Hybrid cloud computing, where private and public cloud resources are combined and applications can migrate freely, ushers in unprecedented flexibility for businesses. To unleash the benefits, commercial products already enable the live migration of full virtual machines between distant cloud datacenters.

Unfortunately, two problems exist. First, no {\em live migration progress management system} exists, leading to (1) guesswork over how long a migration might take and the inability to schedule dependent tasks accordingly; (2) unacceptable application degradations -- application components could become split over distant cloud datacenters for an arbitrary period during migration; (3) inability to balance application performance and migration time -- e.g. to finish migration later for less performance interference.

Second, multi-tier application architectures are widely employed in today's virtualized cloud computing environments. Although existing solutions are able to migrate a single VM efficiently, little attention has been devoted to migrating related VMs in multi-tier applications. Ignoring the relatedness of VMs during migration can lead to serious application performance degradation.

In this thesis, we design the first migration progress management system called Pacer. Pacer's techniques are based on robust and lightweight run-time measurements of system and workload characteristics, efficient and accurate analytic models for progress predictions, and online adaptation to maintain user-defined migration objectives for coordinated and timely migrations.

We formulates the multi-tier application migration problem, and presents a new communication-cost-driven coordinated approach, as well as a system called COMMA that realizes this approach. We experimentally show that using COMMA for the migration of a 3-tier application reduces the amount of inter-component communication impacted by migration by up to 475 times compared to naive parallel migration.