S-CAVE: effective SSD caching to improve virtual machine storage performance

  • Authors:

  • Tian Luo;Siyuan Ma;Rubao Lee;Xiaodong Zhang;Deng Liu;Li Zhou

  • Affiliations:

  • The Ohio State University, Columbus, OH, USA;The Ohio State University, Columbus, OH, USA;The Ohio State University, Columbus, OH, USA;The Ohio State University, Columbus, OH, USA;VMware Inc., Palo Alto, CA, USA;Facebook Inc., Menlo Park, CA, USA

  • Venue:
  • PACT '13 Proceedings of the 22nd international conference on Parallel architectures and compilation techniques
  • Year:
  • 2013

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Abstract

A unique challenge for SSD storage caching management in a virtual machine (VM) environment is to accomplish the dual objectives: maximizing utilization of shared SSD cache devices and ensuring performance isolation among VMs. In this paper, we present our design and implementation of S-CAVE, a hypervisor-based SSD caching facility, which effectively manages a storage cache in a Multi-VM environment by collecting and exploiting runtime information from both VMs and storage devices. Due to a hypervisor's unique position between VMs and hardware resources, S-CAVE does not require any modification to guest OSes, user applications, or the underlying storage system. A critical issue to address in S-CAVE is how to allocate limited and shared SSD cache space among multiple VMs to achieve the dual goals. This is accomplished in two steps. First, we propose an effective metric to determine the demand for SSD cache space of each VM. Next, by incorporating this cache demand information into a dynamic control mechanism, S-CAVE is able to efficiently provide a fair share of cache space to each VM while achieving the goal of best utilizing the shared SSD cache device. In accordance with the constraints of all the functionalities of a hypervisor, S-CAVE incurs minimum overhead in both memory space and computing time. We have implemented S-CAVE in vSphere ESX, a widely used commercial hypervisor from VMWare. Our extensive experiments have shown its strong effectiveness for various data-intensive applications.