Base paper ppt-. A load balancing model based on cloud partitioning for the public cloud.(according to base paper)
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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.
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A load balancing model based on cloud partitioning
A load balancing model based on cloud partitioningLavanya Vigrahala This document presents a load balancing model based on cloud partitioning for public clouds, emphasizing the importance of efficient load management to enhance performance and user satisfaction. The model utilizes a dynamic strategy incorporating game theory to adapt to various operational scenarios, allowing for improved decision-making in job assignments across distributed resources. Future work includes refining cloud division methodologies, optimizing data refresh periods, and exploring additional load balancing strategies.
A load balancing model based on cloud partitioning for the public cloud. ppt
A load balancing model based on cloud partitioning for the public cloud. ppt Lavanya Vigrahala This document presents a load balancing model for public cloud computing that utilizes cloud partitioning and game theory to improve performance and user satisfaction. It describes the architecture involving a main controller and balancers that manage job assignments across different cloud partitions based on their load status. The proposed system aims to optimize resource use through various load balancing algorithms, particularly focusing on the round robin technique.
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- 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
- 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.
- 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.