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Data deduplication, though being efficient for redundancy elimination in storage systems, introduces chunk fragmentation which severely decreases restore performance. Rewriting algorithms are proposed to reduce the chunk fragmentation. Typically, the backup software aggregates files into larger “tar” type files for storage. We observe that, in tar type datasets, a large number of Persistent Fragmented Chunks (PFCs) are repeatedly rewritten by state-of-the-art rewriting algorithms in every backup, which severely impacts restore performance. We found that the existence of PFCs is due to the traditional strategy of storing PFCs along with other chunks in the containers to preserve the stream locality, rendering them always stored in the containers with low utilization. We propose DePFC to reduce PFCs. DePFC identifies and removes PFCs from the containers preserving the stream locality, and groups them together, to increase the utilization of containers holding them for the subsequent backup, thus preventing them from being rewritten again. We further propose an FC Buffer to avoid mistaken rewrites of PFCs and grouping PFCs that cause restore cache thrashing together. Experimental results demonstrate that DePFC improves restore performance of state-of-the-art rewriting algorithms by 44.24-89.42 percent, while attaining comparable deduplication efficiency, and FC Buffer further improves restore performance.

Large numbers of small organisations and prosumers have shifted away from managing data on their own devices and are now heavily reliant on service-providers for both storage and processing of their data. Most such entities are also dependent on those service-providers to perform backups and enable data recovery. Prior work defining users' backup needs was applied to this context in order to establish specifications for appropriate backup arrangements. A sample of service-providers was assessed against those specifications. Their backup and recovery mechanisms were found to fall seriously short of the need.

For the purpose of ensuring data security, traditional systems have widely used redundancy backup to store multiple copies of data. Multiple copies technology has high reliability, but also has the disadvantage of high redundancy storage and low space utilization. On the contrary, EC (Erasure Coding) technology has a high utilization rate of storage space, but the overhead of coding, decoding and data reconstruction is great. So, this paper demonstrates a data backup method based on XOR checksum being suitable for storing hot temporary data, which first splits the data into two parts and then performs the XOR operation of the two parts to generate another part of the data. Finally, the XOR checksum stores the three data parts into different nodes. The checksum not only ensures the security of data but also saves the storage space, thus improving the performance of reading and writing. This strategy achieves a mutual backup between the three nodes in order to ensure data security. Because there is only one copy of original data in the system, this model resolves the data inconsistency problem reasonably and simplifies the data version control existing in the redundancy backup model. Actual data test results show that, compared with the current mainstream Cassandra redundant backup model, the performance of a data backup model based on the XOR checksum proposed and implemented in this paper has been significantly improved: the reading performance improves by an average of 10%, and the writing performance improves by an average of 30%.

With emerging “pay-as-you-go object storage” services, file system backup to object storage is an attractive option in terms of total cost of ownership. However, if a failure or a disaster occurs, a conventional backup system with object storage requires a long time to recover the necessary data since it must restore the entire directory structure of the file system from object storage before accessing the data. In this paper, a new backup method, which achieves on-demand restore as part of file system backup to object storage, is proposed. On-demand restore is a function that restores only a certain directory or file according to the request from an end user. The proposed method backs up relationships between a file and an object on a directory basis in order to efficiently handle the relationships in the restore. It is experimentally shown that the proposed method reduces the response time of file access involving restore by over an order of magnitude.

Industrial work environments have been focusing on employing robots within their work area for their efficiency in tedious jobs and their reliability and low chances of malfunction. However, this does not entirely eliminate the role of the human workers since some jobs need human reasoning or do not worth employing an expensive robot to do. This made the interaction between a human and a robot a persistent safety issue since there have been so many registered accidents where the robot was the main contributor to them. The international standardization organization (ISO) has set some safety procedures to be followed in areas with human-robot interaction (HRI), which should be adopted by every work environment where this interaction exists. This research explains how to adopt this standard in building a software-hardware integrated system that assures the safety and the well-being of the human workers in an HRI environment, without affecting the workflow or the processing speed of the production in the factory. The system is composed of two main parts: main software, and backup software, where an Artificial Intelligence module is embedded to make critical decisions according to the set of sensors’ readings. This integration ensures the safety of the human workers' insight within ISO risk assessment standards.

