Sector: A high performance wide area community data storage and sharing system

Gu, Yunhong; Grossman, Robert L.

doi:10.1016/j.future.2009.05.009

Cited by 20 publications

(8 citation statements)

References 9 publications

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“…The responsibility of data server is to provide data to client. For example, Sector [35] proposed a data storage in optical networks. It is helpful for transmitting scientific datasets securely using s simple API.…”

Section: Data Servermentioning

confidence: 99%

A Survey on Large Scale Metadata Server for Big Data Storage

Patgiri¹,

Nayak²

2020

Preprint

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Big Data is defined as high volume of variety of data with an exponential data growth rate. Data are amalgamated to generate revenue, which results a large data silo. Data are the oils of modern IT industries. Therefore, the data are growing at an exponential pace. The access mechanism of these data silos are defined by metadata. The metadata are decoupled from data server for various beneficial reasons. For instance, ease of maintenance. The metadata are stored in metadata server (MDS). Therefore, the study on the MDS is mandatory in designing of a large scale storage system. The MDS requires many parameters to augment with its architecture. The architecture of MDS depends on the demand of the storage systemâĂŹs requirements. Thus, MDS is categorized in various ways depending on the underlying architecture and design methodology. The article surveys on the various kinds of MDS architecture, designs, and methodologies. This article emphasizes on clustered MDS (cMDS) and the reports are prepared based on a) Bloom filter-based MDS, b) Client-funded MDS, c) Geo-aware MDS, d) Cache-aware MDS, e) Load-aware MDS, f) Hash-based MDS, and g) Tree-based MDS. Additionally, the article presents the issues and challenges of MDS for mammoth sized data.

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Section: Data Servermentioning

confidence: 99%

A Survey on Large Scale Metadata Server for Big Data Storage

Patgiri¹,

Nayak²

2020

Preprint

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“…The paper also presents a formal security analysis of the protocol. Yunhong gu et al [8] proposed Sector which enables users to work with large datasets stored over multiple distributed nodes as if the files were on their local disk. Users do not need to locate data, manage data across multiple nodes, back up data, and manage the addition of new nodes or the deletion of existing nodes to the system.…”

Section: Literature Surveymentioning

confidence: 99%

TSDT: TPA Based Secure Data Transmission of Cloud Data Storage System

Dwivedi¹,

Kanungo²

2017

International Journal of Advanced Research in Computer and Comm

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Data integrity and storage efficiency are two important requirements for cloud storage. Cloud computing has emerged as a long-dreamt vision of the utility computing paradigm that provides reliable and resilient infrastructure for users to remotely store data and use on-demand applications and services. Currently, many individuals and organizations mitigate the burden of local data storage and reduce the maintenance cost by outsourcing data to the cloud. However, the outsourced data is not always trustworthy due to the loss of physical control and possession over the data. In this context a storage service provider is demonstrated who offers storage services to their clients additionally the intermediate service distributors are also demonstrated that re-distribute the service of primary service providers. In this research work we are presented a novel approach i.e. TPA based Secure Data Transmission (TSDT) for the cloud data security using successful data transmission. Due to this for securing the data on the cloud storage and unsecured network transmission the cryptographic solution is proposed. Our experimental results prove the effectiveness and efficiency of our mechanism when evaluating shared data integrity.

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“…Cassandra is of peer-to-peer (P2P) framework connects all nodes together. When information is written into data center A, a copy of the data is immediately backed up into a designated data center B, and each node can designate a permanent storage location in a rack [15] as show in Figure 3. This paper expands the application of a singlecluster replication mechanism to the replication of data center level.…”

Section: Remote Hbase and Cassandra Data Centers Backupmentioning

confidence: 99%

Empirical Analysis of High Efficient Remote Cloud Data Center Backup Using HBase and Cassandra

Chang

Tsai

Chen

et al. 2015

Scientific Programming

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HBase, a master-slave framework, and Cassandra, a peer-to-peer (P2P) framework, are the two most commonly used large-scale distributed NoSQL databases, especially applicable to the cloud computing with high flexibility and scalability and the ease of big data processing. Regarding storage structure, different structure adopts distinct backup strategy to reduce the risks of data loss. This paper aims to realize high efficient remote cloud data center backup using HBase and Cassandra, and in order to verify the high efficiency backup they have applied Thrift Java for cloud data center to take a stress test by performing strictly data read/write and remote database backup in the large amounts of data. Finally, in terms of the effectiveness-cost evaluation to assess the remote datacenter backup, a cost-performance ratio has been evaluated for several benchmark databases and the proposed ones. As a result, the proposed HBase approach outperforms the other databases.

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Sector: A high performance wide area community data storage and sharing system

Cited by 20 publications

References 9 publications

A Survey on Large Scale Metadata Server for Big Data Storage

A Survey on Large Scale Metadata Server for Big Data Storage

TSDT: TPA Based Secure Data Transmission of Cloud Data Storage System

Empirical Analysis of High Efficient Remote Cloud Data Center Backup Using HBase and Cassandra

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