Computing resource transformation, consolidation and decomposition in hybrid clouds

With the promise of providing flexible and elastic computing resources on demand, the cloud computing has been attracting enterprises and individuals to migrate workloads in the legacy environment to the public/private/hybrid clouds. Also, cloud customers want to migrate between cloud providers with different requirements such as cost, performance, and manageability. However the workload migration is often interpreted as an image migration or re-installation/data copying as the exact snapshot of the source machine. Also the various cloud platforms and service models are rarely taken into consideration during the migration analytics. Therefore, although the expectation has risen with various requirements on the target cloud platforms and environments, the cloud migration techniques have not provided enough options that can satisfy the various requirements. In this paper we propose a model to tackle the migration challenges that transform one resource into same or another resource in hybrid clouds. We formulate the problem as a constraint satisfaction problem, and iteratively decompose the server components and consolidate the servers. The ultimate goal is to recommend the optimal target cloud platform and environment with the minimum cost. Through the evaluation of the proposed model using the real enterprise dataset (up to 2012 machines), we prove that the proposed model satisfies the goal. We show that when migrating into virtualized cloud environments, the thorough resource planning can reduce 16% of current resources, about 5%-10% servers can be consolidated, and more than 60% servers are possible candidates for server decomposition.