Backup systems are vital for ensuring data life and usability. Most modern backup software is still laborious to configure, takes a lot of effort to continuously monitor and adjust, and is excessively prone to failure due to insufficient resources or due to hardware problems. This paper presents strategies driving an adaptive backup system that is able to auto-configure, self-monitor, and adapt itself to various conditions, with little human intervention and with very simple initial setup. The main discussion is around the hierarchical structure of the balancing problems that must be solved by the backup system, such that it can adapt to the changing work conditions. Authors propose the Dual Standard Model for automated balancing decisions, which has a striking similarity with human behavior. The software is used in production on a large network containing 1k+ Linux desktops, servers, and clusters at Carnegie Mellon University.

Mainframe systems backup and data archival is performed via proprietary data movement protocols to FICON-attached tape devices or virtual tape libraries using specialized backup software running on the mainframe operating system. These proprietary tape storage systems do not offer support for TCP/IP-attached storage solutions and for the standard protocols used to access these systems. The mainframe backup and archive software is tightly coupled with the tape architecture and by design writes data sets as a sequential, serial stream of data to tape. As the amounts of data in mainframe systems keep growing, the resource intensive backup and archive process become more expensive. The team developed software-defined patented[1] technology that enables backup, archive and restore of mainframe systems directly to TCP/IP-attached object storage systems. Moreover, the technology provides relief in CPU resource utilization by offloading the backup archive and restore processes from standard Central Processors (CP) to zIIP-processors [2]. The software engine invokes the built-in operating system data backup program, DFDSS, to read data sets from disk. DFDSS is invoked using multiple call-back functions that receive control during its operation. The call-back functions are used to receive mainframe disk data as it is read by DFDSS and move it over a shared memory buffer to the software engine. The software engine implements, among others, an S3-compatible client, that can write the received mainframe data directly to object storage systems over TCP/IP. The engine buffers data received from DFDSS in chunks. When a chunk is filled, it is taken by the next available thread to be sent to the object storage. Before sending, the thread may compress and encrypt the chunk data. Each chunk is written as its own object in the object storage. Multiple chunks may be handled and sent in parallel, improving performance over serial write to tape. Finally, a mapping object is written that includes an index of all chunks comprising a backed up mainframe data set. Metadata is assigned to the mapping object to document the logical attributes of the data set such as its block size and record format. This metadata is used during restore to reconstruct the data set. The metadata may also be searched or queried by the object storage management interface. Mapping objects follow a naming convention that allows locating them using direct access upon a restore request, without the need for searching or querying the object storage. This also saves the need to maintain an additional backup catalog on the mainframe side.

Distributed storage systems, built on peer-to-peer networks, can provide large-scale data storage and high data reliability by redundancy. Data backup is the process to store data into a set of redundant storage nodes. Rapid completion of such a process is very critical to maintain system performance. In traditional data backup in distributed systems based on erasure codes, star-structured scheme is used, in which each redundant block is just sent to each target storage node from the source node directly, so the storage throughput and delay are limited by the bottleneck bandwidth, due to bandwidth heterogeneity. The recent “in-network” redundancy generation scheme uses locally repairable property of self-repairing codes to speed up data backup. However, such kind of code does not own maximum distance separable property, thus does not achieve optimal storage efficiency. We still lack a fast backup scheme in distributed systems based on general erasure coding. To this end, we proposed that instead of only focusing on bandwidths between the source node and target nodes, the bandwidths between target storage nodes should be fully taken into account. In our scheme, each redundant data block is divided into some parts according to different proportions and each part of the block is sent to the target storage node via other different storage nodes. The benefit is that spare bandwidths between target storage nodes are used to reduce backup time. We further show how this process can be modeled and derive a formula about the final backup time. We can achieve minimum backup time by solution for classical quadratic programming problem. We conduct both numerical analysis and experimental study. Our experiments shows, the delay reduces 59 %, compared with common star-structured scheme. Meanwhile, the throughput is increased significantly in backup process.

