An Autonomic Resource Allocating SSD

Abstract

When an SSD is used for executing multiple work-loads, its internal resources should be allocated to prevent the competing workloads from interfering with each other. While channel-based allocation strategies turn out to be quite effective in offering performance isolation, questions like 'what is the optimal allocation?' and 'how can one efficiently search for the optimal allocation?' remain unaddressed. To this end, we explore the channel allocation problem in SSDs and employ a reinforcement learning-based approach to address the problem. Specifically, we present an autonomic channel allocating SSD, called AutoAlloc, which can seek near-optimal channel allocation in a self-learning fashion for a given set of co-running workloads. The salient features of AutoAlloc include the following: (i) the optimal allocation can change depending on the user-defined optimization metrics; (ii) the search process takes place in an online setting without any need of extra workload profiling or performance estimation; and, (iii) the search process is fully-automated without requiring any user intervention. We implement AutoAlloc in LightNVM (the Linux subsystem) as part of the FTL, which operates with an emulated Open-Channel SSD. Our extensive experiments using various user-defined optimization metrics and workload execution scenarios indicate that AutoAlloc can find a near-optimal allocation after examining only a very limited number of candidate allocations. © 2024 EDAA.