A Black-Box Self-Learning Scheduler for Cloud Block Storage Systems

A major requirement of cloud block storage services is guaranteed performance and high availability. However, offering guaranteed Service Level Agreements (SLAs) in cloud block storage services is often not straightforward. Cloud block storage performance may be affected by physical disk background operations, like garbage collection, storage cluster features, workload interference and the chraracteristcs of the workload itself. On the other hand, the underlying physical storage drives do not expose the internal states to higher level block storage service offerings. Therefore, SLAs can only be satisfied by over-provisioning the storage resources. To address this issue, we propose a self-learning scheduler that can dynamically adapt based on the workload, and efficiently provide a scheduling decision with zero knowledge of the underlying hardware. We study two candidate algorithms based on Feedback learning and Two-Phase learning. We used workloads that were deducted from real-world block-level traces of an enterprise data center, and conducted extensive simulations. Our results indicate that the self-learning scheduling approach can reduce the SLA violations by mitigating the unexpected resource fluctuation, and the scheduler can also adapt dynamically to various workloads.