Virtual machine (VM) live migration is a critical feature for managing virtualized environments, enabling dynamic load balancing, consolidation for power management, preparation for planned maintenance, and other management features. However, not all virtual machine live migration is created equal. Variants include memory migration, which relies on shared backend storage between the source and destination of the migration, and storage migration, which migrates storage state as well as memory state. We have developed an automated testing framework that measures important performance characteristics of live migration, including total migration time, the time a VM is unresponsive during migration, and the amount of data transferred over the network during migration. We apply this testing framework and present the results of studying live migration, both memory migration and storage migration, in various virtualization systems including KVM, XenServer, VMware, and Hyper-V. The results provide important data to guide the migration decisions of both system administrators and autonomic cloud management systems.
Due to a recent surge of student interest in the field of Voice over IP (VoIP) communications, new and innovative methods were required to be employed in order to keep pace with the increasing enrollment in the Voice Communications course offered at the State University of New York Institute of Technology. The traditional Voice Communications laboratory setup was obsolete and created a bottleneck hindering the students' capability to learn due to increasing class sizes. Under the previous setting, students were required to work in large groups on two shared servers in order to gain hands-on experience. This inevitably caused students to receive unequal portions of hands-on time with the allocated resources. To remedy the aforementioned issues, a centralized virtualization approach was proposed and implemented.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.
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