Difference engine: harnessing memory redundancy in virtual machines

  • Authors:

  • Diwaker Gupta;Sangmin Lee;Michael Vrable;Stefan Savage;Alex C. Snoeren;George Varghese;Geoffrey M. Voelker;Amin Vahdat

  • Affiliations:

  • University of California, San Diego;UT Austin and University of California, San Diego;University of California, San Diego;University of California, San Diego;University of California, San Diego;University of California, San Diego;University of California, San Diego;University of California, San Diego

  • Venue:
  • OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

Virtual machine monitors (VMMs) are a popular platform for Internet hosting centers and cloud-based compute services. By multiplexing hardware resources among virtual machines (VMs) running commodity operating systems, VMMs decrease both the capital outlay and management overhead of hosting centers. Appropriate placement and migration policies can take advantage of statistical multiplexing to effectively utilize available processors. However, main memory is not amenable to such multiplexing and is often the primary bottleneck in achieving higher degrees of consolidation. Previous efforts have shown that content-based page sharing provides modest decreases in the memory footprint of VMs running similar operating systems and applications. Our studies show that significant additional gains can be had by leveraging both sub-page level sharing (through page patching) and in-core memory compression. We build Difference Engine, an extension to the Xen virtual machine monitor, to support each of these--in addition to standard copy-on-write full page sharing--and demonstrate substantial savings not only between VMs running similar applications and operating systems (up to 90%), but even across VMs running disparate workloads (up to 65%). In head-to-head memory-savings comparisons, Difference Engine outperforms VMware ESX server by a factor of 1.5 for homogeneous workloads and by a factor of 1.6-2.5 for heterogeneous workloads. In all cases, the performance overhead of Difference Engine is less than 7%.