Energy consumption of data centers has been in-creasing continuously during the last years due to rising demands of computational power especially in current Grid- and Cloud-Computing systems. One promising approach of reducing this energy consumption is the consolidation of servers by virtualization. Many low loaded computer systems are virtualized and run on few physical servers for reducing the number of energy-consuming computers. At present this consolidation is usually done statically, thus, the administrator of a data center manually migrates many virtual machines with low load onto one physical server which may lead to overloading when the workload is rising unexpectedly. Dynamic server migration that adapts the number of running physical machines to the current workload overcomes these problems. Physical machines can be highly loaded and in case of further rising load virtual machines are migrated to other physical server systems that have been switched on. Such dynamic load aggregation approaches are rarely used and typically only consider few criteria for migration. This paper presents a classification of migration criteria for live migration of virtual machines in load aggregation environments and proposes an algorithm for combining many different kinds of migration criteria to a clustering-based metric. Thus, the novel load aggregation algorithm optimizes energy consumption as well as other migration criteria like runtime performance of applications.
['Daniel Versick', 'Djamshid Tavangarian']
Reducing Energy Consumption by Load Aggregation with an Optimized Dynamic Live Migration of Virtual Machines