Thus far, you’ve learned about setting up new workloads and migrating existing ones. You’ve learned methods to make your VMs resilient to failure and about configuration and troubleshooting methods. There is another handy feature that is baked into Hyper-V, namely, Hyper-V Replica. While failover clustering does a fantastic job of keeping your workloads running, by using the built-in high-availability features of Microsoft clustering, what if you’re in a situation in which you don’t have a failover cluster? Maybe you’re at a location with a single host and no cluster, but you still want some level of built-in redundancy. What if something were to happen to your main production location? In that situation, you’d likely have to rely on your backup software, to get back up and running, but that can take some time, depending on the vendor. These are situations in which Hyper-V Replica can help.

P2P architecture is a viable option for enterprise backup. In contrast to dedicated backup servers, nowadays, a standard solution, making backups directly on organization's workstations should be cheaper as existing hardware is used, more efficient as there is no single bottleneck server, and more reliable as the machines can be geographically dispersed. We present an architecture of a P2P backup system that uses pairwise replication contracts between a data owner and a replicator. In contrast to a standard P2P storage system using directly a distributed hash table DHT, the contracts allow our system to optimize replicas' placement depending on a specific optimization strategy and so to take advantage of the heterogeneity of the machines and the network. Such optimization is particularly appealing in the context of backup: replicas can be geographically dispersed, the load sent over the network can be minimized, or the optimization goal can be to minimize the backup/restore time. However, managing the contracts, keeping them consistent and adjusting them in response to dynamically changing environment is challenging. We built a scientific prototype and ran experiments on 150 workstations in our university's computer laboratories and, separately, on 50 PlanetLab nodes. We found out that the main factor affecting the performance of the system is the availability of the machines. Yet, our main conclusion is that it is possible to build an efficient and reliable backup system on highly unavailable machines, as our computers had just 13% average availability. Copyright © 2015John Wiley & Sons, Ltd.

Hard disks are employed as the primary storage device for consumer electronic products. But the data on hard disks may be lost due to error operations, malicious code attacks and software conflictions. As data backup and recovery on hard disks move to ever more challenging areas, improving the security level of backup data, lowering overhead to the storage system and reducing the fragments created by the backup operation become increasingly important. Therefore, a novel Host Protected Area Virtual File System prototype, which is called as HVF, is presented in this paper. In order to boost security of data store locations, backup data or protected data are saved in Host Protected Area by the storage filter driver. The method of creating bitmap indexes for hard disks and the mechanism of the mapping relationship are proposed in order to reduce the effects to the operating system and cut down the fragments produced by a large number of I/O operations. The simulation results indicate that the proposed HVF has higher security, less overhead and fragments to dominating operating systems.

Secure disks protect user data by applying security features such as data access prevention through access control, user identification through authentication, and data leakage prevention through encryption and decryption. However, data exposure can still be caused by security threats such as malicious users bypassing authentication and the exposure of encryption and decryption keys. Therefore, we analyze a potential vulnerability in the backup feature of product A and draw practical and empirical results to verify the analyzed vulnerability by implementing a proof-of-concept tool.

The importance of frequent backups is uncontroversial. Their creation is simpler than ever today thanks to widespread availability of cheap cloud storage. Common backup solutions, however, tend to be either insecure, inflexible or inefficient in typical backup scenarios.,,,,In this paper, we present triviback, a lightweight and almost trivial, yet powerful solution for outsourcing backups to untrusted cloud storage. Based on recent research results on secure data deduplication, triviback combines strong confidentiality, authenticity and availability guarantees with flexibility and efficiency in terms of low storage and communication costs: Triviback supports efficient preservation of many (overlapping) backup states with storage costs comparable to (insecure) state-of-the-art version control systems (VCS)—while supporting full storage reclamation on deletion of arbitrary backup states.,,,,We prove the security of our contribution, publish a ready-to-use implementation and perform an extensive evaluation of storage and communication costs.

Among the schemes proposed for failure recovery in software-defined networks, installing backup paths in advance is considered to be an effective approach to reduce the recovery latency. However, the pre-installation poses undue storage overheads on flow tables. In this paper, we propose a customized and cost-efficient backup scheme, which achieves fast recovery from any single-link failure. We introduce an improved breadth first search algorithm to construct the customized backup paths of flows to accommodate their diverse routing demands. By analyzing the path characteristics carefully, we observe that non-conflicted backup paths can be aggregated with each other to reduce the number of backup rules. We formulate this backup path aggregation as an optimization problem. The challenge is how to aggregate the backup paths without causing routing ambiguity. To this end, we design a two-stage aggregation (2SA) algorithm. At its core, 2SA leverages conflict matrixes to guarantee the correctness of aggregation. We evaluate our scheme comprehensively with both the real-world topologies and generated topologies. Simulation results show that our scheme can pre-install the customized backup paths with much fewer, typically one order of magnitude, backup rules compared to the traditional flow-based protection without aggregation.

