A method for reducing garbage collection overhead of SSD using machine learning algorithms

In this paper, we attempt to manage GC overhead at the operating system level. In our approach, first, we use a machine learning technique to devise a GC detecting mechanism at the operating system level, and second, we show that by making use of this mechanism performance variance normally observed on SSDs can be reduced. We develop a GC-detector that detects garbage collection of SSDs and request TRIM operations to the SSD when GC is detected. Experimental results running the GC-detector show increase average bandwidth and low performance variance compared to when not using GC-detector.