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virt-similarity: Find clusters of similar/cloned virtual machines Copyright (C) 2013 Red Hat Inc. ====================================================================== Compiling from source --------------------- If checking out from git, then: autoreconf -i Build it: ./configure make Optionally: sudo make install Requirements ------------ - ocaml >= 3.12.0 - ocaml findlib - libguestfs >= 1.14 - ocaml libguestfs bindings Developers ---------- The upstream git repo is: http://git.annexia.org/?p=virt-similarity.git;a=summary Please send patches to the virt-tools mailing list: http://www.redhat.com/mailman/listinfo/virt-tools-list Notes on the technique used --------------------------- (1) We use libguestfs to open each disk image. This allows us to get at the raw data, in case the disk image is stored in some format like qcow2 or vmdk. Also you could extend this program so it could understand encrypted disks. http://libguestfs.org/ http://libguestfs.org/guestfs-java.3.html (2) For each disk, we split it into 64K blocks and hash each block. The reason for choosing 64K blocks is that it's the normal cluster size for qcow2, and the block size used by qemu-img etc. The reason for doing the hashing is so that we can compare the disk images for similarity by holding the complete set of hashes in memory. The hashing reduces each disk by a factor of 4096 (MD5) or 2048 (SHA-256) times, so for example a 10 GB disk image is reduced to a more manageable 2.5 or 5 MB. NB: For speed the hashes are saved in a cache file called 'similarity-cache' in the local directory. You can just delete this file when done. (3) We then compare each disk image, block by block, and record the difference between each pair of images. Note that we DON'T do advanced Cluster Analysis on the disk images. There's not any point since the rebasing operation used by qemu-img can only handle simple differences at the block level; it cannot, for example, move blocks around or do fuzzy matches. http://en.wikipedia.org/wiki/Cluster_analysis (4) We then output a tree (technically a 'Cladogram') showing a hierarchy of guests, using a simple hierarchical clustering algorithm, where we group the two closest guests, then that group with the next closest guest, and so forth. http://en.wikipedia.org/wiki/Cladogram http://en.wikipedia.org/wiki/Hierarchical_clustering