Randomized load balancing in scalable storage systems

Presently, IP-networked real-time streaming media storage has become increasingly common as an integral part of many applications. In recent years, a considerable amount of research has focused on the scalability issues in storage systems. Random placement of data blocks has been proven to be an effective approach to balance heterogeneous workload in a multi-disk environments. However, the main disadvantage of this technique is that statistical variations can still result in short term load imbalances in disk utilization, which in turn, cause large variances in latencies. In this paper, we propose a packet level randomization (PLR) technique to solve this challenge. We quantify the exact performance trade-off between our PLR approach and the traditional block level randomization (BLR) technique through analytical analysis. Our preliminary results show that the PLR technique outperforms the BLR approach and achieves much better load balancing in multi-disk storage systems.