A Smart Background Scheduler for Storage Systems
Maher Kachmar, David Kaeli

TL;DR
This paper introduces a learning-based background scheduler for storage systems that dynamically manages background tasks to optimize performance and meet service level objectives, especially under high workload conditions.
Contribution
It presents a novel priority-based scheduler that learns workload patterns and adaptively schedules background tasks to improve storage system performance and SLO compliance.
Findings
Reduced SLO violations from 54.6% to 6.2%.
Effectively manages background debt dynamically.
Improves performance during high workload periods.
Abstract
In today's enterprise storage systems, supported data services such as snapshot delete or drive rebuild can cause tremendous performance interference if executed inline along with heavy foreground IO, often leading to missing SLOs (Service Level Objectives). Typical storage system applications such as web or VDI (Virtual Desktop Infrastructure) follow a repetitive high/low workload pattern that can be learned and forecasted. We propose a priority-based background scheduler that learns this repetitive pattern and allows storage systems to maintain peak performance and in turn 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 forecast shows that…
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