In the FACTS-based transmission line, if the fault does not include FACTS device, then the impedance calculation is like an ordinary transmission line, and when the fault includes FACTS, then the impedance calculation accounts for the impedances introduced by FACTS device.

1. AN ENHANCED LOAD BALANCING FOR
A PUBLIC CLOUD BY USING DMA
GUIDED BY
S.Vinoth Kumar M.E.,
Asst.Professor
Department of Computer Science and
Engineering

2. ABSTRACT
 Load balancing is one of the major issues in allocating
job or store information in a public cloud.
 We introduce DMA for efficient cloud division and
load balance strategy.
 And also set the refresh period and load status
evaluation for efficient balancing load in public cloud
environment.

3. EXISTING SYSTEM
 ESCE (Equally Spread Current Execution )algorithm .
 Round Robin Algorithm.
 Ant Colony Algorithm.

4. DRAWBACKS
 The degree of load is calculated and shared it to
system in static scheme.
 There is no updated status of the system so the
efficiency is decreased by waiting other load.

5. PROPOSED SYSTEM
 DMA (Division Manage Algorithm)
 Refresh Point

6. ADVANTAGE
 By updating the system information we can know the
status of the system.
 Through using refresh point we can also provide better
status evaluation and balance strategy.
 Dynamic scheme.

7. SYSTEM CONFIGURATION
Operating System -Windows7
Frond End -Java Swing
Database -MS Access 2007
Database Connectivity -JDBC
Development Tool -NetBeans IDE 7.4
SOFTWARE REQUIREMENTS

8. HARDWARE REQUIREMENTS
Processor - Intel Pentium
RAM - 2 GB
Hard Disk - 320 GB

9. MODULE DISCRIPTION
 Authentication module
 Controller module
 Balancing model
 Receiver side

10.  Controller can enter the user name and password.
 Correct.
 Avoid registration process.
CONTROLLER MODULE
 Assigns the suitable partition
 Stored File Detail
AUTHENTICATION MODULE

11.  View Load Status
 Update Balancer
RECEIVER SIDE
 File Name and Secret key.
 Get data
BALANCING MODULE

12. SYSTEM ARCHITECTURE
Fig: System Architecture

13. SCREENSHOT
LOGIN

14. ADMIN CONTROL

15. LOAD STATUS

16. SELECT BALANCER

17. Fig: File Browse
FILE BROWSE

18. SHOW UPLOADING FILE

19. Fig: View Files
VIEW FILE

20. CONCLUSION
Better Load Balancing
The division rule should simply be based on the
geographic location
 In the data statistics analysis, the main controller
and the cloud partition balancers need to refresh
the information at a fixed period.

21. FUTURE ENHANCEMENT
Heuristics load balancing
By using this load balancing We will achieve
swapping of files takes place normal state of
servers is maintained throughout the cloud.
Time consumption of loading files is reduced.

22. REFERENCE
 Nishant. K, Sharma. P, and Nitin. N. (2012), ‘Load balancing of nodes in cloud
using ant colony optimization’, vol no 4, pp 245-249.
 Randles.M, Lamb.D, and Taleb-Bendiab.A, ‘A comparative study into distributed
load balancing algorithms for cloud computing’, in Proc.
 IEEE 24th International Conference on Advanced Information Networking and
Applications, Perth, Australia.
 Penmatsa .S,and Chronopoulos.A.T,’Game-theoretic static load balancing for
distributed systems’, Journal of Parallel and Distributed Computing, vol. 71.
 Grosu.D,Chronopoulos.A.T, and Leung.M.Y ,’ Load balancing in distributed
systems’,n Proc. 16th IEEE Intl Parallel and Distributed Processing .

23. THANKING YOU