Automatic I/O scheduler selection through online workload analysis

Abstract

I/O performance is a bottleneck for many workloads. The I/O scheduler plays an important role in it. It is typically configured once by the administrator and there is no selection that suits the system at every time. Every I/O scheduler has a different behavior depending on the workload and the device. We present a method to select automatically the most suitable I/O scheduler for the ongoing workload. This selection is done online, using a workload analysis method with small I/O traces, finding common I/O patterns. Our dynamic mechanism adapts automatically to one of the best schedulers, sometimes achieving improvements on I/O performance for heterogeneous workloads beyond those of any fixed configuration (up to 5%). This technique works with any application and device type (RAID, HDD, SSD), as long as we have a system parameter to tune. It does not need disk simulations or hardware models, which are normally unavailable. We evaluate it in different setups, and with different benchmarks.