Implementation of Improved Inline Deduplication Scheme for Distributed Cloud Storage

Chavhan, Samiksha; Patil, Pragati; Patle, Gajanan

doi:10.1109/icces48766.2020.9137963

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…The field of duplication and its treatment methods is immense in the literature. It is addressed from different perspectives and domains, started from an overview of the duplication and techniques [21]- [23], improving the detection techniques [24], [25], evaluating the impact of the duplicates [26], and proposing new frameworks [27], [28] and methods [29], [30] to effectively enhance the detection process. However, little research addresses the impact of duplicates on the analysis results.…”

Section: Related Workmentioning

confidence: 99%

Tracking The Sensitivity of The Learning Models Toward Exact and Near Duplicates

Gabr¹,

Helmy²,

Elzanfaly³

2022

IJACSA

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Most real-world datasets contaminated by quality issues have a severe effect on the analysis results. Duplication is one of the main quality issues that hinder these results. Different studies have tackled the duplication issue from different perspectives. However, revealing the sensitivity of supervised and unsupervised learning models under the existence of different types of duplicates, deterministic and probabilistic, is not broadly addressed. Furthermore, a simple metric is used to estimate the ratio of both types of duplicates regardless of the probability by which the record is considered duplicate. In this paper, the sensitivity of five classifiers and four clustering algorithms toward deterministic and probabilistic duplicates with different ratios (0% -15%) is tracked. Five evaluation metrics are used to accurately track the changes in the sensitivity of each learning model, MCC, F1-Score, Accuracy, Average Silhouette Coefficient, and DUNN Index. Also, a metric to measure the ratio of probabilistic duplicates within a dataset is introduced. The results revealed the effectiveness of the proposed metric that reflects the ratio of probabilistic duplicates within the dataset. All learning models, classification, and clustering models are differently sensitive to the existence of duplicates. RF and Kmeans are positively affected by the existence of duplicates which means that their performce increase as the percentage of duplicates increases. Furthermore, the rest of classifiers and clustering algorithms are sensitive toward duplicates existence, especially within high percentage that negatively affect their performance.

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Section: Related Workmentioning

confidence: 99%

Tracking The Sensitivity of The Learning Models Toward Exact and Near Duplicates

Gabr¹,

Helmy²,

Elzanfaly³

2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…The proposed model in [ 70 ] uses two level of deduplication: cross-user deduplication at enterprise-level and cross-enterprise deduplication which checks uploaded data by different enterprises. Basically, there are three major camps of deduplication, i.e., Storage, Data Distribution, and Network Communication [ 72 , 73 ]. The model in [ 74 ] uses deduplication mechanism for the validation of encrypted images stored on cloud storage.…”

Section: Mechanismsmentioning

confidence: 99%

A Systematic Review on Cloud Storage Mechanisms Concerning e-Healthcare Systems

Tahir

Chen

Khan

et al. 2020

Sensors

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As the expenses of medical care administrations rise and medical services experts are becoming rare, it is up to medical services organizations and institutes to consider the implementation of medical Health Information Technology (HIT) innovation frameworks. HIT permits health associations to smooth out their considerable cycles and offer types of assistance in a more productive and financially savvy way. With the rise of Cloud Storage Computing (CSC), an enormous number of associations and undertakings have moved their healthcare data sources to distributed storage. As the information can be mentioned whenever universally, the accessibility of information becomes an urgent need. Nonetheless, outages in cloud storage essentially influence the accessibility level. Like the other basic variables of cloud storage (e.g., reliability quality, performance, security, and protection), availability also directly impacts the data in cloud storage for e-Healthcare systems. In this paper, we systematically review cloud storage mechanisms concerning the healthcare environment. Additionally, in this paper, the state-of-the-art cloud storage mechanisms are critically reviewed for e-Healthcare systems based on their characteristics. In short, this paper summarizes existing literature based on cloud storage and its impact on healthcare, and it likewise helps researchers, medical specialists, and organizations with a solid foundation for future studies in the healthcare environment.

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“…In addition to reducing the amount of data, inline deduplication also helps prevent the duplication of data by comparing the current state of the data with the incoming ones. This method is different from post processing deduplication, which only takes out the redundant data after the data has been written [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Inline Deduplication Checking in cloud environment

Joshi¹

2021

MSEA

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Inline deduplication is a technique that can reduce the amount of storage space used in a cloud environment. It can also improve restore and backup times. However, it is not always effective due to the various factors that affect the effectiveness of this process. For instance, the type of data that is stored and the method that is used to perform deduplication are all important factors that affect the effectiveness of this solution. The goal of this paper is to provide an overview of the various aspects of inline deduplication and its effectiveness in improving the performance and reducing the cost of storage in a cloud-based environment. We then conduct a case study to demonstrate the efficiency of this process in an Amazon Web Services instance. Experiments were able to demonstrate that in cloud environments, the level of deduplication that is achieved can improve performance and reduce the amount of storage space that is used. In case study, the use of inline deduplication on Amazon Web Services led to a 60% reduction in the storage space usage. After conducting a comprehensive analysis of the various aspects of inline deduplication, we concluded that it is a standard feature that can be used by cloud storage providers to reduce their costs and improve the performance of their customers.

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Implementation of Improved Inline Deduplication Scheme for Distributed Cloud Storage

Cited by 5 publications

References 7 publications

Tracking The Sensitivity of The Learning Models Toward Exact and Near Duplicates

Tracking The Sensitivity of The Learning Models Toward Exact and Near Duplicates

A Systematic Review on Cloud Storage Mechanisms Concerning e-Healthcare Systems

Inline Deduplication Checking in cloud environment

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