Cloud-Storage has become part of the standard toolkit for enterprise-grade computing. While being cost- and energy-efficient, cloud storage's availability and data confidentiality can be problematic. A common approach of mitigating those issues are cloud-of-cloud solutions. Another challenge is the integration of such a solution into existing legacy systems. This paper introduces the Archistar Backup Proxy which allows integration of multi-cloud storage into existing legacy enterprise computing landscapes by overloading the industry-standard Amazon S3 protocol. The paper provides multiple lessons-learned during implementation and concludes with a performance evaluation with traditional backup solutions utilizing redundant remote storage.

The maturity of data backup and recovery technology ensures the reliability and availability of the data. But only to ensure that the reliability and availability cannot meet the security needs of data backup in important areas of military, national defense and other important areas. In this paper, we design and implement a security enhanced data backup and recovery system. Using the existing backup and recovery technology, through the authentication based on digital certificate, the security access control based on different roles, process control based on the business model and the audit method based on the operation logs, the system realizes the function to detect the legitimacy of the data to be backed up, operators and the data to be recovered so the system can effectively promote the confidentiality by preventing the operators backing up data to illegal destination and restoring data to illegal target machine.

This chapter covers backup, saving a copy of Kodi in case something goes wrong and you need to step back, and the update process for newly released versions. The two topics belong together

Traditionally File Servers are used as storage medium as Network Attached Storage. However the need for backup servers is a necessity. As a rule of thumb, data in a network environment needs to be replicated on 3 different machines. The amount of data replicated and other issues such as consistency and concurrency maintenance becomes a huge overhead. As a solution to those issues, Erasure Coded Storage is tipped to be the next best alternative. In Erasure Coded Storage, an object (file) is broken down into n blocks, and encoded. Furthermore some redundant parity blocks are created using mathematical formulas, so that redundancy could be provided. In case of loss of original blocks of data, any parity block can be used for recovery. There exist several Erasure Code Techniques, namely Reed-Solomon, Hierarchical, Self-Repairing and Regenerating codes. Each code has its specificity, some look into diminishing bandwidth consumption, while other lesser computational loads. Till date, there are only a few implementations based on erasure codes. Our contribution is that we are proposing a novel architecture for Network Backup System using Erasure Codes in ECNBS that includes three layers. The Interface Layer presents the user with a layout of files similar to windows environment, whereby files are stored in folders and subfolders. The Intermediate Layer (Mapping Layer) stores information about the files and the locations of the related blocks. The Storage Layer is where blocks of data are physically stored. The newly implemented system is fully functional, and has also been compared to the traditionally used File Server.

With the development of data backup technology, there have been a variety of current disaster recovery system model. This paper has chosen three data backup and recovery system to introduce and analyze according to the current basic data backup and recovery system model, designed to make the reader have a clear and thorough understanding of the current disaster recovery system that has already established.

With the development of Internet technology and the increasing popularity of the Internet, the amount of information data of enterprises increased sharply. How to avoid unexpected data corruption and to improve the system of data security and data recovery capabilities has been the focus of attention of users and enterprises. Regular backups of the database is to restore the data the easiest and most effective guarantee method and is an effective measure database administrators to manage data. This paper proposes a plan of database scheduled tasks based on Linux environment. This plan can be timed to export the database, be sent automatically to a secure backup mailbox and complete the regular backup of the database finally.

Computer data has become one of the most valuable assets that individuals, organizations and enterprises own today. The majority of people agree that losing their data (programs, data sets, documentation files, email addresses, photos, customer data, etc.) would be a disaster. The reason most individuals avoid performing data backups though, is because they feel the process is complicated, tedious and expensive. This is not always true. In fact, with the right tool, it’s very easy and affordable. In this paper we compare the performance and system resources usage of five remote incremental backup open source tools for Linux: Rsync, Rdiff-backup, Duplicity, Areca and Link-Backup. These tools are tested using three distinct remote backup operations: full backup, incremental backup and data restoration. The advantages of each tool are described and we select the most efficient backup tool for simple replication operations and the overall best backup tool.

