Backup and Disaster Recovery System for HDFS
Title | Backup and Disaster Recovery System for HDFS |
Publication Type | Conference Paper |
Year of Publication | 2016 |
Authors | Luo, S., Wang, Y., Huang, W., Yu, H. |
Conference Name | 2016 International Conference on Information Science and Security (ICISS) |
ISBN Number | 978-1-5090-5493-0 |
Keywords | backup and disaster recovery system, Benchmark testing, cloud computing, Collaboration, composability, data communication, data retention, file system backup, gigabit Ethernet, Hadoop distributed file system, HDFS, HDFS cluster, Human Behavior, human factors, massive scale data storage, metadata, Metrics, parallel processing, performance evaluation, Policy-Governed Secure Collaboration, pubcrawl, reliability, Resiliency, Scalability, science of security, Servers, storage management |
Abstract | 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. |
URL | https://ieeexplore.ieee.org/document/7885845/ |
DOI | 10.1109/ICISSEC.2016.7885845 |
Citation Key | luo_backup_2016 |
- Human Factors
- storage management
- Servers
- Science of Security
- Scalability
- Resiliency
- Reliability
- pubcrawl
- Policy-Governed Secure Collaboration
- performance evaluation
- parallel processing
- Metrics
- metadata
- massive scale data storage
- backup and disaster recovery system
- Human behavior
- HDFS cluster
- HDFS
- Hadoop distributed file system
- gigabit Ethernet
- file system backup
- data retention
- data communication
- composability
- collaboration
- Cloud Computing
- Benchmark testing