Homogeneous and Automated Migration of Virtual Machines Between Multiple Public Clouds - Xavier, Marcus Rafael; Sette, Ioram Schechtman; Ferraz, Carlos (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia), 2022)
The objective of this work is to analyze the required steps for automated migration of Virtual Machines (VMs) using a proposed solution, called Kumo, through scenarios using public clouds, such as Amazon Web Services (AWS), Microsoft Azure (AZ) and Google Cloud Platform (GCP). A performance evaluation is carried out considering the Total Migration Time (TTM) metric between homogeneous and heterogeneous clouds. Among the homogeneous scenarios, which are those in which the source and destination clouds are from the same provider, but in different data centers, the best result occurred in migrations between Azure clouds, with average TTM of 45m59s. For heterogeneous, the best scenario was the GCP-AWS migration, with TTM of 45m56s. The nine steps for the automated migration of VMs were analyzed, showing that five of them combined significantly impacted, between 94.01% and 99.44%, the TTM of the 9 scenarios tested.