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Does Live Migration of Virtual Machines cost Energy?
"... Abstract—Live migration, the process of moving a virtual machine (VM) interruption-free between physical hosts is a core concept in modern data centers. Power management strategies use live migration to consolidate services in a cluster environment and switch off underutilized machines to save power ..."
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Abstract—Live migration, the process of moving a virtual machine (VM) interruption-free between physical hosts is a core concept in modern data centers. Power management strategies use live migration to consolidate services in a cluster environment and switch off underutilized machines to save power. However, most migration models do not consider the energy cost of migration. This paper experimentally investigates the power consumption and the duration of virtual machine migration. We use the KVM platform for our experiment and show that live migration entails an energy overhead and the size of this overhead varies with the size of the virtual machine and the available network bandwidth. I.
Investigation into the Energy Cost of Live Migration of Virtual Machines
"... Abstract—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 l ..."
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Abstract—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. Index Terms—virtual machine,live virtual machine migration,migration time,migration cost,power consumption,energy overhead,workload types,energyefficient computing. I.
scope and usefulness of Dynamic Voltage and Frequency Scaling
"... Abstract—In this paper, we experimentally investigate the ..."
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Mutual Influence of Application- and Platform-Level Adaptations on Energy-Efficient Computing
"... 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 ex-changing components that enable them to trade energy ..."
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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 ex-changing 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. Keywords—Adaptation, cloud computing, energy-efficient com-puting, virtualization, virtual machines migration, migration costs I.
1 Power-Latency Trade-offs in Virtualized Environments
"... Abstract—The adoption of server virtualization and cloud computing has enabled high flexibility of service execution in the Internet. It also promises the efficient use of resources including power. At present, the cloud infrastructure (physical machines and cloud platforms) and the services employi ..."
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Abstract—The adoption of server virtualization and cloud computing has enabled high flexibility of service execution in the Internet. It also promises the efficient use of resources including power. At present, the cloud infrastructure (physical machines and cloud platforms) and the services employing the infrastructure are managed by independent entities. As a result, it is difficult to jointly configure hardware and software resources, which may introduce significant inefficiency of resource utilization. Often infrastructure providers over provision resources to accommodate a growing demand, but the cost of such inefficiency is gradually being felt by both parties. This paper experimentally examines the effect of system configuration (concurrency) on the power consumption and latency of a video hosting server. We find that the usefulness of concurrency is greatly influenced by the interplay of underlying leased resources and by the interaction of virtual machines with these resources. However, the exact nature of this interplay is difficult to quantitatively establish and, therefore, it is not presented to service providers. Our study encourages the scientific community to pay attention to this aspect and to undertake a more rigorous investigation based on practical observations. Index Terms—Concurrency, parallel programs, power consumption, server power consumption, processor power consumption, virtual machines, latency, performance, virtualized environment I.
A Probabilistic Model for Estimating the Power Consumption of Processors and Network Interface Cards
"... Abstract—Many of the proposed mechanisms aiming to achieve energy-aware adaptations in server environments rely on the existence of models that estimate the power consumption of the server as well as its individual components. Most existing or proposed models employ performance (hardware) monitoring ..."
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Abstract—Many of the proposed mechanisms aiming to achieve energy-aware adaptations in server environments rely on the existence of models that estimate the power consumption of the server as well as its individual components. Most existing or proposed models employ performance (hardware) monitoring counters and the CPU utilization to estimate power consumption, but they do not take into account the statistics of the workload the server processes. In this paper we propose a lightweight probabilistic model that can be used to estimate the power consumption of the CPU, the network interface card (NIC), and the server as a whole. We tested the model’s accuracy by executing custommade benchmarks as well as standard benchmarks on two heterogeneous server platforms. The estimation error associated with our model is less than 1 % for the custom-made benchmark whereas it is less than 12 % for the standard benchmark. Index Terms—Power consumption model, stochastic model, server power consumption, processor power consumption, NIC power consumption, probability distribution function, random variable I.