Keeping up with storage: Decentralized, write-enabled dynamic geo-replication

Matri, Pierre; Pérez, Marı́a S.; Costan, Alexandru; Bougé, Luc; Antoniu, Gabriel

doi:10.1016/j.future.2017.06.009

Cited by 6 publications

(5 citation statements)

References 31 publications

(41 reference statements)

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“…Many efforts have been made to propose and develop different dynamic data replication algorithms, such as 17 , 44 – 46 . A prefetching-aware data replication (PDR) strategy was proposed in Ref.…”

Section: Related Workmentioning

confidence: 99%

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Geographically distributed data management to support large-scale data analysis

Emara,

Trinh,

Huang

2023

Sci Rep

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Nowadays, several companies prefer storing their data on multiple data centers with replication for many reasons. The data that spans various data centers ensures the fastest possible response time for customers and workforces who are geographically separated. It also provides protecting the information from the loss in case a single data center experiences a disaster. However, the amount of data is increasing at a rapid pace, which leads to challenges in storage, analysis, and various processing tasks. In this paper, we propose and design a geographically distributed data management framework to manage the massive data stored and distributed among geo-distributed data centers. The goal of the proposed framework is to enable efficient use of the distributed data blocks for various data analysis tasks. The architecture of the proposed framework is composed of a grid of geo-distributed data centers connected to a data controller (DCtrl). The DCtrl is responsible for organizing and managing the block replicas across the geo-distributed data centers. We use the BDMS system as the installed system on the distributed data centers. BDMS stores the big data file as a set of random sample data blocks, each being a random sample of the whole data file. Then, DCtrl distributes these data blocks into multiple data centers with replication. In analyzing a big data file distributed based on the proposed framework, we randomly select a sample of data blocks replicated from other data centers on any data center. We use simulation results to demonstrate the performance of the proposed framework in big data analysis across geo-distributed data centers.

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Section: Related Workmentioning

confidence: 99%

“…Finally, it replaces the unnecessary replicas with the more popular replicas to save the storage space of each node. To leverage the decentralized architecture of decentralized storage systems such as Dynamo 47 or Voldemort 48 , Matri et al 44 proposed a write-enabled dynamic replication scheme.…”

Section: Related Workmentioning

confidence: 99%

Geographically distributed data management to support large-scale data analysis

Emara,

Trinh,

Huang

2023

Sci Rep

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“…Matri et al suggested a decentralized, write-enabled dynamic geo-replication (DWEDGR) approach. DWEDGR lets users to connect to the closest replication between nodes without having to make a prior request for data that can be changed [45].…”

Section: Related Workmentioning

confidence: 99%

A Novel Intelligent Approach for Dynamic Data Replication in Cloud Environment

et al. 2021

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In recent years, cloud computing research, specifically data replication techniques and their applications, has been growing. If the replicas number is raised and put in multiple positions, it will be expensive to maintain the data usability, performance and stability of the application systems. In this paper, two bio-inspired algorithms were proposed to improve both selection and placement of data replicas in the cloud environment. The suggested algorithms for dynamic data replication are multi-objective particle swarm optimization (MO-PSO) and ant colony optimization (MO-ACO). The first suggested algorithm, i.e., MO-PSO, is employed to obtain the best selected data replica depend on the most frequent one. However, the second suggested algorithm, i.e., MO-ACO, is employed to obtain the best data replica placement depend on the shortest distance, and the replicas availability. A simulation of the suggested strategy was carried out using CloudSim. Each data center (DC) includes hosts with set of virtual machines (VMs). The data replication order is determined at random from a thousand cloudlets. All replication files are randomly distributed in the proposed architecture. The performance of suggested techniques was evaluated against several approaches including: Adaptive Replica Dynamic Strategy (ARDS), Enhance Fast Spread (EFS), Genetic Algorithm (GA), Replica Selection and Placement (RSP), Popular File Replication First (PFRF), and Dynamic Cost-aware Re-replication and Re-balancing Strategy (DCR2S). The simulation results prove that MOPSO gives improved data replication compared against other algorithms. Additionally, MOACO realizes higher data availability, lower cost, and less bandwidth consumption compared with other algorithms.

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“…Matri et al [38] proposed a model for data de-duplication with a classified client-server. This model aims to reduce the delay in data access.…”

Section: Classified Client-server Architecturementioning

confidence: 99%

Improving load balancing for data-duplication in big data cloud computing networks

et al. 2021

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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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Keeping up with storage: Decentralized, write-enabled dynamic geo-replication

Cited by 6 publications

References 31 publications

Geographically distributed data management to support large-scale data analysis

Geographically distributed data management to support large-scale data analysis

A Novel Intelligent Approach for Dynamic Data Replication in Cloud Environment

Improving load balancing for data-duplication in big data cloud computing networks

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