The peak power consumption of hardware components affects their powersupply, packaging, and cooling requirements. When the peak power consumption is high, the hardware components or the systems that use them can become expensive and bulky. Given that components and systems rarely (if ever) actually require peak power, it is highly desirable to limit power consumption to a less-than-peak power budget, based on which power supply, packaging, and cooling infrastructure scan be more intelligently provisioned.

Large volumes of data are generated every day by experiments, simulations and all sorts of applications. It is common to observe situations where portions of data are irregularly replicated and distributed in different data sources. It would be desirable to be able to handle these several pieces of irregular data (replicated or not) as a unique large dataset. This is called data virtualization and is the focus of this paper. In this paper, we present a system which is capable of dealing with irregularly replicated data and is able to create a virtual view of the union of the individual irregular portions of data hosted by each data source. Our system indexes the data intervals from each data source and allows clients to submit queries against the virtual dataset created. In order to select what server will be responsible for each data interval of a query, we use and compare three algorithms, namely Random, Round-Robin and Weighted Round-Robin. The comparison is driven by simulation and the parameters for the simulation are all taken from a real data-centered application (the Virtual Microscope).

The previous research on cluster-based servers has focused on homogeneous systems. However, real-life clusters are almost invariably heterogeneous in terms of the performance, capacity, and power consumption of their hardware components. In this paper, we argue that designing efficient servers for heterogeneous clusters requires defining an efficiency metric, modeling the different types of nodes with respect to the metric, and searching for request distributions that optimize the metric. To concretely illustrate this process, we design a cooperative Web server for a heterogeneous cluster that uses modeling and optimization to minimize the energy consumed per request. Our experimental results for a cluster comprised of traditional and blade nodes show that our server can consume 42 % less energy than an energy-oblivious server, with only a negligible loss in throughput. The results also show that our server conserves 45 % more energy than an energy-conscious server that was previously proposed for homogeneous clusters. 1