BlockFlex: Enabling Storage Harvesting with Software-Defined Flash in Modern Cloud Platforms
BlockFlex: Enabling Storage Harvesting with Software-Defined Flash in Modern Cloud Platforms
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发表时间：
2022
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通讯作者：
Benjamin Reidys;Jinghan Sun;Anirudh Badam;S. Noghabi;Jian Huang
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作者：
Benjamin Reidys;Jinghan Sun;Anirudh Badam;S. Noghabi;Jian Huang
如今的云平台通过按需在多租户应用程序之间划分存储资源，从而有效地利用了存储资源。然而，我们的研究表明，云存储在已分配和未分配存储方面仍未得到充分利用。尽管云提供商已经开发了收集技术来允许虚拟机（vm）使用未分配的资源，但由于缺乏对存储设备中空间、带宽和数据安全隔离的系统支持，这些技术不能直接应用于存储资源。在本文中，我们提出了BlockFlex，一个基于学习的存储收集框架，它可以在现代云平台中以细粒度的粒度收集可用的基于闪存的存储资源。我们重新考虑了存储虚拟化的抽象，并为可驱逐的虚拟机支持透明地收集已分配和未分配的存储。BlockFlex探索了启发式和基于学习的方法，以最大限度地提高存储利用率，同时在存储设备级别确保常规vm和可驱逐vm之间的性能和安全隔离。我们使用可编程固态硬盘（ssd）开发BlockFlex，并在各种数据中心工作负载下演示其效率。
Cloud platforms today make efficient use of storage resources by slicing them among multi-tenant applications on demand. However,ourstudydiscloses thatcloudstorage is stillseriously underutilized for both allocated and unallocated storage. Although cloud providers have developed harvesting techniques toallowevictablevirtualmachines(VMs)touseunallocatedre-sources, these techniques cannot be directly applied to storage resources,due to the lack of systematic support forthe 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.