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4 results for “spot cloud”
OptiSpot: Minimizing Application Deployment Cost using Spot Cloud Resources
<p>1. Attached files: </p> <p>This archive contains 1800 MATLAB files, each one containing the results of a single experiment.<br /> The name of each file follows the following format:</p> <p> A_B_C_D_E_F_G.mat</p> <p>Where the fields A, B, C, D, E, F, and G are described as follows.</p> <p>A: number of users.<br /> Considered values are: 1000, 2000, 5000, 10000.</p> <p>B: maximum response time in milliseconds.<br /> Considered values are: 60, 80, 100, 200.</p> <p>C: overbid time cap in hours.<br /> Considered values are: 5, 20, 80, 0 (note: 0 is a code used to express infinite hours).</p> <p>D: Amazon region.<br /> Considered values are: us-east, eu-west.</p> <p>E: Operating system.<br /> Considerede values are: Windows, Linux.</p> <p>F: Optimization algorithm.<br /> Considered values are: heuristic (which is OptiSpot), fmincon.</p> <p>G: Experiment seed.<br /> Considered values are from 1 to 30</p> <p>2. Data format:</p> <p>MATLAB data format, can be loaded from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</p>
Autonomic Provisioning and Application Mapping on Spot Cloud Resources
<p>1. Attached files: </p> <p>500_100_fmincon_0.95_1.mat <br /> Experiment with 500 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>500_100_heuristic_0.95_1.mat<br /> Experiment with 500 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2000_70_fmincon_0.95_1.mat<br /> Experiment with 2000 users, 70ms max response time, 95% availability, exact algorithm.</p> <p>2000_70_heuristic_0.95_1.mat<br /> Experiment with 2000 users, 70ms max response time, 95% availability, our algorithm.</p> <p>2000_100_fmincon_0.9_1.mat<br /> Experiment with 2000 users, 100ms max response time, 90% availability, exact algorithm.</p> <p>2000_100_heuristic_0.9_1.mat <br /> Experiment with 2000 users, 100ms max response time, 90% availability, our algorithm.</p> <p>2000_100_fmincon_0.95_1.mat <br /> Experiment with 2000 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>2000_100_heuristic_0.95_1.mat <br /> Experiment with 2000 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2000_100_fmincon_0.999_1.mat <br /> Experiment with 2000 users, 100ms max response time, 99.9% availability, exact algorithm.</p> <p>2000_100_heuristic_0.999_1.mat <br /> Experiment with 2000 users, 100ms max response time, 99.9% availability, our algorithm.</p> <p>2000_300_fmincon_0.95_1.mat <br /> Experiment with 2000 users, 300ms max response time, 95% availability, exact algorithm.</p> <p>2000_300_heuristic_0.95_1.mat <br /> Experiment with 2000 users, 300ms max response time, 95% availability, our algorithm.</p> <p>10000_100_fmincon_0.95_1.mat <br /> Experiment with 10000 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>10000_100_heuristic_0.95_1.mat <br /> Experiment with 10000 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2. Data format:</p> <p>MATLAB data format, can be load from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</p>
Metabarcoding for biodiversity inventory blind spots: A test case using the beetle fauna of an insular cloud forest
<p>Soils harbour a rich arthropod fauna, but many species are still not formally described (Linnaean shortfall), and the distribution of those already described is poorly understood (Wallacean shortfall). Metabarcoding holds much promise to fill this gap, however, nuclear copies of mitochondrial genes, and other artefacts lead to taxonomic inflation, which compromises the reliability of biodiversity inventories. Here we explore the potential of a bioinformatic approach to jointly "denoise" and filter non-authentic mitochondrial sequences from metabarcode reads to obtain reliable soil beetle inventories and address open questions in soil biodiversity research, such as the scale of dispersal constraints in different soil layers. We sampled cloud forest arthropod communities from 49 sites in the Anaga peninsula of Tenerife (Canary Islands). We performed whole organism community DNA (wocDNA) metabarcoding, and built a local reference database with COI barcode sequences of 310 species of Coleoptera for filtering reads and the identification of metabarcoded species. This resulted in reliable haplotype data after considerably reducing nuclear mitochondrial copies and other artefacts. Comparing our results with previous beetle inventories, we found: (i) new species records, potentially representing undescribed species; (ii) new distribution records, and; (iii) validated phylogeographic structure when compared with traditional sequencing approaches. Analyses also revealed evidence for higher dispersal constraint within deeper soil beetle communities, compared to those closer to the surface. The combined power of barcoding and metabarcoding contribute to mitigate the important shortfalls associated with soil arthropod diversity data, and thus address unresolved questions for this vast biodiversity fraction.</p>
Metabarcoding for biodiversity inventory blind spots: A test case using the beetle fauna of an insular cloud forest
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