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:

  • Aster Data;UT Austin;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:
  • Communications of the ACM
  • Year:
  • 2010

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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 subpage level sharing (through page patching) and incore memory compression. We build Difference Engine, an extension to the Xen VMM, to support each of these---in addition to standard copy-on-write full-page sharing---and demonstrate substantial savings across VMs running disparate workloads (up to 65%). In head-to-head memory-savings comparisons, Difference Engine outperforms VMware ESX server by a factor 1.6--2.5 for heterogeneous workloads. In all cases, the performance overhead of Difference Engine is less than 7%.