Optimization of Resource Provisioning Cost in Cloud Computing

vanillaoliveInternet and Web Development

Nov 3, 2013 (3 years and 10 months ago)

95 views

Optimization of Resource Provisioning Cost in
Cloud Computing



ABSTRACT:

Cloud consumer can successfully reduce total cost of resource provisioning in cloud
computing environment
.
Numerical studies are extensively performed in which the results
clearly show that with the OCRP
algorithm
, cloud

consumer can successfully minimize total cost
of resource provisioning
in cloud computing environments.

In cloud computing,
cloud providers
can present cloud consumers two provisioning policy for computing resources that is reservation
and on
-
demand plans. In general, cost of utilize computing resources provisioned by condition
plan is cheaper than that provisioned by on
-
demand plan, since clo
ud consumer has to pay to
provider in go forward With the state plan, the customer can decrease the total resource
provisioning cost. However, the finest advance reservation of resources is hard to be achieving
due to uncertainty of consume
r’s future deman
d and providers

resource prices. To address this
difficulty, an optimal cloud resource

provisioning
OCRP

(
optimal cloud resource
provisioning
)



algorithm is projected by formulating a stochastic programming model. The
OCRP algorithm can provision computin
g resources for being used in multiple provisioning
stages as well as a long
-
term plan,. The demand and price doubt is considered in OCRP. In this
paper, different approach
to

gain the solution of the OCRP

algorithm is measured including
deterministic corr
esponding formulation, sample
-
average estimate, and Benders decomposition.
Numerical studies are at length achieve

in which the results clearly show that with the OCRP
algorithm
.


EXISTING SYSTEM:



The condition plan, the price to make use of capital is cheaper than that of the on
-
demand plan. In this method, the user can reduce the price of compute resource provisioning by
using the condition plan.


T
he condition plan, the cloud clients a priori re
serve the resources in
advance.


Disadvantage:




The below provisioning trouble can happen when the kept resources are not capable to
completely meet up the demand due to its doubt.



The over provisioning problem can occur if the reserved resources are more

than the
actual demand in which part of a resource pool will be underutilized.




PROPOSED SYSTEM:

Reduce together below provisioning and over provisioning complexity under the demand
and cost vagueness in cloud compute environment is our motivation to
explore a resource
provisioning plan for cloud consumers. In demanding, an optimal cloud resource provisioning
(OCRP) algorithm is planned to reduce the total charge for provisioning property in a certain
time period.


Advantages:




In provisioning trouble
can be solved by provisioning more resources with on
-
demand
plan to fit the extra demand, the high cost will be incurred due to more expensive price of
resource provisioning with on
-
demand plan.



The cloud consumer to minimize the total cost of resource pro
visioning by reducing the
on
-
demand cost and oversubscribed cost of under provisioning and over provisioning.


SYSTEM SPECIFICATION

HARDWARE REQUIREMENTS

Processor



: Pentium IV

RAM




: 512 MB

Hard Disk



: 80 GB
s



SOFTWARE REQUIREM
ENTS



Front End

: JAVA



Development tool




:
Eclipse



Back End



: SQL Server 2005



Operating System


: Windows XP


FUTURE ENHANCEMENT:




Cloud computing is one of the important domain in today’s technology world and it
is accessible from anywhere so in our future work security constraints are implemented to
provide highly secured resource provisioning.




MODULES:




Cloud computing




Optimal cloud resource provisioning (OCRP)




Virtual machine placement




Quality of Services (QoS)


MODULES DESCRIPTION:




Cloud C
omputing:



Cloud computing is a general term for anything that involves
delivering hosted services over the Internet. These services are broadly divided into three
categories: Infrastructure
-
as
-
a
-
Service (IaaS), Platform
-
as
-
a
-
Service (PaaS) and
Software
-
as
-
a
-
Service
(SaaS). The name cloud computing was inspired by the cloud
symbol that's often used to represent the Internet in flowcharts and diagrams. A cloud
service has three distinct characteristics that differentiate it from traditional hosting. It is
sold on deman
d, typically by the minute or the hour; it is elastic
--

a user can have as
much or as little of a service as they want at any given time; and the service is fully
managed by the provider (the consumer needs nothing but a personal computer and
Internet acc
ess). Significant innovations in virtualization and distributed computing, as
well as improved access to high
-
speed Internet and a weak economy, have accelerated
interest in cloud computing.






Optimal Cloud Resource Provisioning (OCRP):




In particular, an optimal cloud resource provisioning (OCRP)
algorithm is proposed to minimize the total cost for provisioning resources in a certain
time period.
An optimal cloud resource provisioning (OCRP) algorithm
is proposed by
formulating a stochastic programming model.

The OCRP algorithm can provision
computing resources for being used in multiple provisioning stages as well as a long
-
term
plan, e.g., four stages in a quarter plan and twelve stages in a yearly pl
an. The demand
and price uncertainty is considered in OCRP.

To make an optimal decision, the demand
uncertainty from cloud consumer side and price uncertainty from cloud providers are
taken into account to adjust the tradeoff between on
-
demand and oversubs
cribed costs.
This optimal decision is obtained by formulating and solving a stochastic integer
programming problem with multistage recourse.
Benders decomposition
and sample
-
average approximation are also discussed as the possible techniques to

solve the
OCRP
algorithm. Extensive numerical studies and

simulations are performed, and the results
show that OCRP

can minimize the total cost under uncertainty.




Virtual Machine Placement:



When a virtual machine is deployed on a host, the process of
selecting the most suitable host for the virtual machine is known as
virtual machine
placement
, or simply
placement
. During placement, hosts are rated based on the virtual
machine’s hardware and
resource requirements and the anticipated usage of resources.
Host ratings also take into consideration the placement goal: either resource
maximization on individual hosts or load balancing among hosts. The administrator
selects a host for the virtual mac
hine based on the host ratings.
Virtual machine
placement is the process of mapping virtual machines to physical machines. In other
words, virtual machine placement is the process of selecting the most suitable host for the
virtual machine. The process inv
olves categorizing the virtual machines hardware and
resources requirements and the anticipated usage of resources and the placement goal.
The placement goal can either be maximizing the usage of available resources or it can be
saving of power by being ab
le to shut down some servers. The autonomic virtual machine
placement algorithms are designed keeping in mind the above goals.




Quality of Services (QoS):



QoS

(Quality of Service) refers to a broad collection of
networking technologies and techniques. The goal of QoS is to provide guarantees on the
ability of a network to deliver predictable results. Elements of network performance
within the scope of QoS often

include availability (uptime),

bandwidth

(throughput),
latency (delay), and error rate. QoS involves prioritization of network traffic. QoS can be
targeted at a network interface, toward a given server or router's performance, or in terms
of specific appl
ications. A network monitoring system must typically be deployed as part
of QoS, to insure that networks are performing at the desired level. QoS is especially
important for the new generation of Internet applications such as

VoIP
, video
-
on
-
demand
and othe
r consumer services. Some core networking technologies like

Ethernet

were not
designed to support prioritized traffic or guaranteed performance levels, making it much
more difficult to implement QoS solutions across the Internet.








SYSTEM ARCHITECTURE
:






















DATA FLOW DIAGRAM:



Cloud


Consumer







Cloud Broker


Cloud Provider


Cloud Provider


Services


Reservation


On
-

Demand



DB