HDFS is designed to store massive scale data on commodity hardware reliably. However, it is still vulnerable to severe site disaster, eg. fire, earthquake. The off-site file system backup is an important strategy for data retention. In this paper, we present an efficient, easy-to-use off-site backup and recovery system for HDFS. The system includes a client based on HDFS v2.3.0 with additional feature of off-site backup, and a remote server with a HDFS cluster to keep the backup data. It supports full backup and regularly incremental backup. Both the metadata alteration and the data blocks alteration will be recorded and transferred to remote backup server. Several techniques are used to improve efficiency and throughput. In our experiment, the average speed of backup is up to 95 MB/s, approaching the theoretical maximum speed of gigabit Ethernet.

HDFS has been widely used for storing massive scale data which is vulnerable to site disaster. The file system backup is an important strategy for data retention. In this paper, we present an efficient, easy- to-use Backup and Disaster Recovery System for HDFS. The system includes a client based on HDFS with additional feature of remote backup, and a remote server with a HDFS cluster to keep the backup data. It supports full backup and regularly incremental backup to the server with very low cost and high throughout. In our experiment, the average speed of backup and recovery is up to 95 MB/s, approaching the theoretical maximum speed of gigabit Ethernet.

Reduce backup window with lower system overheads is a challenge for backup system in massive small files environment. In this article, a new autoregressive-based online backup scheme for small files (ABBSF) is presented to achieve the goal by enhancing the utilization ratio of bandwidth resource in normal times. ABBSF exploits single transfer pattern and package transfer pattern to deal with small files. ABBSF employs single transfer to send one small file to the backup side periodically, and collects the network situation in this procedure. According to these network performance information, it builds autoregressive-based model for predicting available network bandwidth in the next period. Meanwhile, it provides predictive step length control to make more utilization of predictive model for maintaining low system overheads. On the basis of prediction, ABBSF makes appropriate transfer policy for package transfer. Finally, it employs package transfer to pack several files and sent them to the backup side. Experiments show that ABBSF has a good performance of prediction with relatively low system overheads to make full use of network bandwidth when backup small files.

Cloud backup has been an important issue ever since large quantities of valuable data have been stored on the personal computing devices. Data reduction techniques, such as deduplication, delta encoding, and Lempel-Ziv (LZ) compression, performed at the client side before data transfer can help ease cloud backup by saving network bandwidth and reducing cloud storage space. However, client-side data reduction in cloud backup services faces efficiency and privacy challenges. In this paper, we present Pangolin, a secure and efficient cloud backup service for personal data storage by exploiting application awareness. It can speedup backup operations by application-aware client-side data reduction technique, and mitigate data security risks by integrating selective encryption into data reduction for sensitive applications. Our experimental evaluation, based on a prototype implementation, shows that our scheme can improve data reduction efficiency over the state-of-the-art methods by shortening the backup window size to 33%~75%, and its security mechanism for sensitive applications has negligible impact on backup window size.

In this paper, we present YuruBackup, a space-efficient and highly scalable incremental backup system in the cloud. YuruBackup enables fine-grained data de-duplication with hierarchical partitioning to improve space efficiency to reduce bandwidth of both backup and restore processes, and storage costs. On the other hand, YuruBackup explores a highly scalable architecture for fingerprint servers that allows to add one or more fingerprint servers dynamically to cope with increasing clients. In this architecture, the fingerprint servers in a DB cluster are used for scaling writes of fingerprint catalog, while the slaves are used for scaling reads of fingerprint catalog. We present the system architecture of YuruBackup and its components, and we have implemented a proof-of-concept prototype of YuruBackup. By conducting performance evaluation in a public cloud, experimental results demonstrate the efficiency of the system.

