Multi-level Selective Deduplication for VM Snapshots in Cloud Storage

In a virtualized cloud computing environment, frequent snapshot backup of virtual disks improves hosting reliability but storage demand of such operations is huge. While dirty bit-based technique can identify unmodified data between versions, full deduplication with fingerprint comparison can remove more redundant content at the cost of computing resources. This paper presents a multi-level selective deduplication scheme which integrates inner-VM and cross-VM duplicate elimination under a stringent resource requirement. This scheme uses popular common data to facilitate fingerprint comparison while reducing the cost and it strikes a balance between local and global deduplication to increase parallelism and improve reliability. Experimental results show the proposed scheme can achieve high deduplication ratio while using a small amount of cloud resources.