-One of the mechanisms to achieve energy efficiency in virtualized environments is to consolidate the workload (virtual machines) of underutilized servers and to switch-off these servers all together. Similarly, the workloads of overloaded servers can be distributed onto other servers for a load balancing reason. Central to this approach is the migration of virtual machines at runtime, which may introduce its own overhead in terms of energy consumption and service execution latency. This paper experimentally investigates the magnitude of this overhead. We use the Kernel-based Virtual Machine (KVM) hypervisor and a custom-made benchmark for our experiments. We will demonstrate that the workload of a virtual machine does not have any bearing on the power consumption of the destination server during migration but it has on the source server. Moreover, the available network bandwidth and the size of the virtual machine do indeed introduce a non-negligible energy overhead and migration latency on both the source and the destination server.
Abstract-For service management systems the early recognition of situations that necessitate a rebinding or a migration of services is an important task. To describe these situations on differing levels of detail and to allow their recognition even if only incomplete information is available, we employ the ontology language OWL 2 and the reasoning services defined for it. In this paper we provide a case study on the performance of state of the art OWL 2 reasoning systems for answering class queries and conjunctive queries modeling the relevant situations for service rebinding or migration in the differing OWL 2 profiles.
Abstract-We experimentally investigate the mutual influence of application-and platform-level adaptations in a virtualized cluster environment. At the application level, applications can adapt to a changing execution environment by dynamically exchanging components that enable them to trade energy for utility and vice versa. Likewise, at the platform level, virtual machine monitors can migrate virtual machines from one server to another either to consolidate workloads and switch-off underutilized servers or to distribute the workload of overloaded servers. Our experiment quantify impacts of various types of adaptations on QoS, power consumption, and energy-overhead.
Workload consolidation is a technique applied to achieve energy efficiency in data centres. It can be realized via live migration of virtual machines (VMs) between physical servers with the aim to power off idle servers and thus, save energy. In spite of innumerable benefits, the VM migration process introduces additional costs in terms of migration time and the energy overhead. This paper investigates the influence of workload as well as interference effects on the migration time of multiple VMs. We experimentally show that the migration time is proportional to the volume of memory copied between the source and the destination machines. Our experiment proves that the VMs, which run simultaneously on the physical machine compete for the available resources, and thus, the interference effects that occur, influence the migration time. We migrate multiple VMs in all possible permutations and investigate into the migration times. When the goal is to power off the source machine it is better to migrate memory intensive VMs first. Kernel-based Virtual Machine (KVM) is used as a hypervisor and the benchmarks from the SPEC CPU2006 benchmark suite are utilized as the workload.
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