Energy sustainability indicators for urban environments

Sustainable development comprises of three dimensions. The three dimensions are the environment, the social and the economic. There have been many indicators used to measure the three dimensions of sustainability. For example air pollution, consumption of natural resources, quality of open space, noise, equity and opportunities and economic benefits from transport and land use. Urban areas constitute the most crucial factor in the sustainability. Urban systems affect and are affected by natural systems beyond their physical boundaries and in general the interdependence between the urban system and the regional and global environment is not reflected in urban decision making. The use of energy in the urban system constitutes the major element in the construction and function of urban areas. Energy impacts across the boundaries of the three dimensions of sustainability. The objective of this research is to apply energy-use-indicators to the urban system as a measure of sustainability. This methodology is applied to a case study in the United Kingdom.

A constraints-based resource discovery model for multi-provider cloud environments

Increasingly infrastructure providers are supplying the cloud marketplace with storage and on-demand compute resources to host cloud applications. From an application user's point of view, it is desirable to identify the most appropriate set of available resources on which to execute an application. Resource choice can be complex and may involve comparing available hardware specifications, operating systems, value-added services, such as network configuration or data replication, and operating costs, such as hosting cost and data throughput. Providers' cost models often change and new commodity cost models, such as spot pricing, have been introduced to offer significant savings. In this paper, a software abstraction layer is used to discover infrastructure resources for a particular application, across multiple providers, by using a two-phase constraints-based approach. In the first phase, a set of possible infrastructure resources are identified for a given application. In the second phase, a heuristic is used to select the most appropriate resources from the initial set. For some applications a cost-based heuristic is most appropriate; for others a performance-based heuristic may be used. A financial services application and a high performance computing application are used to illustrate the execution of the proposed resource discovery mechanism. The experimental result shows the proposed model could dynamically select an appropriate set of resouces that match the application's requirements.

Low temperature conditions in building sandstone: the role of extreme events in temperate environments

Data on rock temperatures has previously been collected to characterise typical diurnal regimes, and more recently to describe short-term variability in extreme locations. However, there is also the case that little is understood concerning the impact of extreme events in otherwise temperate environments. Internal stone temperatures (5?cm) collected during the atypical cold extreme experienced, throughout the UK, in December 2010 show a difference between ambient air temperatures and aspect-related thermal differences, particularly concerning temperature lows and the influence of radiative heating. In this case, debris release was not visible; however, laboratory simulations have shown that under such conditions, surface loss does not necessarily negate the occurrence of internal stone modifications. This preparatory sequence of change demonstrates that surface loss is not the result of one process, but rather many operating over time to sufficiently decrease stone strength to facilitate obvious damage.

The Klondike goldfields and Pleistocene environments of Beringia

The Klondike goldfields of Yukon, Canada, contain a key record of Pleistocene Beringia, the region of Alaska, Siberia, and Yukon that remained largely unglaciated during the late Cenozoic. A concentration of mining exposures, with relict permafrost that is locally more than 700,000 years old, provides exceptional preservation of paleoenvironmental archives and a new perspective on the nature of paleoenvironments during the Pleistocene. A critical feature is the stratigraphic association of distal tephra beds with these paleoenvironmental archives, which facilitates their regional correlation and, in many cases, provides independent ages for the paleoenvironmental assemblages. Paleoenvironmental analyses of fossil arctic ground-squirrel middens and buried vegetation indicate the presence of cryoxerophilous ("steppe-tundra") vegetation growing on well-drained substrates with deep active layers (seasonal thaw depths) during cold intervals of the Pleistocene. Studies of full-glacial paleosols and cryostratigraphic relations of associated ground ice indicate the importance of active loess deposition and surface vegetation cover in maintaining the functionally distinct mammoth-steppe biome, which supported grazing mega-fauna populations, including mammoth, horse, and bison.

IO Performance Prediction in Consolidated Virtualized Environments

We propose a trace-driven approach to predict the performance degradation of disk request response times due to storage device contention in consolidated virtualized environments. Our performance model evaluates a queueing network with fair share scheduling using trace-driven simulation. The model parameters can be deduced from measurements obtained inside Virtual Machines (VMs) from a system where a single VM accesses a remote storage server. The parameterized model can then be used to predict the effect of storage contention when multiple VMs are consolidated on the same virtualized server. The model parameter estimation relies on a search technique that tries to estimate the splitting and merging of blocks at the the Virtual Machine Monitor (VMM) level in the case of multiple competing VMs. Simulation experiments based on traces of the Postmark and FFSB disk benchmarks show that our model is able to accurately predict the impact of workload consolidation on VM disk IO response times.