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VMTorrent: scalable P2P virtual machine streaming

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.