Virtualization is often used in cloud computing platforms for its several advantages in efficiently managing resources. However, virtualization raises certain additional challenges, and one of them is lack of power metering for virtual machines (VMs). Power management requirements in modern data centers have led to most new servers providing power usage measurement in hardware and alternate solutions exist for older servers using circuit and outlet level measurements. However, VM power cannot be measured purely in hardware. We present a solution for VM power metering, named Joulemeter. We build power models to infer power consumption from resource usage at runtime and identify the challenges that arise when applying such models for VM power metering. We show how existing instrumentation in server hardware and hypervisors can be used to build the required power models on real platforms with low error. Our approach is designed to operate with extremely low runtime overhead while providing practically useful accuracy. We illustrate the use of the proposed metering capability for VM power capping, a technique to reduce power provisioning costs in data centers. Experiments are performed on server traces from several thousand production servers, hosting Microsoft's realworld applications such as Windows Live Messenger. The results show that not only does VM power metering allow virtualized data centers to achieve the same savings that non-virtualized data centers achieved through physical server power capping, but also that it enables further savings in provisioning costs with virtualization.

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There has been a recent industrial effort to develop multi-resource hierarchical schedulers. However, the existing implementations have some shortcomings in that they might leave resources unallocated or starve certain jobs. This is because the multi-resource setting introduces new challenges for hierarchical scheduling policies. We provide an algorithm, which we implement in Hadoop, that generalizes the most commonly used multi-resource scheduler, DRF [1], to support hierarchies. Our evaluation shows that our proposed algorithm, H-DRF, avoids the starvation and resource inefficiencies of the existing open-source schedulers and outperforms slot scheduling.

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Commercial buildings consume nearly 19% of delivered energy in the U.S, nearly half (42%) of which is consumed in buildings with digital control systems [23] comprised of wired sensor networks. These sensors have scant metadata, and are represented by "tags" which are obscure, buildingspecific and not machine parseable. We develop a human-inthe-loop synthesis technique which uses syntactic and datadriven steps to parse these sensor tags into a common namespace, which can enable portable building applications. We show that our technique allows an expert to fully parse a large fraction (70%) of the tags with 24, 15 and 43 examples for three large commercial buildings comprising 1586, 2522 and 1865 sensors respectively, and deploy three portable applications on two buildings with less than 30 examples.