Improving load balancing for data-duplication in big data cloud computing networks |
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Authors: | Javadpour Amir Abadi Ali Majed Hossein Rezaei Samira Zomorodian Mozhdeh Rostami Ali Shokouhi |
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Institution: | 1.School of Computer Science and Technology, Guangzhou University, Guangzhou, 510006, China ;2.Department of Computer Science, University of Isfahan, Isfahan, Iran ;3.Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen, Netherlands ;4.Department of Computer Science and Electronic Engineering, Islamic Azad University of Khomeini Shahr, Khomeini Shahr, Iran ;5.Department of Communication Engineering, University of Sistan and Baluchestan, Zahedan, Iran ; |
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Abstract: | Data transmission and retrieval in a cloud computing environment are usually handled by storage device providers or physical storage units leased by third parties. Improving network performance considering power connectivity and resource stability while ensuring workload balance is a hot topic in cloud computing. In this research, we have addressed the data duplication problem by providing two dynamic models with two variant architectures to investigate the strengths and shortcomings of architectures in Big Data Cloud Computing Networks. The problems of the data duplication process will be discussed accurately in each model. Attempts have been made to improve the performance of the cloud network by taking into account and correcting the flaws of the previously proposed algorithms. The accuracy of the proposed models have been investigated by simulation. Achieved results indicate an increase in the workload balance of the network and a decrease in response time to user requests in the model with a grouped architecture for all the architectures. Also, the proposed duplicate data model with peer-to-peer network architecture has been able to increase the cloud network optimality compared to the models presented with the same architecture. |
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