Partitioning based Approach for Load Balancing Public Cloud
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This document presents a load balancing model based on cloud partitioning for public cloud environments, addressing the performance issues caused by uneven workload distribution. It highlights the importance of using dynamic load balancing strategies, implemented through a main controller and balancers that manage job distribution among cloud partitions. By improving resource allocation and efficiency, the proposed system aims to enhance overall user satisfaction in cloud computing services.
![T.V V S S C Divya.et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 6, Issue 5, (Part - 1) May 2016, pp.22-24
www.ijera.com 24 | P a g e
3. Balancers
4. Server Status
CONCLUSION
The overall goal of this project is to balance the
load on clouds. Balancing load on the cloud will
improve the performance of cloud services
substantially It will prevent overloading of servers,
which would otherwise degrade the performance
VII. FUTURE SCOPE
Find other load balance strategy: Other load
balance strategies may provide better results, so
tests are needed to compare different strategies.
Many tests are needed to guarantee system
availability and efficiency.
REFERENCES
[1] “Comscore,”
http://www.comscoredatamine.com/.
[2] A. G. Miklas, K. K. Gollu, K. K. W. Chan,
S. Saroiu, P. K. Gummadi, and E. de Lara,
“Exploiting social interactions in mobile
systems,” in Ubicomp, 2007, pp. 409–428.
[3] D. Niyato, P. Wang, W. Saad, and A.
Hjørungnes, “Controlled coalitional games
for cooperative mobile social
networks,” IEEE Transactions on Vehicular
Technology, vol. 60, no. 4, pp. 1812–1824,
2011.
[4] M. Brereton, P. Roe, M. Foth, J. M. Bunker,
and L. Buys, “Designing participation in
agile ridesharing with mobile social
software,” in OZCHI, 2009, pp. 257–260.
[5] Z. Yang, B. Zhang, J. Dai, A. C. Champion,
D. Xuan, and D. Li, “E-smalltalker: A
distributed mobile system for social
networking in physical proximity,” in
ICDCS, 2010, pp. 468–477.
[6] References R. Hunter, The why of cloud,
http://www.gartner.com/
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Partitioning based Approach for Load Balancing Public Cloud
- 1. T.V V S S C Divya.et al. Int. Journal of Engineering Research and Applications www.ijera.com ISSN: 2248-9622, Vol. 6, Issue 5, (Part - 1) May 2016, pp.22-24 www.ijera.com 22 | P a g e Partitioning based Approach for Load Balancing Public Cloud 1 T.V V S S C Divya,2 V.Sireesha ,3 S.k.Chaitanya,4 N.Baswanth 1, 2, 3,4 Pursuing B.Tech (CSE) from St. Ann’s College of Engineering. & Technology. Chirala, Andhra Pradesh -, 523 167 INDIA 5 Dr. P Harini working as Professor & HOD(CSE) in St. Ann’s College of Engineering. & Technology. Chirala, Andhra Pradesh -, 523 167 INDIA ABSTRACT Load Balancing Model Based on Cloud Partitioning for the Public Cloud environment has an important impact on the performance of network load. A cloud computing system which does not use load balancing has numerous drawbacks. Now-a-days the usage of internet and related resources has increased widely. Due to this there is tremendous increase in workload. So there is uneven distribution of this workload which results in server overloading and may crash. In such systems the resources are not optimally used. Due to this the performance degrades and efficiency reduces. Cloud computing efficient and improves user satisfaction. This project is a better load balance model for public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory for load balancing strategy to improve the efficiency in the public cloud environment. I. INTRODUCTION Cloud Computing is a concept that has many computers interconnected through a real time network like internet. cloud computing means distributed computing. Cloud computing enables convenient, on-demand, dynamic and reliable use of distributed computing resources. The cloud computing model has five main characteristics on demand service, broad network access, resource pooling, flexibility, measured service. Cloud computing is efficient and scalable but to maintain the stability of processing many jobs in the cloud computing is a very difficult problem. The job arrival pattern cannot be predicted and the capacities of each node in the cloud differ. Hence for balancing the usage of internet and related resources has increased widely. Due to this there is tremendous increase in workload. So there is uneven distribution of this workload which results in server overloading and may crash. In such the load, it is crucial to control workloads to improve