HDFSX: Big data Distributed File System with small files support

Elkafrawy, Passent; Sauber, Amr M.; Hafez, M.

doi:10.1109/icenco.2016.7856457

Cited by 8 publications

(7 citation statements)

References 3 publications

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“…We provide the protection of data by accurate encryption. (7) Storage provider verification: storage providers allow people and organizations to buy space to store their data. e storage provider has to be verified or is untrusted.…”

Section: Analysis Of the Proposed Modelmentioning

confidence: 99%

A New Secure Model for Data Protection over Cloud Computing

Sauber

Elkafrawy

Shawish

et al. 2021

Computational Intelligence and Neuroscience

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The main goal of any data storage model on the cloud is accessing data in an easy way without risking its security. A security consideration is a major aspect in any cloud data storage model to provide safety and efficiency. In this paper, we propose a secure data protection model over the cloud. The proposed model presents a solution to some security issues of cloud such as data protection from any violations and protection from a fake authorized identity user, which adversely affects the security of the cloud. This paper includes multiple issues and challenges with cloud computing that impairs security and privacy of data. It presents the threats and attacks that affect data residing in the cloud. Our proposed model provides the benefits and effectiveness of security in cloud computing such as enhancement of the encryption of data in the cloud. It provides security and scalability of data sharing for users on the cloud computing. Our model achieves the security functions over cloud computing such as identification and authentication, authorization, and encryption. Also, this model protects the system from any fake data owner who enters malicious information that may destroy the main goal of cloud services. We develop the one-time password (OTP) as a logging technique and uploading technique to protect users and data owners from any fake unauthorized access to the cloud. We implement our model using a simulation of the model called Next Generation Secure Cloud Server (NG-Cloud). These results increase the security protection techniques for end user and data owner from fake user and fake data owner in the cloud.

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Section: Analysis Of the Proposed Modelmentioning

confidence: 99%

A New Secure Model for Data Protection over Cloud Computing

Sauber

Elkafrawy

Shawish

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…However, the capacity of the cache limits the number of small files stored in the system. Elkafrawy et al [11] present a new structure for HDFS (HDFSX) to avoid higher memory usage, flooding network, requests overhead and centralized point of failure. This method can reduce the load of MDS, but it did not consider the access related between files.…”

Section: A Changing the Storage And Management Mechanismmentioning

confidence: 99%

A Small File Merging Strategy for Spatiotemporal Data in Smart Health

2019

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With the rapid development of smart health, the health sensors and wearable devices bring huge amounts of small files of spatiotemporal data, which are distributed in different servers, and affect the I/O performance of the system seriously. There are many methods to solve the problems of a small file, but most of them are applied to specific applications. However, due to the influence of user access behavior and data type, these methods are not effective when applied to sensors data in smart health. In this paper, a novel small file merging strategy for smart health is proposed. By analyzing the features of health sensors data and the preferences of user access, the strategy uses spatiotemporal clustering for the historical user access information. Then, weights are applied to these clusters based on the access density to determine the access-related spatiotemporal range. Finally, the spatiotemporal range is used to merge small files. The experimental results show that the merging strategy is simple but efficient, and it can effectively reduce user access delay for small files of spatiotemporal data in smart health.INDEX TERMS Smart health, small file merging, spatiotemporal data, spatiotemporal clustering.

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“…MR provides applications to work on thousands of machines and petabytes of information in parallel and in a cost-effective manner. is is accomplished by using commodity hardware to build cluster nodes as core basic components of Hadoop [4][5][6][7]. A Hadoop Cluster consists of a single master node that hosts the JobTracker service and a set of workers each hosting a TaskTracker service.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hadoop Security Model for Addressing Malicious Collusive Workers

Sauber

Awad

Shawish

et al. 2021

Computational Intelligence and Neuroscience

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With the daily increase of data production and collection, Hadoop is a platform for processing big data on a distributed system. A master node globally manages running jobs, whereas worker nodes process partitions of the data locally. Hadoop uses MapReduce as an effective computing model. However, Hadoop experiences a high level of security vulnerability over hybrid and public clouds. Specially, several workers can fake results without actually processing their portions of the data. Several redundancy-based approaches have been proposed to counteract this risk. A replication mechanism is used to duplicate all or some of the tasks over multiple workers (nodes). A drawback of such approaches is that they generate a high overhead over the cluster. Additionally, malicious workers can behave well for a long period of time and attack later. This paper presents a novel model to enhance the security of the cloud environment against untrusted workers. A new component called malicious workers’ trap (MWT) is developed to run on the master node to detect malicious (noncollusive and collusive) workers as they convert and attack the system. An implementation to test the proposed model and to analyze the performance of the system shows that the proposed model can accurately detect malicious workers with minor processing overhead compared to vanilla MapReduce and Verifiable MapReduce (V-MR) model [1]. In addition, MWT maintains a balance between the security and usability of the Hadoop cluster.

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HDFSX: Big data Distributed File System with small files support

Cited by 8 publications

References 3 publications

A New Secure Model for Data Protection over Cloud Computing

A New Secure Model for Data Protection over Cloud Computing

A Small File Merging Strategy for Spatiotemporal Data in Smart Health

A Novel Hadoop Security Model for Addressing Malicious Collusive Workers

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