On IO Latency Prediction Accuracy and Automated Load Balancing in Consolidated VM Environments

Manually managing IO workloads and performance in consolidated VM environments is often difficult and error prone. Thus, automated IO workload (re) placement using virtual disk migration is a key functionality of large scale VM infrastructure. A promising approach is to place IO workloads based on predicted IO latencies, but previous prediction models are often inaccurate due to dependence on there being only a linear relationship between workload parameters and IO latency. This paper presents a new accurate IO latency prediction model for use in automated load balancing. Our experiments show that our model improves relative error ratio of IO latency prediction by 67% for SSDs and 43% for HDDs on average. We also evaluate how the improvement of IO latency prediction affects actual load balancing and overall IO performance. Contrary to our expectation, we find that the significant improvement of IO latency prediction accuracy does not translate into significant overall performance improvement.