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Multi-level Selective Deduplication for VM Snapshots in Cloud Storage

Multi-level Selective Deduplication for VM Snapshots in Cloud Storage. Wei Zhang*, Hong Tang † , Hao Jiang † , Tao Yang*, Xiaogang Li † , Yue Zeng † * University of California at Santa Barbara † Aliyun.com Inc. Motivations.

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Multi-level Selective Deduplication for VM Snapshots in Cloud Storage

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  1. Multi-level Selective Deduplication for VMSnapshots in Cloud Storage Wei Zhang*, Hong Tang†, Hao Jiang†, Tao Yang*, Xiaogang Li†, Yue Zeng† * University of California at Santa Barbara † Aliyun.com Inc.

  2. Motivations • Virtual machines on the cloud use frequent backup to improve service reliability • Used in Alibaba’s Aliyun - the largest public cloud service in China • High storage demand • Daily backup workload: hundreds of TB @ Aliyun • Number of VMs per cluster: 10000+ • Large content duplicates • Limited resource for deduplication • No special hardware or dedicated machines • Small CPU& memory footprint

  3. Focus and Related Work • Previous work • Version-based incremental snapshot backup • Inter-block/VM duplicates are not detected. • Chunk-based file duduplication • High cost for chunk lookup • Focus on • Parallel backup of a large number of virtual disks. • Large files for VM disk images. • Contributions • Cost-constrained solution with very limited computing resource • Multi-level selective duplicate detection and parallel backup.

  4. Requirements • Negligible impact on existing cloud service and VM performance • Must minimize CPU and IO bandwidth consumption for backup and deduplication workload • (e.g. <1% of total resource). • Fast backup speed • Compute backup for 10,000+ users within a few hours each day during light cloud workload. • Fault tolerance constraint • addition of data deduplication should not decrease the degree of fault tolerance.

  5. Design Considerations • Design alternatives • An external and dedicated backup storage system. • A decentralized and co-hosted backup system with full deduplication Backup Cloud service backup backup backup . . . Cloud service Cloud service Cloud service

  6. Design Considerations • Decentralized architecture running on a general purpose cluster • co-hosting both elastic computing and backup service • Multi-level deduplication • Localize backup traffic and exploit data parallelism • Increase fault tolerance • Selective deduplication • Use minimal resource while still removing most of redundant content and accomplishing good efficiency

  7. Key Observations • Inner-VM data characteristics • Exploit unchanged data to localize deduplication • Cross-VM data characteristics • Small common data dominates duplicates • Zipf-like distribution of VM OS/user data • Separate consideration of OS and user data

  8. VM Snapshot Representation Segments are fix-sized Data blocks are variable-sized

  9. Processing Flow of Multi-level Deduplication

  10. Data Processing Steps • Segment level checkup. • Use dirty bitmap to see which segments are modified. • Block level checkup • Divide a segment into variable-sized blocks, and compare their signatures with the parent snapshot • Checkup from common dataset (CDS) • Identify duplicate chunks from CDS • Write new snapshot blocks • Write new content chunks to stoage. • Save recipes • Save segment meta-data information

  11. Architecture of Multi-level VM snapshot backup Cluster node

  12. Status& Evaluation • Prototype system running on Alibaba’s Aliyuan cloud. • Based on Xen. • 100 nodes and each has 16 cores, 48G memory, 25VMs. • Use <150MB per machine for backup&deduplication • Evaluation data from Aliyuan’s production cluster • 41TB. • 10 snapshots per VM • Segment size: 2MB. • Avg. Block size: 4KB

  13. Data Characteristics of the Benchmark • Each VM uses 40GB storage space on average • OS and user data disks: each takes ~50% of space • OS data • 7 main stream OS releases: • Debian, Ubuntu, Redhat, CentOS, Win2003 32bit, win2003 64 bit and win2008 64 bit. • User data • From 1323 VM users

  14. Impacts of 3-Level Deduplication Level 1: Segment-level detection within VM Level 2: Block-level detection within VM Level 3: Common data block detection across-VM

  15. Impact for Different OS Releases

  16. Separate consideration of OS and user data Both have Zipf-like data distribution But popularity growth differs as the cluster size/VM users increase

  17. Commonality among OS releases 1G common OS meta data covers 70+%

  18. Cumulative coverage of popular user data Coverage is the summation of covered data block size*frequency

  19. Space saving compared to perfect deduplication as CDS size increases 100G CDS (1GB index) -> 75% of perfect dedup

  20. Impact of dataset-size increase

  21. Conclusions • Contributions: • A multi-level selective deduplication scheme among VM snapshots • Inner-VM deduplication localizes backup and exposes more parallelism • global deduplication with a small common data set appeared in OS and data disks • Use less than 0.5% of memory per node to meet a stringent cloud resource requirement -> accomplish 75% of what perfect deduplication does. • Experiments • Achieve 500TB/hour on a 1000-node cloud cluster • Reduce bandwidth by 92% -> 40TB/hour

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