system performance and maintain stability. The load on every cloud is variable and dependent on various factors. To handle this problem of imbalance of load on clouds and to increase its working efficiency, this paper tries to implement “A Model for load balancing by Partitioning the Public Cloud”. Good load balancing makes cloud computing more efficient and also improves user satisfaction . This project is aimed at the public cloud which has numerous nodes. A system having main controller, balancers, servers and a client is implemented here. It introduces a switch mechanism to choose different strategies for different situations. This paper divides the public cloud into cloud partitions and applies different strategies to balance the load on cloud. 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 Assigning jobs to the cloud partition 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 ˛, change to idle status. (2) Normal: When the percentage of the normal nodes exceeds ˇ, change to normal load status. (3) Overload: When the percentage of the overloaded nodes exceeds change to overloaded status. RESEARCH ARTICLE OPEN ACCESS
- 2. T.V V S S C Divya.et al. Int. Journal of Engineering Research and Applications www.ijera.com ISSN: 2248-9622, Vol. 6, Issue 5, (Part - 1) May 2016, pp.22-24 www.ijera.com 23 | P a g e II. RELATED WORK II.I Existing System with Drawbacks Cloud computing is efficient and scalable but maintaining the stability of processing so many jobs in the cloud computing environment is a very complex problem with load balancing receiving much attention for researchers. 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. 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. A dynamic scheme is used here for its flexibility. II.II Proposed System with Features Load balancing schemes depending on whether the system dynamics are important can be either static or 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. A dynamic scheme is used here for its flexibility. 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. 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. When the environment is very large and complex, these divisions simplify the load balancing. The cloud has a main controller that chooses the suitable partitions for arriving jobs while the balancer for each cloud partition chooses the best load balancing strategy. III. SYSTEM ARCHITECTURE IV MODULES The Following modules are : User Module Admin Module User Module:- In this module, Users are having authentication and security to access the detail which is presented in the ontology system. Before accessing or searching the details user should have the account in that otherwise they should register first. Admin Module: In cloud computing model admin can login into system and they can manage the servers in the module. Admin can check server status and they can balance the severs. Admin can accept the permissions which are send from user. V. RESULTS 1. User Home Page 2.Admin Home Page
- 3. T.V V S S C Divya.et al. Int. Journal of Engineering Research and Applications www.ijera.com ISSN: 2248-9622, Vol. 6, Issue 5, (Part - 1) May 2016, pp.22-24 www.ijera.com 24 | P a g e 3. Balancers 4. Server Status CONCLUSION The overall goal of this project is to balance the load on clouds. Balancing load on the cloud will improve the performance of cloud services substantially It will prevent overloading of servers, which would otherwise degrade the performance VII. FUTURE SCOPE Find other load balance strategy: Other load balance strategies may provide better results, so tests are needed to compare different strategies. Many tests are needed to guarantee system availability and efficiency. REFERENCES [1] “Comscore,” http://www.comscoredatamine.com/. [2] A. G. Miklas, K. K. Gollu, K. K. W. Chan, S. Saroiu, P. K. Gummadi, and E. de Lara, “Exploiting social interactions in mobile systems,” in Ubicomp, 2007, pp. 409–428. [3] D. Niyato, P. Wang, W. Saad, and A. Hjørungnes, “Controlled coalitional games for cooperative mobile social networks,” IEEE Transactions on Vehicular Technology, vol. 60, no. 4, pp. 1812–1824, 2011. [4] M. Brereton, P. Roe, M. Foth, J. M. Bunker, and L. Buys, “Designing participation in agile ridesharing with mobile social software,” in OZCHI, 2009, pp. 257–260. [5] Z. Yang, B. Zhang, J. Dai, A. C. Champion, D. Xuan, and D. Li, “E-smalltalker: A distributed mobile system for social networking in physical proximity,” in ICDCS, 2010, pp. 468–477. [6] References R. Hunter, The why of cloud, http://www.gartner.com/ DisplayDocument?doc cd=226469&ref= g noreg