The document discusses performance analysis of cloud computing centers using queuing systems. It aims to evaluate key performance indicators like response time distribution and mean number of tasks using a queuing model. The proposed system models cloud server farms as COCOMO II systems to obtain more accurate estimations of performance metrics while addressing issues with existing models like high traffic intensity and service time variation. It analyzes how changing server numbers and buffer sizes impacts the performance indicators.

1. PERFORMANCE ANALYSIS OF CLOUD COMPUTING
AND COST ESTIMATION USING COCOMO II TECHNIQUE
OBJECTIVE:
The main objective of this project is to evaluate the performance analysis of
cloud computing centers using queuing systems. To obtain accurate estimation of
the complete probability distribution of the request response time and other
important performance indicators such as mean number of tasks in the system,
blocking probability, and probability.
PROBLEM DIFINITION:
 A cloud center can have a large number of facility (server) nodes,
typically of the order of hundreds or thousands, traditional queuing
analysis rarely considers systems of this size.
 The coefficient of variation of task service time may be high.
 Due to the dynamic nature of cloud environments, diversity of user’s
requests and time dependency of load, cloud centers must provide
expected quality of service at widely varying loads.
ABSTRACT:
Cloud Computing is a novel paradigm for the provision of computing
infrastructure, which aims to shift the location of the computing infrastructure to

2. the network in order to reduce the costs of management and maintenance of
hardware and software resources. Cloud computing has a service-oriented
architecture in which services are broadly divided into three categories:
Infrastructure-as-a- Service (IaaS), which includes equipment such as hardware,
Storage, servers, and networking components are made accessible over the
Internet; Platform-as-a-Service (PaaS), which includes hardware and software
computing platforms such as virtualized servers, operating systems, and the like;
and Software-as-a-Service (SaaS), which includes software applications and other
hosted services.
To obtain accurate estimation of the complete probability distribution of the
request response time and other important performance indicators. The model
allows cloud operators to determine the relationship between the number of servers
and input buffer size, on one side, and the performance indicators such as mean
number of tasks in the system, blocking probability, and probability that a task will
obtain immediate service, on the other.
EXISTING SYSTEM:
 The number of servers is comparatively small, typically below 10,
which makes them unsuitable for performance analysis of cloud
computing data centers.
 Approximations are very sensitive to the probability distribution of
task service times.

3.  User may submit many tasks at a time because of this bags-of-task
will appear.
DISADVANTAGES:
 Due to dynamic nature of cloud environments, diversity of user’s
requests and time dependency of load is high.
 Traffic intensity is high.
 The coefficient of variation of task service time is high.
 Modeling errors.
PROPOSED SYSTEM:
In Proposed system, the task is sent to the cloud center is serviced within a
suitable facility node; upon finishing the service, the task leaves the center. A
facility node may contain different computing resources such as web servers,
database servers, directory servers, and others. A service level agreement, SLA,
outlines all aspects of cloud service usage and the obligations of both service
providers and clients, including various descriptors collectively referred to as
Quality of Service (QoS). QoS includes availability, throughput, reliability,
security, and many other parameters, but also performance indicators such as
response time, task blocking probability, probability of immediate service, and
mean number of tasks in the system, all of which may be determined using the
tools of queuing theory.

4. We model a cloud server farm as a COCOMO II system which indicates that
the inter arrival time of requestsis exponentially distributed, while task service
times are independent and identically distributed random variables that follow a
general distribution with mean value of u. The system under consideration contains
m servers which render service in order of task request arrivals (FCFS).The
capacity of system is m þ r which means the buffer size for incoming request is
equal to r. As the population size of a typical cloud center is relatively high while
the probability that a given user will request service is relatively small, the arrival
process can be modeled as a Markovian process.
ADVANTAGES:
 Less Traffic Intensity.
 Analytical technique based on an approximate Markov chain model
for best performance evaluation.
 General Service time for requests and large number of servers makes
our model flexible in terms of scalability and diversity of service time.
 High degree of accuracy for the mean number of tasks in the system,
blocking probability, probability, response time.
ALGORITHM USED:
1. COCOMO-II
2. A-Priori Algorithm

5. 3. AES (Advanced Encryption Standard)
ARCHITECTURE DIAGRAM:
Cloud Server User
H
Coordinator
Internet
CS1 CS2 CSn
Back-end Database
Shared File system
SYSTEM REQUIREMENTS:
Hardware Requirements:
• Intel Pentium IV
• 256/512 MB RAM
• 1 GB Free disk space or greater

6. • 1 GB on Boot Drive
• 17” XVGA display monitor
• 1 Network Interface Card (NIC)
Software Requirements:
• MS Windows XP/ windows 7
• MS IE Browser 6.0/later
• MS Dot Net Framework 4.0
• MS Visual Studio.Net 2010
• Internet Information Server (IIS)
• MS SQL Server 2005
• Windows Installer 3.1
APPLICATIONS:
1. Organizations
2. Cloud Providers Clients
3. Government Sectores