Globally accessed databases are having massive number of transactions and database size also been increased as MBs and GBs on daily basis. Handling huge volume of data’s in real time environment are big challenges in the world. Global domain providers like banking, insurance, finance, railway, and many other government sectors are having massive number of transactions. Generally global providers are used to take the backup of their sensitive data’s multiple times in a day, also many of the providers used to take the data backup once in few hours as well. Implementing backup often for huge size of databases without losing single bit of data is a susceptible problem and challengeable task. This research addresses the difficulties of the above problems and provides the solution to how to optimize and enhance the process for compress the real time database and implement backup of the database in multiple devices using parallel way. Our proposed efficient algorithms provides the solution to compress the real time databases more effectively and improve the speed of backup and restore operations.

As various features of the smartphone have been used, a lot of information have been stored in the smartphone, including the user`s personal information. However, a frequent update of the operating system and applications may cause a loss of data and a risk of missing important personal data. Thus, the importance of data backup is significantly increasing. Many users employ the backup feature to store their data securely. However, in the point of forensic view these backup files are considered as important objects for investigation when issued hiding of smartphone or intentional deletion on data of smartphone. Therefore, in this paper we propose a scheme that analyze structure and restore data for Kies backup files of Samsung smartphone which has the highest share of the smartphone in the world. As the experimental results, the suggested scheme shows that the various types of files are analyzed and extracted from those backup files compared to other tools.

Due to the relatively low bandwidth of WAN that supports cloud backup services and the increasing amount of backed-up data stored at service providers, the deduplication scheme used in the cloud backup environment must remove the redundant data for backup operations to reduce backup times and storage costs and for restore operations to reduce restore times. In this paper, we propose SAFE, a source deduplication framework for efficient cloud backup and restore operations. SAFE consists of three salient features, (1) Hybrid Deduplication, combining the global file-level and local chunk-level deduplication to achieve an optimal tradeoff between the deduplication efficiency and overhead to achieve a short backup time; (2) Semantic-aware Elimination, exploiting file semantics to narrow the search space for the redundant data in hybrid deduplication process to reduce the deduplication overhead; and (3) Unmodified Data Removal, removing the files and data chunks that are kept intact from data transmission for some restore operations. Through extensive experiments driven by real-world datasets, the SAFE framework is shown to maintain a much higher deduplication efficiency/overhead ratio than existing solutions, shortening the backup time by an average of 38.7 %, and reduce the restore time by a ratio of up to 9.7 : 1.

Recent developments in grid and cloud computing technologies have enhanced the performance and scale of storage media. Data management and backup are becoming increasingly important in these environments. Backup systems constitute an important component of operating system security. However, it is difficult to recover backup data from an environment where the operating system does not work because the storage hardware has been damaged. This study analyzes the Volume Shadow Copy Service (VSS) used by the Windows operating system. Windows 8 has been implemented for mobile environments; hence, it could be used for data recovery from damaged mobile devices. VSS is a backup infrastructure provided by Windows that creates point-in-time copies of a volume (known as volume shadow copies). Windows Vista and later versions use this service instead of the restore point feature used in earlier versions of the operating system. The restore point feature logically copied and stored specified files, whereas VSS copies and stores only data that have changed in the volume. In a live system, volume shadow copies can be checked and recovered using built-in system commands. However, it is difficult to analyze the files stored in the volume shadow copies of a nonfunctioning system, such as a disk image, because only the changed data are stored. Therefore, this study analyzes the structure of Volume Shadow Copy (VSC) files that were logically stored. This analysis confirms the locations of the changed data and original copies by identifying a structure that stores the file data stream to file system metadata. On the basis of our research, we propose a practical application to develop tools for the recovery of snapshot data stored within the VSC files. We also present results of our successful performance test.

As the computer system has developed much in this highly information-oriented society, database security has become a very important problem and its backup strategies need to be made more efficiently and safety. The image copy method has been used as the most simple and dependable recovery mechanism for media failure. However, this method spends high overhead costs for massive data transmission and much processing time in the normal operation of the database. To cover such weak points, incremental and full backup methods are adopted before updated trucks reach a predetermined level. Moreover, when the number of full backup files exceeded a predetermined level, we stop incremental and full backups and switch it to the image copy. This paper applies cumulative damage model to backup of files in a database system, by putting damage shock by update, failure shock by database failure and damage by dumped files, and considers the tradeoff among overhead costs of image copy and incremental, full backup methods, and discusses analytically an optimal policy for the image copy backup interval. Finally, numerical examples are given in the case of Poisson process and exponential distributions.

