Scaling up the backup storage for an ever-increasing volume of virtual machine (VM) images is a critical issue in virtualization environments. While deduplication is known to effectively eliminate duplicates for VM image storage, it also introduces fragmentation that will degrade read performance. We propose RevDedup, a deduplication system that optimizes reads to latest VM image backups using an idea called reverse deduplication. In contrast with conventional deduplication that removes duplicates from new data, RevDedup removes duplicates from old data, thereby shifting fragmentation to old data while keeping the layout of new data as sequential as possible. We evaluate our RevDedup prototype using microbenchmark and real-world workloads. For a 12-week span of real-world VM images from 160 users, RevDedup achieves high deduplication efficiency with around 97% of saving, and high backup and read throughput on the order of 1GB/s. RevDedup also incurs small metadata overhead in backup/read operations.