Data location optimization for a self-organized distributed storage system

Swarm-inspired algorithms allow the creation of complex systems that are scalable in many dimensions, adaptable to changing conditions, and robust against failure. These properties make them suitable for the challenges inherent in distributed storage systems. However, these swarm-based approaches reach their impressive performance by trading away correctness guarantees, occasionally leading to misplaced data items. In order to achieve consistent storage, there is a need for a constant optimization of the store´s data structure. In this paper, we describe a fully distributed and scalable heuristic for the optimization of the location of stored data items within a distributed storage system based on the brood sorting method used by ants. We evaluate our heuristic using best- and worst-case test data sets to determine whether our location optimization method converges and whether it improves the location and organization of data inside a large-scale storage network.