid method for resource provisioning in data centres. This method is used to allocate the
resources at the working conditions and also for the energy stored in the power consumptions. Proposed method is used to allocate the
process behind the cloud storage.
Keywords: Cloud workload, Hybrid resource provisioning, Cloud storage and Invariant reports.
1. INTRODUCTION
Cloud Computing is the common buzzword in today’s
Information Technology. Cloud computing platforms are
rapidly emerging as the preferred option for hosting
applications in many business contexts [5]. An important
feature of the cloud that differentiates it from traditional
services is its apparently infinite amount of resource capacity
(e.g. CPU, storage, Network) offered at a competitive rate.
It eliminates the need for setting up infrastructure which takes
several months. Start-up Companies need not invest on the
infrastructure because the resources are available in the cloud
[6]. Cloud Computing enables users to acquire resources
dynamically and elastically.
A major challenge in resource provisioning technique is to
determine the right amount of resources required for the
execution of work in order to minimize the financial cost from
the perspective of users and to maximize the resource
utilization from the perspective of service providers [4]. So,
Cloud computing is one of the preferred options in today’s
enterprise. Resource provisioning means the selection,
deployment, and run-time management of software (e.g.,
database management servers, load balancers) and hardware
resources (e.g., CPU, storage, and network) for ensuring
guaranteed performance for applications. This resource
provisioning takes Service Level Agreement (SLA) into
consideration for providing service to the cloud users. This is
an initial agreement between the cloud users and cloud service
providers which ensures Quality of Service (QoS) parameters
like performance, availability, reliability, response time etc.
Based on the application needs Static Provisioning/Dynamic
Provisioning and Static/Dynamic Allocation of resources have
to be made in order to efficiently make use of the resources
without violating SLA and meeting these QoS parameters.
Over provisioning and under provisioning of resources must
be avoided. Another important constraint is power
consumption. Care should be taken to reduce power
consumption, power dissipation and also on VM placement.
There should be techniques to avoid excess power
consumption. So the ultimate goal of the cloud user is to
minimize cost by renting the resources and from the cloud
service provider’s perspective to maximize profit by
efficiently allocating the resources. In order to achieve the
goal the cloud user has to request cloud service provider to
make a provision for the resources either statically or
dynamically so that the cloud service provider will know how
many instances of the resources and what resources are
required for a particular application. By provisioning the
resources, the QoS parameters like availability, throughput,
security, response time, reliability, performance etc must be
achieved without violating SLA.
Platform as a Service is a way to rent hardware, operating
systems, storage and network capacity over the internet. It
delivers a computing platform or software stack as a service to
run applications. This can broadly be defined as application
development environment offered as a ‘service’ by the
vendors. The development community can use these platforms
to code their applications and then deploy the applications on
the infrastructure provided by the cloud vendor. Here again,
the responsibility of hosting and managing the required
infrastructure will be with the cloud vendor. AppEngine,
Bungee Connect, LongJump, Force.com, WaveMaker are all
instances of PaaS.
2. RELATED WORKS
From the last fewer, cloud computing has evolved as
delivering software and hardware services over the internet.
The extensive research is going on to extend the capabilities
of cloud computing. Given below present related work in the
area of cloud’s scalability and resource provisioning in cloud
computing.
In 2010 ChunyeGong, Jie Liu, Oiang Zhang, Haitao Chen and
Zhenghu has discussed Characteristics of Cloud Computing.
