Deduplication decreases the physical occupancy of files in a storage volume by removing duplicate copies of data chunks, but creates data-sharing dependencies that complicate standard storage management tasks. Specifically, data migration plans must consider the dependencies between files that are remapped to new volumes and files that are not. Thus far, only greedy approaches have been suggested for constructing such plans, and it is unclear how they compare to one another and how much they can be improved. We set to bridge this gap for seeding —migration in which the target volume is initially empty. We prove that even this basic instance of data migration is NP-hard in the presence of deduplication. We then present GoSeed, a formulation of seeding as an integer linear programming (ILP) problem, and three acceleration methods for applying it to real-sized storage volumes. Our experimental evaluation shows that, while the greedy approaches perform well on “easy” problem instances, the cost of their solution can be significantly higher than that of GoSeed’s solution on “hard” instances, for which they are sometimes unable to find a solution at all.

Deduplication reduces the size of the data stored in large-scale storage systems by replacing duplicate data blocks with references to their unique copies. This creates dependencies between files that contain similar content, and complicates the management of data in the system. In this paper, we address the problem of data migration, where files are remapped between different volumes as a result of system expansion or maintenance. The challenge of determining which files and blocks to migrate has been studied extensively for systems without deduplication. In the context of deduplicated storage, however, only simplified migration scenarios have been considered. In this paper, we formulate the general migration problem for deduplicated systems as an optimization problem whose objective is to minimize the system’s size while ensuring that the storage load is evenly distributed between the system’s volumes, and that the network traffic required for the migration does not exceed its allocation. We then present three algorithms for generating effective migration plans, each based on a different approach and representing a different tradeoff between computation time and migration efficiency. Our greedy algorithm provides modest space savings, but is appealing thanks to its exceptionally short runtime. Its results can be improved by using larger system representations. Our theoretically optimal algorithm formulates the migration problem as an ILP (integer linear programming) instance. Its migration plans consistently result in smaller and more balanced systems than those of the greedy approach, although its runtime is long and, as a result, the theoretical optimum is not always found. Our clustering algorithm enjoys the best of both worlds: its migration plans are comparable to those generated by the ILP-based algorithm, but its runtime is shorter, sometimes by an order of magnitude. It can be further accelerated at a modest cost in the quality of its results.