Latency reduction and load balancing in coded storage systems

Erasure coding has been used in storage systems to enhance data durability at a lower storage overhead. However, these systems suffer from long access latency tails due to a lack of flexible load balancing mechanisms and passively launched degraded reads when the original storage node of the requested data becomes a hotspot. We provide a new perspective to load balancing in coded storage systems by proactively and intelligently launching degraded reads and propose a variety of schemes to make optimal decisions either per request or across requests statistically. Experiments on a 98-machine cluster based on the request traces of 12 million objects collected from Windows Azure Storage (WAS) show that our schemes can reduce the median latency by 44.7% and the 95th-percentile tail latency by 77.8% in coded storage systems.