This document presents a load balancing model for public cloud environments using game theory to enhance efficiency and user satisfaction. It involves partitioning the cloud into distinct sections managed by a main controller and balancers, which dynamically assign jobs based on the status of cloud partitions. The proposed model aims to develop an effective load balancing algorithm, focusing on optimizing performance parameters like CPU load and memory capacity.

2. A LOAD BALANCING MODEL BASED ON CLOUD
PARTITIONING FOR THE PUBLIC CLOUD

3. K.Poojitha(10NP1A1219)
J.Pooja Naidu(10NP1A1220)
G.Prathyusha (10NP1A1235)
V.Lavanya(10NP1A1247)

5. VISION
• Load balancing in the cloud computing environment has an
important impact on the performance. Good load balancing
makes cloud computing more efficient and improves user
satisfaction.
• The algorithm applies the “game theory” to the load
balancing strategy to improve the efficiency in the public
cloud environment.

6. EXISTING SYSTEM
• Since the job arrival pattern is not predictable and the capacities of each
node in the cloud differ, for load balancing problem, workload control is
crucial to improve system performance and maintain stability.
• In general ,Load balancing schemes depending on whether the system
dynamics are important can be either static and dynamic. Static schemes
do not use the system information and are less complex while dynamic
schemes will bring additional costs for the system but can change as the
system status changes.

7. PROPOSED SYSTEM
• The load balancing model given in this article is aimed
at the public cloud which has numerous nodes with
distributed computing resources in many different
geographic locations.
• Thus, this model divides the public cloud into several
cloud partitions.
• The model has a main controller and balancers to
gather and analyze the information. Thus, the dynamic
control has little influence on the other working
nodes. The system status then provides a basis for
choosing the right load balancing strategy.

8. MODULES

9. • The load balance solution is done by the main controller and the
balancers. The main controller first assigns jobs to the suitable
cloud partition and then communicates with the balancers in each
partition to refresh this status information. Since the main
controller deals with information for each partition, smaller data
sets will lead to the higher processing rates. The balancers in each
partition gather the status information from every node and then
choose the right strategy to distribute the jobs. The relationship
between the balancers and the main controller

10. SYSTEM ARCHITECTURE

11. When a job arrives at the public cloud, the first step is to choose the
right partition. The cloud partition status can be divided into three
types:
(1) Idle: When the percentage of idle nodes exceeds “ALPHA”, change
to idle status.
(2) Normal: When the percentage of the normal nodes exceeds
“BETA”, change to normal load status.
(3) Overload: When the percentage of the overloaded nodes exceeds
,”GAMMA” change to overloaded status.
The parameters ALPHA,BETA,GAMMA and are set by the cloud
partition balancers.
The main controller has to communicate with the balancers frequently
to refresh the status information.

13. FUNCTIONAL REQUIREMENTS

14. SYSTEM CONFIGURATION
• HARDWARE CONFIGURATION
• Processor - Pentium –IV
• Speed - 1.1 Ghz
• RAM - 256 MB(min)
• Hard Disk - 20 GB
• Key Board - Standard Windows Keyboard
• Mouse - Two or Three Button Mouse
• Monitor - SVGA-Super video graphics array

15. SYSTEM CONFIGURATION
• SOFTWARE CONFIGURATION
• Operating System : Windows XP,7
• Programming Language : JAVA
• Java Version : JDK 1.6 & above.

16. CONCLUSION
• Till now we have discussed on basic concepts of Cloud
Computing and Load balancing. The research work can
be proceeded to implement the total solution of load
balancing in a complete cloud environment. Our
objective for this paper is to develop an effective
load balancing algorithm using Round Robin technique
to maximize or minimize different performance
parameters like CPU load, Memory capacity, Delay or
network load for the clouds of different sizes.

17. REFERENCE
Gaochao Xu, Junjie Pang, and Xiaodong Fu “A Load Balancing
Model Based on Cloud Partitioning for the Public Cloud”
TSINGHUA SCIENCE AND TECHNOLOGY ISSN 1007 - 0214
04 /12 pp 34-39 Volume 18, Number 1, February 2013.