Cloud Computing is process of hosting remote services over the internet. It provides data recovery easily .The system is having a persistent local backup which checks out periodically changes of data in the user folder. The compression technique is also the main objective of the system as to reduce the bandwidth which it takes for storing and accessing data from the cloud. The local backup process which is in the system itself where all the data of the user is stored securely. Video files are compressed automatically by the software known as FFMPEG and new copy of video file with compression is stored in to the local backup. The storage is over the cloud in the Microsoft one drive which has to be activated before storing data and retrieving data. The user data is updated each time and data backup is also present for the files added, deleted or modified. The one drive where the data is stored and accessible anytime and anywhere across the world. The user of data is the only authorized person to access the data. The sharing of data is possible and also the graph of various compressed and uncompressed file can be analyzed for the purpose of saving bandwidth. Data is protected both in local backup and stored over the cloud for automatically retrieving it. The result provides that the data security is given by maintaining two backups through which data can be easily retrieved with compressing videos to save the bandwidth

Cloud computing is an emerging service that enables users to store and manage their data easily at a low cost. We propose a Deduplicating Secure Backup System with Encryption Intensity Selection (DSB-SEIS) that combines features to amend security and performance of cloud-based backup services. Our scheme introduces the concept of encryption intensity selection to cloud backup systems, which allows users to select the encryption intensity of their files. We also combine features such as deduplication, assured deletion, and multi-aspect awareness to further enhance our scheme. The DSB-SEIS performance is measured over an OpenStack cloud installed on CloudLab resources demonstrating that DSB-SEIS can improve the backup service.

Based on research and analysis of the Ceph file system, the log cache backup scheme is proposed. The log cache backup scheme reduces metadata access delays by caching logs and reducing the time of storing logs to server clusters. In order to prevent the loss of cached data in metadata servers, a cached data backup scheme is proposed. Compared to the metadata management subsystem of the Ceph, the log cache backup scheme can effectively improve the access performance of metadata. Finally, based on open source codes of the Ceph file system, the log cache backup scheme is implemented. Compared to the performance of the Ceph metadata management subsystem, experiment results show that performance improvements of the log cache backup scheme are up to 11.5%.

Recently, as a robust backup, geographically dispersed and distributed backup systems to save data have been attracting attention. However, existing distributed backup systems that can be operated at low cost present various difficulties such as processing time, immediacy, and storage capacity. Therefore, as an existing method intended to solve this problem, multiagentbased distributed backup systems exist for individuals to solve problems of processing time and immediacy. However, problems of storage capacity exist in such systems. This research presents a deduplication technique of block data to solve problems of storage capacity. By incorporating this into the system, we aim at storage capacity reduction of backup data.

The main thing that IT operation managers consider is protecting assets of corporation from system failure and disaster. Therefore, this research proposed a backup system for a disaster recovery. Previous backup method is that if database update occurs, this record is saved in redo log, and if the size of record file is over than expected, this file is saved in archive log in order. Thus, it is possible to occur errors of data loss from the process of data backup which change in real time while changes of database occur. Suggested backup system is back redo log up to database of transaction log in real time, and back a record that can be omitted from previous backup method up to archive log. When recover the data, it is possible to recover redo log in real time online, and it minimizes data loss. Also, throughout multi thread processing method data recovery is performed and it is designed that system performance is improved. To verify stability of backup system CPN(Coloured Petri Net) is introduced, and each step of backup system is displayed in diagram form, and th e stability is verified based on the definition and theorem of CPN.

