A Smart Background Scheduler for Storage Systems

In today's enterprise storage systems, supported data services such as snapshot delete or drive rebuild can result in tremendous performance overhead if executed inline along with heavy foreground IO, often leading to missing Service Level Objectives (SLOs). Typical storage system applications such as Virtual Desktop Infrastructure (VDI) or web services follow a repetitive high/low workload pattern that can be learned and forecasted. We propose a priority-based background scheduler that learns this pattern and allows storage systems to maintain peak performance and meet service level objectives (SLOs) while supporting a number of data services. When foreground IO demand intensifies, system resources are dedicated to service foreground IO requests and any background processing that can be deferred are recorded to be processed in future idle cycles as long as our forecaster predicts that the storage pool has remaining capacity. The smart background scheduler adopts a resource partitioning model that allows both foreground and background IO to execute together as long as foreground IOs are not impacted, harnessing any free cycles to clear background debt. Using traces from VDI and web services applications, we show how our technique can out-perform a static policy that sets fixed limits on the deferred background debt and reduces SLO violations from 54.6% (when using a fixed background debt watermark), to only 6.2 % when dynamically adjusted by our smart background scheduler.