{"id":165140,"date":"2013-06-01T00:00:00","date_gmt":"2013-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/mig-efficient-migration-of-desktop-vms-using-semantic-compression\/"},"modified":"2018-10-16T21:36:45","modified_gmt":"2018-10-17T04:36:45","slug":"mig-efficient-migration-of-desktop-vms-using-semantic-compression","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mig-efficient-migration-of-desktop-vms-using-semantic-compression\/","title":{"rendered":"MiG: Efficient Migration of Desktop VMs using Semantic Compression"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We consider the problem of efficiently migrating desktop virtual machines. The key challenge is to migrate the desktop VM quickly and in a bandwidth-efficient manner. The idea of replaying computation to reconstruct state seems appealing. However, our detailed analysis shows that the match between the source memory and the memory reconstructed via replay at the destination is poor, even at the sub-page level; the ability to reconstruct memory state is stymied because modern OSes use address space layout randomization (ASLR) to improve security, and page prefetching to improve performance.<\/p>\n<p>Despite these challenges, we show that desktop VM memory state can be efficiently compressed for transfer without relying on replay, using a suite of semantic techniques \u2013 collectively dubbed as MiG \u2013 that are tailored to the type of each memory page. Our evaluation on Windows and Linux desktop VMs shows that MiG is able to compress the VM state effectively, requiring on average 51-65% fewer bytes to be transferred during migration compared to standard compression, and halving the migration time in a typical setting.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We consider the problem of efficiently migrating desktop virtual machines. The key challenge is to migrate the desktop VM quickly and in a bandwidth-efficient manner. The idea of replaying computation to reconstruct state seems appealing. However, our detailed analysis shows that the match between the source memory and the memory reconstructed via replay at the [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-165140","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"","msr_edition":"USENIX Annual Technical 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