Even Data Placement for Load Balance in Reliable Distributed Deduplication Storage Systems

Modern distributed storage systems often deploy deduplication to remove content-level redundancy and hence improve storage efficiency. However, deduplication inevitably leads to unbalanced data placement across storage nodes, thereby degrading read performance. This paper studies the load balance problem in the setting of a reliable distributed deduplication storage system, which deploys deduplication for storage efficiency and erasure coding for reliability. We argue that in such a setting, it is generally challenging to find a data placement that simultaneously achieves both read balance and storage balance objectives. To this end, we formulate a combinatorial optimization problem, and propose a greedy, polynomial-time Even Data Placement (EDP) algorithm, which identifies a data placement that effectively achieves read balance while maintaining storage balance. We further extend our EDP algorithm to heterogeneous environments. We demonstrate the effectiveness of our EDP algorithm under real-world workloads using both extensive simulations and prototype testbed experiments. In particular, our testbed experiments show that our EDP algorithm reduces the file read time by 37.41% compared to the baseline round-robin placement, and the reduction can further reach 52.11% in a heterogeneous setting.