An Efficient Data Segmentation and Replication Technique for Cloud using Fuzzy Centrality Measures

Cloud computing is a creating worldview to give reliable and resilient infrastructure permitting the clients (data proprietors) to store their data and the data purchasers (clients) can get to the data from cloud servers. This worldview decreases storage and maintenance cost of the data proprietor. Notwithstanding, cloud data storage still offers ascend to security related issues. In the event of shared data, the data face both cloud-explicit and insider threats. In this work, we propose fuzzy centrality measure based division and replication of data in the cloud for perfect execution and security that consider both security and execution issues. In our framework, we separate a data records and imitate the isolated data over the cloud center points utilizing fuzzy centrality measures. Every one of the nodes stores just a solitary data fragment of a particular data document that guarantees that even if there should arise an occurrence of a fruitful attack, no significant information is uncovered to the attacker. In addition, the cloud nodes storing the data fragments, are separated with certain distance by methods for altered fuzzy T-coloring to prohibit an attacker of predicting the locations of the fragments. We likewise contrast the exhibition of the our methodology and other standard replication plans. The greater amount of security with improved performance was observed.

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An Efficient Data Segmentation and Replication Technique for Cloud using Fruit Fly Optimized Centrality Measures

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f1104.0486s419 ◽

2019 ◽

Vol 8 (6S4) ◽

pp. 497-501

Keyword(s):

Data Storage ◽

Fruit Fly ◽

Centrality Measures ◽

Fruit Fly Optimization Algorithm ◽

Cloud Data ◽

Fruit Fly Optimization ◽

Cloud Data Storage ◽

And Performance ◽

Replication Technique ◽

Cloud Servers

Cloud computing is a creating worldview to give dependable and versatile framework permitting the clients (data proprietors) to store their data and the data customers (clients) can get to the data from cloud servers. This paradigm decreases stockpiling and upkeep cost of the data proprietor. However, cloud data storage still gives rise to security related problems. In case of shared data, the data face both cloud-specific and insider threats. In this work, we propose FOA( fruit fly optimization algorithm ) optimized centrality measure fragmentation and replication of information in the cloud for optimum performance and security that consider both security and performance issues. FOA is a technique for deducing global optimization based on the foraging character of the fruit fly. The sensory perception of the fruit fly is superior than that of other species, particularly the sense of smell and vision . In our methodology, we divide a data files and replicate the fragmented data over the cloud nodes using FOA centrality measures. Every one of the cloud node just store a single information data fragment that ensures even if there arise an occurrence of a successful attack ,no important information is shown to the attacker .We also compare the performance of the our methodology with other standard replication schemes. Observed results shows higher level of security and performance imrpovements.

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Increasing reliability and fault tolerance of a secure distributed cloud storage

10.47350/iccs-de.2020.16 ◽

2020 ◽

Author(s):

N. Kucherov ◽

M. Babenko ◽

A. Tchernykh ◽

V. Kuchukov ◽

I. Vashchenko

Keyword(s):

Fault Tolerance ◽

Data Storage ◽

Storage System ◽

Number System ◽

Modular Arithmetic ◽

Cloud Data ◽

Cloud Data Storage ◽

Data Storage System ◽

Set Up ◽

Cloud Servers

The work develops the architecture of a multi-cloud data storage system based on the principles of modular arithmetic. This modication of the data storage system allows increasing reliability of data storage and fault tolerance of the cloud system. To increase fault-tolerance, adaptive data redistribution between available servers is applied. This is possible thanks to the introduction of additional redundancy. This model allows you to restore stored data in case of failure of one or more cloud servers. It is shown how the proposed scheme will enable you to set up reliability, redundancy, and reduce overhead costs for data storage by adapting the parameters of the residual number system.

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Enhanced Data Security from Single Clouds to Multiclouds

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i9.87 ◽

2018 ◽

Vol 6 (9) ◽

pp. 66

Author(s):

Umesh Banodha ◽

Praveen Kumar Kataria

Keyword(s):

Data Storage ◽

Network Connectivity ◽

Data Replication ◽

Recovery Process ◽

Cloud Environment ◽

Storage Security ◽

Cloud Data ◽

Replication Strategy ◽

Cloud Data Storage ◽

Data Files

Cloud is an emerging technology that stores the necessary data and electronic form of data is produced in gigantic quantity. It is vital to maintain the efficacy of this data the need of data recovery services is highly essential. Cloud computing is anticipated as the vital foundation for the creation of IT enterprise and it is an impeccable solution to move databases and application software to big data centers where managing data and services is not completely reliable. Our focus will be on the cloud data storage security which is a vital feature when it comes to giving quality service. It should also be noted that cloud environment comprises of extremely dynamic and heterogeneous environment and because of high scale physical data and resources, the failure of data centre nodes is completely normal.Therefore, cloud environment needs effective adaptive management of data replication to handle the indispensable characteristic of the cloud environment. Disaster recovery using cloud resources is an attractive approach and data replication strategy which attentively helps to choose the data files for replication and the strategy proposed tells dynamically about the number of replicas and effective data nodes for replication. Thus, the objective of future algorithm is useful to help users together the information from a remote location where network connectivity is absent and secondly to recover files in case it gets deleted or wrecked because of any reason. Even, time oriented problems are getting resolved so in less time recovery process is executed.

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A Group Collaboratable Proof of Retrievability Scheme for Cloud Data Storage

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v13i7.2501 ◽

2014 ◽

Vol 13 (7) ◽

pp. 4625-4632

Author(s):

Jyh-Shyan Lin ◽

Kuo-Hsiung Liao ◽

Chao-Hsing Hsu

Keyword(s):

Cloud Computing ◽

Data Storage ◽

Cloud Service ◽

Third Party ◽

Group Collaboration ◽

Cloud Data ◽

Cloud Data Storage ◽

Important Trend ◽

Data Update ◽

Proof Of Retrievability

Cloud computing and cloud data storage have become important applications on the Internet. An important trend in cloud computing and cloud data storage is group collaboration since it is a great inducement for an entity to use a cloud service, especially for an international enterprise. In this paper we propose a cloud data storage scheme with some protocols to support group collaboration. A group of users can operate on a set of data collaboratively with dynamic data update supported. Every member of the group can access, update and verify the data independently. The verification can also be authorized to a third-party auditor for convenience.

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