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15 results for “cloud functions”
Empirical measurements of function placements and executions in a mixed cloud-edge cluster
<p>Empirical measurements used for the Skippy Scheduler, an optimized container scheduler for serverless edge computing in Kubernetes.</p>
Non-equilibrium fractionation during ice cloud formation in iCAM5: evaluating the common parameterization of supersaturation as a linear function of temperature
<p>This archive includes data and python scripts to create the figures in</p> <p>Duetsch, M., Blossey, P. N., Steig, E. J., and Nusbaumer, J. M. (2019). Non-equilibrium fractionation during ice cloud formation in iCAM5: evaluating the common parameterization of supersaturation as a linear function of temperature. Submitted to J. Adv. Model. Earth Sy.</p> <p><strong>Model</strong></p> <p>Model output data are saved in model.tar.gz</p> <p>For all simulations, monthly averages of the following variables are saved in h0 files (used for Figures 3, 6, 8, 9):<br> T: temperature (Si_real simulations only)<br> PS: surface pressure (Si_real simulations only)<br> U: zonal wind (Si_real simulations only)<br> V: meridional wind (Si_real simulations only)<br> LANDFRAC: fraction of surface area covered by land<br> PRECT_H2O: total precipitation rate for H2O<br> PRECT_HDO: total precipitation rate for HDO<br> PRECT_H218O: total precipitation rate for H218O<br> H2OV: H2O mixing ratio for vapor<br> HDOV: HDO mixing ratio for vapor<br> H218OV: H218O mixing ratio for vapor<br> H2OI: H2O mixing ratio for cloud ice<br> HDOI: HDO mixing ratio for cloud ice<br> H218OI: H218O mixing ratio for cloud ice<br> H2OL: H2O mixing ratio for cloud liquid<br> HDOL: HDO mixing ratio for cloud liquid<br> H218OL: H218O mixing ratio for cloud liquid</p> <p>For the control and Si_real microphysical sensitivity simulations, 6-hourly averages of the following variables are saved in h1 files (used for Figures 4, 5, 7):<br> PRECL_H2O: Large-scale precipitation rate for H2O (Si_real simulations only)<br> PRECL_HDO: Large-scale precipitation rate for HDO<br> PRECL_H218O: Large-scale precipitation rate for H218O<br> PRECL_SAT: Large-scale precipitation rate for Si tracer (Si_real simulations only)<br> PRECL_TMP: Large-scale precipitation rate for T tracer (Si_real simulations only)<br> PRECL_RVD: Large-scale precipitation rate for R^D tracer<br> PRECL_RVO: Large-scale precipitation rate for R^18O tracer</p> <p>To limit the size of the data set, only the first 5 years of the simulations are saved.</p> <p><strong>Measurements</strong></p> <p>Ice core measurements for present-day climate and last glacial maximum are saved in icecores_PD.txt and icecores_LGM.txt, respectively.</p> <p>LGM values are averaged from 19000 BCE to 16000 BCE, PD values are averaged from 1000 BCE to 2000 CE.</p> <p>Antarctic surface snow measurements from Masson-Delmotte et al. (2008) are available at https://doi.org/10.1594/PANGAEA.681697</p> <p><strong>Python scripts</strong></p> <p>Scripts run with python 3.6</p> <p>Required packages:<br> - numpy<br> - matplotlib<br> - netCDF4<br> - datetime<br> - Basemap<br> - copy<br> - intergrid<br> - cmocean</p>
Replication Package for: Streaming vs. Functions: A Cost Perspective on Cloud Event Processing
<p>In cloud event processing, data generated at the edge is processed in real-time by cloud resources. Both distributed stream processing (DSP) and Function-as-a-Service (FaaS) have been proposed to implement such event processing applications. FaaS emphasizes fast development and easy operation, while DSP emphasizes efficient handling of large data volumes. Despite their architectural differences, both can be used to model and implement loosely-coupled job graphs. In this paper, we consider the selection of FaaS and DSP from a cost perspective. We implement stateless and stateful workflows from the Theodolite benchmarking suite using cloud FaaS and DSP. In an extensive evaluation, we show how application type, cloud service provider, and runtime environment can influence the cost of application deployments and derive decision guidelines for cloud engineers.</p>
Data - A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces
<p>Data and scripts associated with the article "A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces"</p>
Functional and genetic diversity changes through time in a cloud forest ant assemblage
<p>Climate change in the Neotropics is causing upslope range shifts. We used arrays of ant species collected in a cloud forest at 1,500 m in Área de Conservación Guanacaste (ACG), northwestern Costa Rica, collected in two time periods (1998-2000 and 2008-2011) to measure changes in species richness and diversity over a decade. Using metrics of community structure, we found that the species assemblage in the collections from the 1990s were significantly phylogenetically clustered and functionally less diverse as compared to collections from the early 2000's. At both time points, the assemblages were significantly phylogenetically clustered and while the difference in functional diversity between the time points was not significant, the ant assemblage has become lighter in color (on average) over time. When individual species are considered the overall pattern of replacement is consistent with the cloud forest ant assemblage being colonized by arrivals from lower-elevation forests. The invertebrate communities on cloud forested mountain tops are especially vulnerable to a changing climate as there are two factors working together; no higher terrain to which they can move and the invasion of more and more taxa from lower downslope. This vulnerability is already measurable.</p>
Memory tracing, profiling and autotuning for containerised cloud functions
<p>This dataset contains CSV files with the raw measurements of 4x10 traces as well as scripts and reference data for distinguishing two usage profiles based on raw measurements of 2x20 traces. It accompanies the publication «Resource Management for Cloud Functions with Memory Tracing, Profiling and Autotuning» presented at WoSC6 @ Middleware 2020.</p>
