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Memory buddies: exploiting page sharing for smart colocation in virtualized data centers

Published: 11 March 2009 Publication History

Abstract

Many data center virtualization solutions, such as VMware ESX, employ content-based page sharing to consolidate the resources of multiple servers. Page sharing identifies virtual machine memory pages with identical content and consolidates them into a single shared page. This technique, implemented at the host level, applies only between VMs placed on a given physical host. In a multi-server data center, opportunities for sharing may be lost because the VMs holding identical pages are resident on different hosts. In order to obtain the full benefit of content-based page sharing it is necessary to place virtual machines such that VMs with similar memory content are located on the same hosts.
In this paper we present Memory Buddies, a memory sharing-aware placement system for virtual machines. This system includes a memory fingerprinting system to efficiently determine the sharing potential among a set of VMs, and compute more efficient placements. In addition it makes use of live migration to optimize VM placement as workloads change. We have implemented a prototype Memory Buddies system with VMware ESX Server and present experimental results on our testbed, as well as an analysis of an extensive memory trace study. Evaluation of our prototype using a mix of enterprise and e-commerce applications demonstrates an increase of data center capacity (i.e. number of VMs supported) of 17%, while imposing low overhead and scaling to as many as a thousand servers.

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cover image ACM Conferences
VEE '09: Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
March 2009
148 pages
ISBN:9781605583754
DOI:10.1145/1508293
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 11 March 2009

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Author Tags

  1. consolidation
  2. page sharing
  3. virtualization

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  • (2024)Meunik: Rethinking Virtual Machine Memory Resource Management for Unikernel-Based VMs2024 IEEE/ACM Symposium on Edge Computing (SEC)10.1109/SEC62691.2024.00030(292-305)Online publication date: 4-Dec-2024
  • (2023)A Comprehensive Study on Cloud Computing: Architecture, Load Balancing, Task Scheduling and Meta-Heuristic OptimizationIntelligent Cyber Physical Systems and Internet of Things10.1007/978-3-031-18497-0_11(137-162)Online publication date: 4-Feb-2023
  • (2021)An Approximation Algorithm for Sharing-Aware Virtual Machine Revenue MaximizationIEEE Transactions on Services Computing10.1109/TSC.2017.278672814:1(1-15)Online publication date: 1-Jan-2021
  • (2021)Speeding Up VM Startup by Cooperative VM Image CachingIEEE Transactions on Cloud Computing10.1109/TCC.2018.27915099:1(360-371)Online publication date: 1-Jan-2021
  • (2020)Scheduling Live Migration of Virtual MachinesIEEE Transactions on Cloud Computing10.1109/TCC.2017.27542798:1(282-296)Online publication date: 1-Jan-2020
  • (2019)CoWLightProceedings of the 8th International Workshop on Hardware and Architectural Support for Security and Privacy10.1145/3337167.3337170(1-8)Online publication date: 23-Jun-2019
  • (2019)Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-StudyIEEE Access10.1109/ACCESS.2019.29444207(141868-141887)Online publication date: 2019
  • (2019)Constraint-aware VM placement in heterogeneous computing clustersCluster Computing10.1007/s10586-019-02966-6Online publication date: 1-Aug-2019
  • (2018)SLA Management For Virtual Machine Live Migration Using Machine Learning with Modified Kernel and Statistical ApproachEngineering, Technology & Applied Science Research10.48084/etasr.16928:1(2459-2463)Online publication date: 20-Feb-2018
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