Adaptive data replication strategy in cloud computing for performance improvement

2016 ◽  
Vol 10 (5) ◽  
pp. 925-935 ◽  
Author(s):  
Najme Mansouri
Keyword(s):  
Cloud Computing ◽  
Performance Improvement ◽  
Data Replication ◽  
Replication Strategy

Hierarchical data replication strategy to improve performance in cloud computing

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Najme Mansouri ◽  
Mohammad Masoud Javidi ◽  
Behnam Mohammad Hasani Zade
Keyword(s):  
Cloud Computing ◽  
Data Replication ◽  
Hierarchical Data ◽  
Improve Performance ◽  
Replication Strategy

Enhanced Data Security from Single Clouds to Multiclouds

2018 ◽  
Vol 6 (9) ◽  
pp. 66
Author(s):  
Umesh Banodha ◽  
Praveen Kumar Kataria
Keyword(s):  
Data Storage ◽  
Network Connectivity ◽  
Data Replication ◽  
Recovery Process ◽  
Cloud Environment ◽  
Storage Security ◽  
Cloud Data ◽  
Replication Strategy ◽  
Cloud Data Storage ◽  
Data Files

Cloud is an emerging technology that stores the necessary data and electronic form of data is produced in gigantic quantity. It is vital to maintain the efficacy of this data the need of data recovery services is highly essential. Cloud computing is anticipated as the vital foundation for the creation of IT enterprise and it is an impeccable solution to move databases and application software to big data centers where managing data and services is not completely reliable. Our focus will be on the cloud data storage security which is a vital feature when it comes to giving quality service. It should also be noted that cloud environment comprises of extremely dynamic and heterogeneous environment and because of high scale physical data and resources, the failure of data centre nodes is completely normal.Therefore, cloud environment needs effective adaptive management of data replication to handle the indispensable characteristic of the cloud environment. Disaster recovery using cloud resources is an attractive approach and data replication strategy which attentively helps to choose the data files for replication and the strategy proposed tells dynamically about the number of replicas and effective data nodes for replication. Thus, the objective of future algorithm is useful to help users together the information from a remote location where network connectivity is absent and secondly to recover files in case it gets deleted or wrecked because of any reason. Even, time oriented problems are getting resolved so in less time recovery process is executed.

scholarly journals Data replication strategy with satisfaction of availability, performance and tenant budget requirements

2019 ◽  
Vol 22 (4) ◽  
pp. 1199-1210 ◽  
Author(s):  
Said Limam ◽  
Riad Mokadem ◽  
Ghalem Belalem
Keyword(s):  
Data Replication ◽  
Replication Strategy

scholarly journals Parallel MapReduce: Maximizing Cloud Resource Utilization and Performance Improvement Using Parallel Execution Strategies

2018 ◽  
Vol 2018 ◽  
pp. 1-17
Author(s):  
Ahmed Abdulhakim Al-Absi ◽  
Najeeb Abbas Al-Sammarraie ◽  
Wael Mohamed Shaher Yafooz ◽  
Dae-Ki Kang
Keyword(s):  
Cloud Computing ◽  
Performance Improvement ◽  
Biomedical Applications ◽  
Large Data ◽  
Cost Effective ◽  
Parallel Execution ◽  
Computing Platform ◽  
Execution Strategy ◽  
Large Data Analysis ◽  
Cloud Resources

MapReduce is the preferred cloud computing framework used in large data analysis and application processing. MapReduce frameworks currently in place suffer performance degradation due to the adoption of sequential processing approaches with little modification and thus exhibit underutilization of cloud resources. To overcome this drawback and reduce costs, we introduce a Parallel MapReduce (PMR) framework in this paper. We design a novel parallel execution strategy of Map and Reduce worker nodes. Our strategy enables further performance improvement and efficient utilization of cloud resources execution of Map and Reduce functions to utilize multicore environments available with computing nodes. We explain in detail makespan modeling and working principle of the PMR framework in the paper. Performance of PMR is compared with Hadoop through experiments considering three biomedical applications. Experiments conducted for BLAST, CAP3, and DeepBind biomedical applications report makespan time reduction of 38.92%, 18.00%, and 34.62% considering the PMR framework against Hadoop framework. Experiments' results prove that the PMR cloud computing platform proposed is robust, cost-effective, and scalable, which sufficiently supports diverse applications on public and private cloud platforms. Consequently, overall presentation and results indicate that there is good matching between theoretical makespan modeling presented and experimental values investigated.

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