Tracking climate vulnerability across spatial distribution and functional traits in Magnolia gentryi from the Peruvian tropical montane cloud forest
<p>Understanding the responses of tree species' functional traits to climate variability is essential for predicting the future of Tropical Montane Cloud Forest (TMCF) tree species through acclimation, especially in Andean montane environments where fog pockets act as moisture traps. We studied the distribution of <em>Magnolia gentryi</em> to measure its spatial arrangement and identify local hotspots, while also evaluating the extent to which climate-related factors are associated with its distribution. Finally, we analyzed variations in 13 functional traits of <em>M. gentryi</em> and the climate links to infer the shaping plant acclimate capacity. Our results show that Andean TMCF climatic factors constrain <em>M. gentryi</em> spatial distribution with significant patches or gaps, associated with high precipitation rates and mean minimum temperature. The functional traits of <em>M. gentryi</em> are constrained by Andean TMCF climatic factors, resulting in reduced within-species acclimation in functional traits associated with a hydric deficit. The association between functional traits and climate oscillation is crucial for understanding the growth conditions of relict-endemic species and is essential for conservation efforts. Changes in forest trait diversity and species composition occur because of fluctuations in hydraulic safety–efficiency gradients.</p>
A combination of morphological and photosynthetic functional traits maintains the vertical distribution of bryophytes in a subtropical cloud forest
<p><span>The distribution and performance of bryophyte species are known to vary with vertical gradients, due to changes in environmental factors, especially light conditions. However, the morphological and physiological drivers of bryophyte distribution along forest vertical gradients are poorly understood. </span></p> <p><span><span><b>Methods</b><b>:</b> We conducted a comparative analysis of 28 morphological and photosynthetic functional traits in 18 species of mosses and liverworts distributed among three vertical microhabitats (ground, tree trunk, and branch) to analyze trait variance among the microhabitats and bryophyte life–forms in a subtropical cloud forest in Ailao Mountain, Yunnan, southwest China. Principle component analysis (PCA) was used to summarize trait differences among bryophyte species.</span></span></p> <p><span><b>Key Results: </b></span><span>In contrast to trunk and ground dwellers, branch dwellers tend to reduce light interception (smaller leaf and cell sizes, lower chlorophyll content); protect against damage from intense irradiation (higher ratios of carotenoids to chlorophyll); raise light energy utilization (higher photosynthetic capacity) and cope with lower environmental moisture conditions (pendant life-forms, thicker cell wall). Principal component analysis showed that ecological strategies of bryophytes in response to levels of irradiation were specialized in branch–dwellers, although those in ground and trunk–dwelling species were less distinct.</span></p> <p><b>Conclusions:</b> Environmental filtering shaped the functional traits combination and spatial distribution of bryophytes along the vertical gradients. Bryophyte species from upper canopy of cloud forests show narrow variation in functional traits under intense light , whereas species in the lower vertical strata associated with low levels of light intensity exhibited contrasting, but more diverse ecological strategies.</p>
Tracking climate vulnerability across spatial distribution and functional traits in Magnolia gentryi from the Peruvian tropical montane cloud forest
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A combination of morphological and photosynthetic functional traits maintains the vertical distribution of bryophytes in a subtropical cloud forest
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Functional and genetic diversity changes through time in a cloud forest ant assemblage
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JSS Data from Function-specific Scheduling Policies in Cloud-Edge, Multi-Region Serverless Systems
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Updated JSS Data from Function-specific Scheduling Policies in Cloud-Edge, Multi-Region Serverless Systems
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Simulated function placements in predefined mixed cloud-edge clusters
<p>Experiments in a simulation environment to evaluate the placement quality of the Skippy Scheduler, an optimized container scheduler for serverless edge computing in Kubernetes.</p>
A long-term monthly dataset of cloud fraction over the Arctic based on multiple satellite products using cumulative distribution function matching and Bayesian maximum entropy
<p>The low accuracy of satellite Cloud fraction (CF) over the Arctic seriously restricts accurate assessment of regional and global radiant energy balance under the changing climate. Previous studies have reported that not a single satellite CF product could satisfy the needs of accuracy and spatio-temporal coverage simultaneously for long-term applications over the Arctic. Merging multiple CF products with complementary properties is an effective way to produce more spatiotemporally complete and accurate CF data record. This study proposed a spatiotemporal statistical data fusion framework based on cumulative distribution function (CDF) matching and Bayesian maximum entropy (BME) method to produce a syncretic 1°×1° CF dataset in the Arctic during 2000-2020. The original datasets contain CF from MOD08/MYD08, CERES-SSF Terra/Aqua, CLARA-A2 AM/PM, PATMOS-x AM/PM, ISCCP-H AM/PM. The fused CF product is more consistent with the active satellite data GEWEX-CALIPSO and the ground-based observation data CRU TS4.05.</p>
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.