LoADM: Load-Aware Directory Migration Policy in Distributed File Systems
Wang Yuan-zhang, Peng Zhang, Fengkui Yang, Ke Zhou, Chunhua Li · 2024
Distributed file systems often suffer from load imbalance when encountering skewed workloads. A few directories can become hotspots due to frequent access. Failure to migrate these high-load directories promptly will result in node overload, which can seriously degrade the performance of the system. To solve this challenge, in this paper, we propose a novel load-aware directory migration policy named LoADM to alleviate the load imbalance caused by hot directories. LoADM consists of three parts, i.e. learning-based directory hotness model, urgency analysis and multidimensional directory migration model. Specifically, we use a directory hotness model to identify potentially high-load directories in advance. Second, by combining the predicted directory hotness and system node status, the urgency analysis determines when to trigger a migration or tolerate an imbalance. Then, peer directory co-migration is proposed to better exploit data locality. Finally, we migrate high-load directories to appropriate storage nodes through a Particle Swarm Optimization based directory migration model. Extensive experiments show that our approach provides a promising data migration policy and can greatly improve performance compared to the state-of-the-art.