Autonomic Resource Management with Support Vector Machines
GRID '11 Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing
Cost-Aware and SLO-Fulfilling Software as a Service
Journal of Grid Computing
Virtual Machine Allocation in Cloud Computing Environment
International Journal of Cloud Applications and Computing
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Infrastructure as a Service providers use virtualization to abstract their hardware and to create a dynamic data center. Virtualization enables the consolidation of virtual machines as well as the migration of them to other hosts during runtime. Each provider has its own strategy to efficiently operate a data center. We present a rule based mapping algorithm for VMs, which is able to automatically adapt the mapping between VMs and physical hosts. It offers an interface where policies can be defined and combined in a generic way. The algorithm performs the initial mapping at request time as well as a remapping during runtime. It deals with policy and infrastructure changes. We extended the open source IaaS solution Eucalyptus and we evaluated it with typical policies: maximizing the compute performance and VM locality to achieve a high performance and minimizing energy consumption. The evaluation was done on state-of-the-art servers in our own data center and by simulations using a workload of the Parallel Workload Archive. The results show that our algorithm performs well in dynamic data centers environments.