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.
Structural health monitoring (SHM) is an active area of research devoted to systems that can autonomously and proactively assess the structural integrity of bridges, buildings, and aerospace vehicles. Recent technological advances promise the eventual ability to cover a large civil structure with low-cost wireless sensors that can continuously monitor a building's structural health, but researchers face several obstacles to reaching this goal, including high data-rate, data-fidelity, and time-synchronization requirements. This article describes two systems the authors recently deployed in real-world structures.
It is currently difficult to build practical and reliable programming systems out of distributed and resource-constrained sensor devices. The state of the art in today's sensornet programming is centered around a component-based language called nesC. nesC is a nodelevel language-a program is written for an individual node in the network-and nesC programs use the services of an operating system called TinyOS. We are pursuing an approach to programming sensor networks that significantly raises the level of abstraction over this practice. The critical change is one of perspective: rather than writing programs from the point of view of an individual node, programmers implement a central program that conceptually has access to the entire network. This approach pushes to the compiler the task of producing node-level programs that implement the desired behavior.We present the Pleiades programming language, its compiler, and its runtime. The Pleiades language extends the C language with constructs that allow programmers to name and access node-local state within the network and to specify simple forms of concurrent execution. The compiler and runtime system cooperate to implement Pleiades programs efficiently and reliably. First, the compiler employs a novel program analysis to translate Pleiades programs into message-efficient units of work implemented in nesC. The Pleiades runtime system orchestrates execution of these units, using TinyOS services, across a network of sensor nodes. Second, the compiler and runtime system employ novel locking, deadlock detection, and deadlock recovery algorithms that guarantee serializability in the face of concurrent execution. We illustrate the readability, reliability and efficiency benefits of the Pleiades language through detailed experiments, and demonstrate that the Pleiades implementation of a realistic application performs similar to a hand-coded nesC version that contains more than ten times as much code.
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