VMTorrent: scalable P2P virtual machine streaming
VMTorrent: scalable P2P virtual machine streaming
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DOI：
10.1145/2413176.2413210
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发表时间：
2012-12
期刊：
影响因子：
6.4
通讯作者：
Joshua Reich;Oren Laadan;E. Brosh;A. Sherman;V. Misra;Jason Nieh;D. Rubenstein
中科院分区：
文献类型：
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作者：
Joshua Reich;Oren Laadan;E. Brosh;A. Sherman;V. Misra;Jason Nieh;D. Rubenstein
云通常将虚拟机（VM）映像存储在网络存储上。这造成了严重的潜在可伸缩性瓶颈，因为启动单个新的VM实例至少需要数百MB的网络读取。由于此瓶颈在新VM的读取密集型启动期间发生得最严重，因此我们专注于可扩展地最小化靴子VM和加载其关键应用程序的时间。虽然有效的可伸缩P2P流技术的视频点播（VOD）的情况下，块到达顺序和恒定速率是可用的，没有技术解决可伸缩的大型可执行流。VM执行是不确定的、发散的、可变速率的，并且不能错过块。VMTORST引入了块优先级、基于配置文件的执行预取、按需获取以及VM映像表示与底层数据流的解耦的新颖组合。VMTORQRST提供了第一个完整的和有效的解决方案，这个日益增长的可扩展性问题，是基于更好地利用现有的容量，而不是扔更多的硬件在it.Supported分析建模，我们提出了全面的实验评估VMTORQRST的真实的系统的规模，证明了VMTORQRST的有效性。我们发现，VMTORST支持可比的执行时间，实现使用本地磁盘。VMTORST在扩展到100个实例的同时保持了这一性能，与当前最先进的技术相比，速度提高了11倍，与传统网络存储相比，速度提高了30倍。
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.