Abstract-With the ability to move virtual machines between physical hosts, live migration is a core feature of virtualisation. However for migration to be useful, deployable feature on a large (datacentre) scale, we need to predict migration times with accuracy. In this paper, we characterise the parameters affecting live migration with particular emphasis on the Xen virtualisation platform. We discuss the relationships between the important parameters that affect migration and highlight how migration performance can vary considerably depending on workload. We further provide 2 simulation models that are able to predict migration times to within 90% accuracy for both synthetic and real-world benchmarks.

ASSESSING THE QUALITY or validity of a piece of data is not usually done in isolation. You typically examine the context in which the data appears and try to determine its original sources or review the process through which it was created. This is not so straightforward when dealing with digital data, however: the result of a computation might have been derived from numerous sources and by applying complex successive transformations, possibly over long periods of time.As the quantity of data that contributes to a particular result increases, keeping track of how different sources and transformations are related to each other becomes more difficult. This constrains the ability to answer questions regarding a result's history, such as: What were the underlying assumptions on which the result is based? Under what conditions does it remain valid? What other results were derived from the same data sources?The metadata that needs to be systematically captured to answer those (or similar) questions is called provenance (or lineage) and refers to a graph describing the relationships among all the elements (sources, processing steps, contextual information and dependencies) that contributed to the existence of a piece of data.This article presents current research in this field from a practical perspective, discussing not only existing systems and the fundamental concepts needed for using them in applications today, but also future challenges and opportunities. A number of use cases illustrate how provenance might be useful in practice.Where does data come from? Consider the need to understand the conditions, parameters, or assumptions behind a given result-in other words, the ability to point at a piece of data, for example, research result or anomaly in a system trace, and ask: Where did it come from? This would be useful for experiments involving digital data (such as in silico experiments in biology, other types of numerical simulations, or system evaluations in computer science).The provenance for each run of such experiments contains the links between results and corresponding starting conditions or configuration parameters. This becomes important especially when considering processing pipelines, where some early results serve as the basis of further experiments. Manually tracking all the parameters from a final result through intermediary data and to original sources is burdensome and error-prone.Of course, researchers are not the only ones requiring this type of tracking. The same techniques could be used to help people in the business or financial sectors-for example, figuring out the set of assumptions behind the statistics reported to a board of directors, or determining which mortgages were part of a traded security.

The increasing storage capacity and necessary redundancy of data centers and other large-scale IT facilities has drawn attention to the issue of reducing the power consumption of hard drives. This work comprehensively investigates the power consumption of hard drives to determine typical runtime power profiles. We have instrumented at a fine-grained level and present our findings which show that (i) the energy consumed by the electronics of a drive is just as important as the mechanical energy consumption; (ii)the energy required to access data is affected by physical location on a drive; and (iii) the size of data transfers has measurable effect on power consumption.

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Abstract-This paper quantifies the energy consumption in six 10 Gbps and four 1 Gbps interconnects at a fine-grained level, introducing two metrics for calculating the energy efficiency of a network interface from the perspective of network throughput and host CPU usage. It further compares the energy efficiency of multiport 1 Gbps to 10 Gbps interconnects.