Final year projects in bangalore 9611582234 targetjsolutions@gmail.com

1. TARGETJ SOLUTIONS
• REAL TIME PROJECTS
• IEEE BASED PROJECTS
• EMBEDDED SYSTEMS
• PAPER PUBLICATIONS
• MATLAB PROJECTS
• targetjsolutions@gmail.com
• (0)9611582234, (0)9945657526

2. PERFORMANCE ANALYSIS
OF CLOUD COMPUTING
CENTERS USING
M/G/M/M+R QUEUING
SYSTEMS

3. ABSTRACT
• Successful development of cloud computing paradigm
necessitates accurate performance evaluation of cloud
data centers. As exact modeling of cloud centers is not
feasible due to the nature of cloud centers and diversity
of user requests, we describe a novel approximate
analytical model for performance evaluation of cloud
server farms and solve it 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

4. EXISTING SYSTEM
• It is vital to isolate network performance between the clients for ensuring fair
usage of the constrained and shared network resources of the physical
machine. Unfortunately, the existing network performance isolation
techniques are not effective for cloud computing systems because they are
difficult to be adopted in a large scale and require non-trivial modification to
the network stack of a guest OS

5. DISADVANTAGES
• *) Wastage of bandwidth and reduce performance of the server
• *) More expensive
• *) Tracking of usage is so difficult

6. PROPOSED SYSTEM
• we propose a performance isolation-enabled virtual distributed
Ethernet to overcome such difficulties. It is a network virtualization
software module running on a host OS. It intends to allocate fair share
of outgoing link bandwidth to the co-hosted clients and divide a
client's share to the virtual machines owned by it in a fair way.
• Our approach supports full virtualization of a guest OS, ease in wide
scale adoption, limited modification to the existing system, low run-time
overhead and work-conserving servicing. Experimental results
show the effectiveness of the proposed mechanism.
• Every client received at least 99.5% of its bandwidth share as specified
by its weight. 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

7. ADVANTAGES
• The goal of a streaming warehouse is to propagate new data across all the
relevant tables and views as quickly as possible. Once new data are loaded,
the applications and triggers defined on the warehouse can take immediate
action. This allows businesses to make decisions in nearly real time, which may
lead to increased profits, improved customer satisfaction, and prevention of
serious problems that could develop if no action was taken

8. SOFTWARE
REQUIREMENTS
• Operating System : Windows XP Professional
• Environment : Visual Studio .NET 2010
• Language : C#.NET
• Web Technology : Active Server Pages.Net
• Back end : MS-SQL-Server 2008

9. HARDWARE REQUIREMENTS:
Processor : Pentium III / IV
Hard Disk : 40 GB
Ram : 256 MB
Monitor : 15VGA Color
Mouse : Ball / Optical
Keyboard : 102 Keys

10. REFERENCE
• [1] B. Adelberg, H. Garcia-Molina, and B. Kao, “Applying Update
Streams in a Soft Real-Time Database System,” Proc. ACM SIGMOD
Int’l Conf. Management of Data, pp. 245-256, 1995.
• [2] B. Babcock, S. Babu, M. Datar, and R. Motwani, “Chain: Operator
Scheduling for Memory Minimization in Data Stream Systems,” Proc.
ACM SIGMOD Int’l Conf. Management of Data, pp. 253-264, 2003.
• [3] S. Babu, U. Srivastava, and J. Widom, “Exploiting K-constraints to
Reduce Memory Overhead in Continuous Queries over Data
Streams,” ACM Trans. Database Systems, vol. 29, no. 3, pp. 545- 580,
2004.
• [4] S. Baruah, “The Non-preemptive Scheduling of Periodic Tasks
upon Multiprocessors,” Real Time Systems, vol. 32, nos. 1/2, pp. 9- 20,
2006.
• [5] S. Baruah, N. Cohen, C. Plaxton, and D. Varvel, “Proportionate
Progress: A Notion of Fairness in Resource Allocation,” Algorithmica,
vol. 15, pp. 600-625, 1996.