Edges: Evenly Distributing Garbage-Collections for Enterprise SSDs via Stochastic Optimization

Abstract

Solid-state drives (SSDs) have been widely used in various computing systems owing to their significant advantages over hard disk drives (HDDs). One critical challenge that hinders its further adoption in enterprise systems is to resolve the performance variability issue caused by the garbage collection (GC) process that frees flash memory containing invalid data. To overcome this challenge, we formulate a stochastic optimization model that characterizes the nature of the GC process and considers both total GC count and GC distribution over time. Based on the optimization model, we propose Edges, an innovative self-adaptive GC strategy that evenly distributes GCs for enterprise SSDs. The key insight behind Edges is that the number of invalid pages is a finer-grained metric of triggering GCs than the number of free blocks. By testing various traces from practical applications, we show that Edges is able to reduce the total GC counts by as high as 70.17% and GC variance by up to 57.29%, compared to the state-of-the-art GC algorithm.