In this paper summarize the general characteristics of cloud
computing which will help the development and adoption of
this rapidly evolving technology. The key characteristics of
cloud computing
are low cost, high reliability, high scalability, security. To
make clear and essential of cloud computing, proposes the

Volume 3– Issue 12, 774 - 777, 2014
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characteristics of this area which make the cloud computing
being cloud computing and distinguish it from other research
area. The cloud computing has its own technical, economic,
user experience characteristics. The service oriented, loose
coupling, strong fault tolerant, business model and ease use
are main characteristics of cloud computing. Abstraction and
accessibility are two keys to achieve the service oriented
conception. In loose coupling cloud computing run in a client-
server model. The client or cloud users connect loosely with
server or cloud providers. Strong fault tolerant stand for main
technical characteristics. The ease use user experience
characteristic helps cloud computing being widely accepted
by non computer experts. These characteristics expose the
essential of cloud computing. [1]
In 2010 Pushpendra kumar pateria, Neha Marria discussed
resource provisioning in sky environment. Resource manager
is used for resource provisioning and allocate of resource as
user request. Offer the rule based resource manager in sky
environment for utilization the private cloud resource and
security requirement of resource of critical application and
data .Decision is made on the basis of rule. Performance of
resource manager is also evaluated by using cloudsim on basis
of resource utilization and cost in sky environment. Set
priorities request and allocate resource accordingly. Sky
computing provides computing concurrent access to multiple
clouds according user requirement. Define the Cloud services
like Software as a service (SaaS), Platform as a Service
(PaaS) and Infrastructure as a service. [2]
In 2010 Zhang Yu Hua, Zhang Jian ,Zhang Wei Hua present
argumentation about the intelligent cloud computing system
and Data warehouse that record the inside and outside data of
Cloud Computing System for data analysis and data mining.
Management problem of CCS are: balance between capacity
and demand, capacity development planning, performance
optimization, system safety management. Architecture of the
Intelligence cloud computing system is defined with Data
source, data warehouse and Cloud computing management
information system. [3]
In 2008 discussed about the Phoenix by Jianfeng Zhan, Lei
Wang, Bipo Tu, Yong Li, Peng Wang, Wei Zhou and Dan
Meng. In this paper discuss the designed and implemented
cloud management system software Phoenix Cloud. Different
department of large organization often maintain dedicate
cluster system for different computing loads. The department
from big organization have operated cluster system with
independent administration staffs and found many problem
like resource utilization rates of cluster system are varying,
dedicated cluster systems cannot provision enough resources
and number of administration staff for cluster system is high.
So here designed and implemented cloud management system
software Phoenix Cloud to consolidate high performance
computing jobs and Web service application on shared cluster
system. Phoenix Cloud decreases the scale of required cluster
system for a large organization.
improves the benefit of scientific computing department,
and provisions resources. [4]
In 2010 Shu-Ching Wang, Kuo-Qin Yan, Wen-Pin Liao and
Shun-Sheng Wang discussed about Load Balancing in Three-
Level Cloud Computing Network. Cloud computing utilize
low power host to achieve high reliability. In this Cloud
computing is to utilize the computing resources on the
network to facilitate the execution of complicated tasks that
require large-scale computation. Use the OLB scheduling
algorithm is used to attempt each node keep busy and goal of
load balance. Also proposed LBMM (Load Balance Min-
Min) scheduling algorithm can make the minimum execution
time of each task on cloud computing environment and this
will improve the load unbalance of the Min-Min. In order to
reach load balance and decrease execution time for each node
in the three-level cloud computing network, the OLB and
LBMM scheduling algorithm are integrated. The load
balancing of three-level cloud computing network is utilized
all calculating result could be integrated first by the
secondlevel node [5]
In January 31, 2011, Sivadon Chaisiri, Bu-Sung Lee, and
Dusit Niyato discuss about the Optimization of Resource
Provisioning Cost. Under the resource provisioning optimal
cloud provisioning algorithm illustrates virtual machine
management that consider multiple provisioning stages with
demand price uncertainty. In this task system model of cloud
computing environment has been thoroughly explained using
various techniques such as cloud consumer, virtual machine
and cloud broker in details. [8]
The agent-based adaptive resource allocation is discussed in
2011 by the Gihun Jung, Kwang Mong Sim. In this paper the
provider needs to allocate each consumer request to an
appropriate data center among the distributed data centers
because these consumers can satisfy with the service in terms
of fast allocation time and execution response time. Service
provider offers their resources under the infrastructure as a
service model. For IaaS the service provider delivers its
resources at the request of consumers in the form of VMs. To
find an appropriate data center for the consumer request,
propose an adaptive resource allocation model considers both
the geographical distance between the location of consumer
and datacenters and the workload of data center. With
experiment the adaptive resource allocation model shows
higher performance. An agent based test bed designed and
implemented to demonstrate the proposed adaptive resource
allocation model. The test bed implemented using JAVA with
JADE (Java Agent Development framework). [9]
3. SYSTEM ARCHITECTURE
Dynamically adjusting the number of machines has each type
to minimize total energy consumption and performance
penalty in terms of scheduling delay. In my proposed using
the hybrid method for resource provisioning in data centers.
