InterCloud RAIDer: A Do-It-Yourself Multi-cloud Private Data Backup System

2014 ◽  
pp. 453-468 ◽  
Author(s):  
Chih Wei Ling ◽  
Anwitaman Datta
Keyword(s):  
Data Backup ◽  
Backup System ◽  
Private Data ◽  
Do It Yourself ◽  
Multi Cloud

DTAR: Deduplication TAR Scheme for Data Backup System

2011 ◽  
pp. 292-300
Author(s):  
Sung Woon Kang ◽  
Ho Min Jung ◽  
Jung Geun Lee ◽  
Jin Haeng Cho ◽  
Young Woong Ko
Keyword(s):  
Data Backup ◽  
Backup System

scholarly journals Data Backup and Recovery With a Minimum Replica Plan in a Multi-Cloud Environment

2020 ◽  
Vol 12 (2) ◽  
pp. 102-120
Author(s):  
Mohammad M. Alshammari ◽  
Ali A. Alwan ◽  
Azlin Nordin ◽  
Abedallah Zaid Abualkishik
Keyword(s):  
Cloud Storage ◽  
Denial Of Service ◽  
Data Recovery ◽  
Cloud Environment ◽  
Preventive Action ◽  
Data Reliability ◽  
High Data ◽  
Data Backup ◽  
Backup And Recovery ◽  
Multi Cloud

Cloud computing has become a desirable choice to store and share large amounts of data among several users. The two main concerns with cloud storage are data recovery and cost of storage. This article discusses the issue of data recovery in case of a disaster in a multi-cloud environment. This research proposes a preventive approach for data backup and recovery aiming at minimizing the number of replicas and ensuring high data reliability during disasters. This approach named Preventive Disaster Recovery Plan with Minimum Replica (PDRPMR) aims at reducing the number of replications in the cloud without compromising the data reliability. PDRPMR means preventive action checking of the availability of replicas and monitoring of denial of service attacks to maintain data reliability. Several experiments were conducted to evaluate the effectiveness of PDRPMR and the results demonstrated that the storage space used one-third to two-thirds compared to typical 3-replicas replication strategies.

scholarly journals Determination of Data Backup System Fault Tolerance

2019 ◽  
pp. 64-78
Author(s):  
A.M. Patutina ◽  
I.V. Rudakov
Keyword(s):  
Fault Tolerance ◽  
Data Storage ◽  
Markov Processes ◽  
Queuing Theory ◽  
Storage System ◽  
Data Backup ◽  
Backup System ◽  
Analytical Simulation ◽  
Storage Technologies ◽  
Semi Markov Processes

The paper considers the method of researching the fault tolerance of the backup system based on semi-Markov processes, and presents a quick overview of calculation methods for dynamic and static models of reliability analysis. The research shows that implementations based on semi-Markov processes have practically no limitations on building failure, recovery and backup models, and also provide the most accurate results. We described the existing storage technologies. By mixed, i.e., analytical-simulation, modeling, we implemented the data backup model. The simulation part, which determines the fault tolerance of switches and servers, is presented in terms of queuing theory, and the analytical part suggests defining fault tolerance for a data storage system based on semi-Markov processes.

Incremental local data backup system based on bacula

2018 ◽  
Author(s):  
Yang Chao ◽  
Xu Wen ◽  
Wu Guohui ◽  
Qi Xinge ◽  
Liu Sai ◽  
...  
Keyword(s):  
Local Data ◽  
Data Backup ◽  
Backup System

scholarly journals Data Backup and Recovery With a Minimum Replica Plan in a Multi-Cloud Environment

2021 ◽  
pp. 794-814
Author(s):  
Mohammad M. Alshammari ◽  
Ali A. Alwan ◽  
Azlin Nordin ◽  
Abedallah Zaid Abualkishik
Keyword(s):  
Denial Of Service ◽  
Data Recovery ◽  
Storage Space ◽  
Cloud Environment ◽  
Preventive Action ◽  
Data Reliability ◽  
High Data ◽  
Data Backup ◽  
Backup And Recovery ◽  
Multi Cloud

Cloud computing has become a desirable choice to store and share large amounts of data among several users. The two main concerns with cloud storage are data recovery and cost of storage. This article discusses the issue of data recovery in case of a disaster in a multi-cloud environment. This research proposes a preventive approach for data backup and recovery aiming at minimizing the number of replicas and ensuring high data reliability during disasters. This approach named Preventive Disaster Recovery Plan with Minimum Replica (PDRPMR) aims at reducing the number of replications in the cloud without compromising the data reliability. PDRPMR means preventive action checking of the availability of replicas and monitoring of denial of service attacks to maintain data reliability. Several experiments were conducted to evaluate the effectiveness of PDRPMR and the results demonstrated that the storage space used one-third to two-thirds compared to typical 3-replicas replication strategies.

scholarly journals Short-term forecasting models for automated data backup system: segmented regression analysis

2020 ◽  
Vol 42 ◽  
pp. e46073
Author(s):  
Leandro Duarte Pereira ◽  
Pedro Paulo Balestrassi ◽  
Vinicius de Carvalho Paes ◽  
Anderson Paulo de Paiva ◽  
Rogério Santana Peruchi ◽  
...  
Keyword(s):  
Linear Regression ◽  
Control Charts ◽  
Business Success ◽  
Short Term ◽  
Data Backup ◽  
Backup System ◽  
Logical Sequence ◽  
Forecast Models ◽  
Short Term Forecasting ◽  
Object Of Study

The Information and Communication Technology (ICT) becomes a critical area to business success; organizations need to adopt additional measures to ensure the availability of their services. However, such services are often not planned, analyzed and monitored, which impacts the assurance quality to customers. The backup is the service addressed in this study, with the object of study of the automated data backup systems in operation at the Federal University of Itajuba - Brazil. The main objective of this research was to present a logical sequence of steps to obtain short-term forecast models that estimate the point at which each recording media reaches its storage capacity limit. The input data was collected in the metadata generated by the backup system, with 2 years data window. For the implementation of the models, the simple univariate linear regression technique was employed in conjunction, in some cases, with the simple segmented linear regression. In order to discover the breakpoint, a targeted approach to residual analysis was applied. The results obtained by the iterative implementation of the proposed algorithm showed adherence to the characteristics of the analyzed series, with accuracy measures, regression significance, normality residual through control charts, model adjustment, among others. As a result, an algorithm was developed for integration into automated backup systems using the methodology described in this study.

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