RS-Pooling: an adaptive data distribution strategy for fault-tolerant and large-scale storage systems

Quezada-Naquid, Moisés; Marcelín-Jiménez, Ricardo; Gonzalez-Compeán, J. L.; Carretero, Jesús

doi:10.1007/s11227-015-1569-7

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…CRUSH [38] is a pseudo-random data distribution algorithm that efficiently and robustly distributes replicas across heterogeneous and structured clusters. RS-Pooling [39] is an adaptive random data distribution strategy for fault-tolerant, large-scale storage systems. Moreover, a scheme distribution dynamic such as AREN [40] is a replication scheme for cloud storage based on bandwidth and a collaborative cache strategy to provide a number of replicas of the popular content.…”

Section: Related Workmentioning

confidence: 99%

On the Efficient Delivery and Storage of IoT Data in Edge–Fog–Cloud Environments

Barron

Sánchez‐Gallegos

Carrizales-Espinoza

et al. 2022

Sensors

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Cloud storage has become a keystone for organizations to manage large volumes of data produced by sensors at the edge as well as information produced by deep and machine learning applications. Nevertheless, the latency produced by geographic distributed systems deployed on any of the edge, the fog, or the cloud, leads to delays that are observed by end-users in the form of high response times. In this paper, we present an efficient scheme for the management and storage of Internet of Thing (IoT) data in edge–fog–cloud environments. In our proposal, entities called data containers are coupled, in a logical manner, with nano/microservices deployed on any of the edge, the fog, or the cloud. The data containers implement a hierarchical cache file system including storage levels such as in-memory, file system, and cloud services for transparently managing the input/output data operations produced by nano/microservices (e.g., a sensor hub collecting data from sensors at the edge or machine learning applications processing data at the edge). Data containers are interconnected through a secure and efficient content delivery network, which transparently and automatically performs the continuous delivery of data through the edge–fog–cloud. A prototype of our proposed scheme was implemented and evaluated in a case study based on the management of electrocardiogram sensor data. The obtained results reveal the suitability and efficiency of the proposed scheme.

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Section: Related Workmentioning

confidence: 99%

On the Efficient Delivery and Storage of IoT Data in Edge–Fog–Cloud Environments

Barron

Sánchez‐Gallegos

Carrizales-Espinoza

et al. 2022

Sensors

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show abstract

“…The conventional sensing layer is mainly used to collect relevant physical information, such as temperature, humidity, air composition, and optical signals. The continuous collection and storage of data by the relevant sensor networks of the IoT cause problems of data distribution and data management [ 1 – 3 ]. The relevant data of the IoT are mainly classified into structured data and unstructured data.…”

Section: Introductionmentioning

confidence: 99%

“…e continuous collection and storage of data by the relevant sensor networks of the IoT cause problems of data distribution and data management [1][2][3]. e relevant data of the IoT are mainly classified into structured data and unstructured data.…”

Section: Introductionmentioning

confidence: 99%

Load Balancing Cloud Storage Data Distribution Strategy of Internet of Things Terminal Nodes considering Access Cost

Jia-rong

2022

Computational Intelligence and Neuroscience

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With the rapid development of Internet of Things (IoT) technology, IoT terminal nodes are facing many challenges in data storage, distribution, and data management. In particular, in the IoT terminal nodes considering access cost, the corresponding data distribution and storage are professional, complex, and miscellaneous. Based on the abovementioned current situation, this article innovatively proposes a complex sensor data placement algorithm based on the cloud storage distribution of IoT terminal nodes. Under this algorithm, the accurate division of IoT data I/O methods is realized through reasonable configuration. Through the adaptive sensing algorithm, while fully considering the access cost of the algorithm, the performance of the IoT data storage system is further optimized. In the corresponding terminal node load balancing problem, this article innovatively proposes the terminal node data sorting and distribution algorithm through the node data. The sorting and distribution algorithm realizes the precise segmentation of the IoT data to be processed, thereby realizing the improvement of data reading and processing speed. Based on the proposed algorithm, this article designs a load balancing cloud storage data distribution optimization system of IoT terminal nodes considering access cost and carries out experimental verification in a real environment. The experimental results show that the data pattern division accuracy corresponding to the proposed distribution strategy is improved to 97.13% and the corresponding data access efficiency is improved to 98.3%, compared with the traditional distribution strategy. Therefore, the data distribution strategy proposed in this article has obvious performance advantages and further promotion value.

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A research review on approaches/techniques used in big data environment

Yadav

Sharma

2016

2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC)

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RS-Pooling: an adaptive data distribution strategy for fault-tolerant and large-scale storage systems

Cited by 4 publications

References 20 publications

On the Efficient Delivery and Storage of IoT Data in Edge–Fog–Cloud Environments

On the Efficient Delivery and Storage of IoT Data in Edge–Fog–Cloud Environments

Load Balancing Cloud Storage Data Distribution Strategy of Internet of Things Terminal Nodes considering Access Cost

A research review on approaches/techniques used in big data environment

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