Existing backup systems are unsatisfactory: commercial backup services are reliable but expensive while peer-to-peer systems are cheap but offer limited assurance of data reliability. This article introduces Friendstore, a system that provides inexpensive and reliable backup by giving users the choice to store backup data only on nodes they trust (typically those owned by friends and colleagues). Because it is built on trusted nodes, Friendstore is not burdened by the complexity required to cope with potentially malicious participants. Friendstore only needs to detect and repair accidental data loss and to ensure balanced storage exchange. The disadvantage of using only trusted nodes is that Friendstore cannot achieve perfect storage utilization. Friendstore is designed for a heterogeneous environment where nodes have very different access link speeds and available disk spaces. To ensure long-term data reliability, a node with limited upload bandwidth refrains from storing more data than its calculated maintainable capacity. A high bandwidth node might be limited by its available disk space. We introduce a simple coding scheme, called XOR(1,2), which doubles a node's ability to store backup information in the same amount of disk space at the cost of doubling the amount of data transferred during restore. Analysis and simulations using long-term node activity traces show that a node can reliably back up tens of gigabytes of data even with low upload bandwidth.

while many solutions for making backup and restoring data are known for servers and desktops, mobile devices pose several challenges, mainly due to the differentiating in devices, vendors, operating system and version available in mobile market. We introduce a new backup and restore approach for mobile devices, which helps to reduce the effort in saving and restoring personal data. Our approach is minimize time in efficient way for backup and restoring the data on on-line server using android platform. We are using compression (ZIP) which will reduce the size of file which we are interested to backup and we also use some other component which helps us to reduce the time in backup and efficient storage at the server side. Using of various compression techniques such as RLE, Lempel ziv, wavelets can introduce overhead for maintaining different compression for different purpose. Instead of XML, we are using PHP, the most advantage of using php is that it store file as fragmented manner and did not need any extra effort to do. Also MYsql is far better because it have the flexibility (using SQL commands like SELECT, DELETE, SORT, INSERT, etc.) to easily manipulate the data.

This paper introduced the definition of the self-construction digital resources and it needs long-term preservation. The author produced a suitable backup system of the self-construction digital resources according to its software and hardware features, and made a simple analysis.

Protection techniques for optical networks mainly rely on preallocated backup bandwidth, which may not be able to provide full protection guarantee when multiple failures occur in a network. To protect against multiple concurrent potential failures and to utilize the available resources more efficiently, strategies such as backup reprovisioning (BR) rearrange backups of protected connections after one failure occurs or, more generally, whenever the network state changes, e.g., when a new request arrives or terminates. Recently, new solutions for automatized management in optical networks promise to allow customers to specify on-demand the terms of the service level agreement (SLA) to be guaranteed by the service provider. In this paper, we study different backup reprovisioning techniques able to further reduce the capacity requirements exploiting the knowledge, among the other service level specifications (SLSs), of the connection holding time.

The promise of automatic data backup into the cloud is alluring. Off-site backup offers protection against a whole class of catastrophic risks (fire, flood, etc.) that on-site backup solutions cannot. Data can be backed up into the cloud automatically with little or no user involvement. Incremental backup software running detects the latest changes, encrypts the data, and sends it into the cloud. Files can be restored on demand and some services allow copies of files to be downloaded through a web interface to other machines , providing a form of file sharing. With costs dropping to ∼$60-$100 per year for unlimited storage, it is not surprising that many home and small business users are signing up. In this paper, we evaluate four popular consumer cloud storage offerings - Mozy, Carbonite, Dropbox, and CrashPlan - to determine if they live up to the benefits users expect. We document wide variations in backup and restore performance, the type of data that is backed-up, no liability for data loss, and problems with data privacy. From our experiments, we derive a set of lessons and recommendations for consumer cloud storage that if followed more uniformly, could substantially improve the cloud storage experience for many consumers.

Apart from high space efficiency, other demanding requirements for enterprise de-duplication backup are high performance, high scalability, and availability for large-scale distributed environments. The main challenge is reducing the significant disk input/output (I/O) overhead as a result of constantly accessing the disk to identify duplicate chunks. Existing inline de-duplication approaches mainly rely on duplicate locality to avoid disk bottleneck, thus suffering from degradation under poor duplicate locality workload. This paper presents Chunkfarm, a post-processing de-duplication backup system designed to improve capacity, throughput, and scalability for de-duplication. Chunkfarm performs de-duplication backup using the hash join algorithm, which turns the notoriously random and small disk I/Os of fingerprint lookups and updates into large sequential disk I/Os, hence achieving high write throughput not influenced by workload locality. More importantly, by decentralizing fingerprint lookup and update, Chunkfarm supports a cluster of servers to perform de-duplication backup in parallel; it hence is conducive to distributed implementation and thus applicable to large-scale and distributed storage systems.