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VMTorrent: scalable P2P virtual machine streaming

Published: 10 December 2012 Publication History

Abstract

Clouds commonly store Virtual Machine (VM) images on networked storage. This poses a serious potential scalability bottleneck as launching a single fresh VM instance requires, at minimum, several hundred MB of network reads. As this bottleneck occurs most severely during read-intensive launching of new VMs, we focus on scalably minimizing time to boot a VM and load its critical applications.
While effective scalable P2P streaming techniques for Video on Demand (VOD) scenarios where blocks arrive in-order and at constant rate are available, no techniques address scalable large-executable streaming. VM execution is non-deterministic, divergent, variable rate, and cannot miss blocks. VMTORRENT introduces a novel combination of block prioritization, profile-based execution prefetch, on-demand fetch, and decoupling of VM image presentation from underlying data-stream. VMTORRENT provides the first complete and effective solution to this growing scalability problem that is based on making better use of existing capacity, instead of throwing more hardware at it.
Supported by analytic modeling, we present comprehensive experimental evaluation of VMTORRENT on real systems at scale, demonstrating the effectiveness of VMTORRENT. We find that VMTORRENT supports comparable execution time to that achieved using local disk. VMTORRENT maintains this performance while scaling to 100 instances, providing up to 11x speedup over current state-of-the-art and 30x over traditional network storage.

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cover image ACM Conferences
CoNEXT '12: Proceedings of the 8th international conference on Emerging networking experiments and technologies
December 2012
384 pages
ISBN:9781450317757
DOI:10.1145/2413176
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: 10 December 2012

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Author Tags

  1. P2P
  2. bittorrent
  3. cloud computing
  4. file systems
  5. on-demand
  6. swarming
  7. virtual appliances
  8. virtual machines

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Overall Acceptance Rate 198 of 789 submissions, 25%

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  • (2022)Fast Provisioning of Virtual Machine Based on Intelligent Virtual Machine Image Prefetching2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta)10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00120(778-787)Online publication date: Dec-2022
  • (2022)A Cache-Aware Virtual Machine Placement With Network Constraints in Large-Scale Network Emulation2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00069(285-294)Online publication date: Dec-2022
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  • (2019)Comparison between Chunk-Based and Layer-Based Container Image Storage Approaches: an Empirical Study2019 IEEE International Conference on Service-Oriented System Engineering (SOSE)10.1109/SOSE.2019.00034(197-1975)Online publication date: Apr-2019
  • (2019)On the Importance of Container Image Placement for Service Provisioning in the Edge2019 28th International Conference on Computer Communication and Networks (ICCCN)10.1109/ICCCN.2019.8846920(1-9)Online publication date: Jul-2019
  • (2019)A Fast Approach of Provisioning Virtual Machines by Using Image Content Similarity in CloudIEEE Access10.1109/ACCESS.2019.29075967(45099-45109)Online publication date: 2019
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