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PERFORMANCE ANALYSIS OF CLOUD COMPUTING

CENTERS USING M/G/M/M+R QUEUING

SYSTEMS

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

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

DISADVANTAGES

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

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

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

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

HARDWARE REQUIREMENTS:

Processor : Pentium III / IVHard Disk : 40 GBRam : 256 MBMonitor : 15VGA ColorMouse : Ball / OpticalKeyboard : 102 Keys

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.