This method is used to allocate the resources at the working
conditions and also energy stored for the power
consumptions. Proposed method is used to allocate the
process behind the cloud storage.
3.1 User Interface Design
In this module we design the windows for the project. These
windows are used to send a message from one to another. In
this module mainly we are focusing the login design page
with the Partial knowledge information. Application Users
need to view the application they need to login through the
User Interface GUI is the media to connect User and Media
Database.

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3.2 Dynamic Capacity Provisioning In Data Centers
In this section we address the simulation of heterogeneous
active machine. Here we are going to create difference
machine for stored based on the client demand that is
production data centers often comprise heterogeneous
machines with different capacities and energy consumption
characteristics..The energy level consumption is updated by
the cloud service provider which belongs to the datacenter.
Data center have more right the route the heterogeneous active
machine. This area regulates Heterogeneous active machine
creation. Also it regulates memory consumption and key
challenge that often has been overlooked or considered
difficult to address is heterogeneity,
3.3 Machine Heterogeneity Model Approach
This simulation addresses the production data centers often
comprise several types of machines from multiple update.
They have heterogeneous processor architectures and speeds,
hardware features, memory and disk capacities. Consequently,
they have different runtime energy consumption rates. Here
we are going to create difference machine for stored based on
the client demand that is production data centers often
comprise heterogeneous machines with different capacities
and energy consumption characteristics..The energy level
consumption is updated by the cloud service provider which
belongs to the datacenter.
3.4 Resource Monitoring and Management System
Production data centers receive a vast number of
heterogeneous resource requests with diverse resource
demands, durations, priorities and performance. The
heterogeneous nature of both machine and workload in
production cloud environments has profound implications on
the design of DCP schemes. Here we address accurate
characterization of both workload and machine
heterogeneities. Standard K-means clustering, we show that
the heterogeneous workload can be divided into multiple task
classes with similar characteristics in terms of resource and
performance objectives.
3.5 Dynamic Capacity Provisioning Approach
The workload traces contain scheduling events, resource
demand and usage records. The job is an application that
consists of one or more tasks. Each task is scheduled on a
single physical machine. When a job is submitted, the user
can specify the maximum allowed resource demand for each
task in terms of required CPU and memory size. Dynamically
adjust the number of active machines in a data center in order
to reduce energy consumption while meeting the service level
objectives (SLOs) of workloads. The coordinates of each
point in these figures correspond to a combination of CPU and
memory requirements.
Figure 1. Architecture of proposed framework
The resource provisioning in Cloud Computing, the long-held
dream of computing as a utility, has the potential to transform
a large part of the IT industry, making software even more
attractive as a service and shaping the way IT hardware is
designed and purchased. Developers with innovative ideas for
new Internet services no longer require the large capital
outlays in hardware to deploy their service or the human
expense to operate it. They need not be concerned about over-
provisioning for a service whose popularity does not meet
their predictions, thus wasting costly resources, or under-
provisioning for one that becomes wildly popular, thus
missing potential customers and revenue. The Methodology
based on Infrastructure as a Service layer to access resources
on-demand. A Rule Based Resource Manager is proposed to
scale up private cloud and presents a cost effective solution in
terms of money spent to scale up private cloud on-demand by
taking public cloud’s resources and that never permits secure
information to cross the organization’s firewall in hybrid
cloud. Also set the time for public cloud and private cloud to
fulfill the request.
4. CONCLUSION
The user’s usages have large number of progress in an
environment. So there have large number of problems are
occurred in the cloud. The resource provisioning can be
overcome by hybrid method. This proposed method is used to
allocate the resources with working conditions. It shows the
energy is very efficiency and the overcome the workload with
the good performance.

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