BlockFlex: Enabling Storage Harvesting with Software-Defined Flash in Modern Cloud Platforms


Benjamin Reidys and Jinghan Sun, University of Illinois at Urbana-Champaign; Anirudh Badam and Shadi Noghabi, Microsoft Research; Jian Huang, University of Illinois at Urbana-Champaign


Cloud platforms today make efficient use of storage resources by slicing them among multi-tenant applications on demand. However, our study discloses that the cloud storage is still seriously underutilized for both allocated and unallocated storage. Although cloud providers have developed harvesting techniques to allow evictable virtual machines (VMs) to use unallocated resources, these techniques cannot be directly applied to storage resources, due to the lack of systematic support for isolation of space, bandwidth, and data security in storage devices.

In this paper, we present Blockflex, a learning-based storage harvesting framework, which can harvest available flash-based storage resources at a fine-grained granularity in modern cloud platforms. We rethink the abstractions of storage virtualization and enable transparent harvesting of both allocated and unallocated storage for evictable VMs. Blockflex explores both heuristics and learning-based approaches to maximize the storage utilization, while ensuring the performance and security isolation between regular and evictable VMs at the storage device level. We develop Blockflex with programmable solid-state drives (SSDs) and demonstrate its efficiency with various datacenter workloads.

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@inproceedings {280926,
author = {Benjamin Reidys and Jinghan Sun and Anirudh Badam and Shadi Noghabi and Jian Huang},
title = {{BlockFlex}: Enabling Storage Harvesting with {Software-Defined} Flash in Modern Cloud Platforms},
booktitle = {16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22)},
year = {2022},
isbn = {978-1-939133-28-1},
address = {Carlsbad, CA},
pages = {17--33},
url = {},
publisher = {USENIX Association},
month = jul,

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