Predicting the data transfer throughput of cloud networks plays an important role in several resource optimization applications, such as auto-scaling, replica selection, and load balancing. However, constant short-term variations in cloud networks make the prediction of end-to-end data transfer throughput a very challenging task. The parameters that affect the throughput can be categorized into three different areas: end-system characteristics (e.g., disk I/O bandwidth, CPU utilization), network characteristics (e.g., network bandwidth, latency, background traffic, bandwidth shaping mechanisms), and dataset characteristics (e.g., average file size, dataset size). Although there are promising results in the literature using neural networks, the datasets are collected from network layer devices and memory-to-memory data transfers where end-system and dataset characteristics are not considered as part of the problem. Also, very few studies use multivariate time series data collected from cloud networks, and the variables differ from study to study. In this project, we collected multivariate time series data from Amazon Web Services (AWS) by conducting intra- and inter-region transfers between storage systems and compute resources using monitoring services. This dataset is unique in the sense that end-system metrics in addition to network throughput are collected from both source and destination systems. Different average file size, instance type, and regionality parameters provide various settings, making the dataset applicable to various types of prediction models.

This dataset is about book series and is filtered where the books is Real-time processor architectures for worst case execution time reduction. It has 9 columns such as book series, earliest publication date, latest publication date, avg publication date, and number of authors. The data is ordered by earliest publication date.

CPU hours, institutions, and PI's by year.

Dataset containing real-world and synthetic samples on legit and malware samples in the form of time series. The samples consider machine-level performance metrics: CPU usage, RAM usage, number of bytes read and written from and to disk and network. Synthetic samples are generated using a GAN.

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Project and allocation data from XDCDB.

This repository contains the datasets used in Mirzadeh et al., 2023. It includes three InSAR time-series datasets from the Envisat descending orbit, ALOS-1 ascending orbit, and Sentinel-1A in ascending and descending orbits, acquired over the Abarkuh Plain, Iran, as well as the geological map of the study area and the GNSS and hydrogeological data used in this research.

Dataset 1: Envisat descending track 292

• Date: 06 Oct 2003 - 05 Sep 2005 (12 acquisitions)
• Processor: ISCE/stripmapStack + MintPy
• Displacement time-series (in HDF-EOS5 format): timeseries_LOD_tropHgt_ramp_demErr.h5
• Mean LOS Velocity (in HDF-EOS5 format): velocity.h5
• Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5
• Geometry (in HDF-EOS5 format): geometryRadar.h5

Dataset 2: ALOS-1 ascending track 569

• Date: 06 Dec 2006 - 17 Dec 2010 (14 acquisitions)
• Processor: ISCE/stripmapStack + MintPy
• Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5
• Mean LOS Velocity (in HDF-EOS5 format): velocity.h5
• Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5
• Geometry (in HDF-EOS5 format): geometryRadar.h5

Dataset 2: Sentinel-1 ascending track 130 and descending track 137

• Date: 14 Oct 2014 - 28 Mar 2020 (129 ascending acquisitions) + 27 Oct 2014 - 29 Mar 2020 (114 descending acquisitions)
• Processor: ISCE/topsStack + MintPy
• Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5
• Mean LOS Velocity (in HDF-EOS5 format): velocity.h5
• Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5
• Geometry (in HDF-EOS5 format): geometryRadar.h5

The time series and Mean LOS Velocity (MVL) products can be georeferenced and resampled using the makTempCoh and geometryRadar products and the MintPy commands/functions.

No description is available. Visit https://dataone.org/datasets/sha256%3A8b9b600f61bbd7d944013b78645ce2bb2494d735129ab86e43ba55f51657d613 for complete metadata about this dataset.

The Finite-Difference Time-Domain (FDTD) method is a popular numerical modelling technique in computational electromagnetics. The volumetric nature of the FDTD technique means simulations often require extensive computational resources (both processing time and memory). The simulation of Ground Penetrating Radar (GPR) is one such challenge, where the GPR transducer, subsurface/structure, and targets must all be included in the model, and must all be adequately discretised. Additionally, forward simulations of GPR can necessitate hundreds of models with different geometries (A-scans) to be executed. This is exacerbated by an order of magnitude when solving the inverse GPR problem or when using forward models to train machine learning algorithms.

We have developed one of the first open source GPU-accelerated FDTD solvers specifically focussed on modelling GPR. We designed optimal kernels for GPU execution using NVIDIA’s CUDA framework. Our GPU solver achieved performance throughputs of up to 1194 Mcells/s and 3405 Mcells/s on NVIDIA Kepler and Pascal architectures, respectively. This is up to 30 times faster than the parallelised (OpenMP) CPU solver can achieve on a commonly-used desktop CPU (Intel Core i7-4790K). We found the cost-performance benefit of the NVIDIA GeForce-series Pascal-based GPUs – targeted towards the gaming market – to be especially notable, potentially allowing many individuals to benefit from this work using commodity workstations. We also note that the equivalent Tesla-series P100 GPU – targeted towards data-centre usage – demonstrates significant overall performance advantages due to its use of high-bandwidth memory. The performance benefits of our GPU-accelerated solver were demonstrated in a GPR environment by running a large-scale, realistic (including dispersive media, rough surface topography, and detailed antenna model) simulation of a buried anti-personnel landmine scenario.

The previous version of this program (AFBG_v1_0) may be found at http://dx.doi.org/10.1016/j.cpc.2016.08.020.

Autonomous Underwater Vehicle (AUV) Monterey Bay Time Series from Feb 2016. This data set includes CTD and fluorometer data from the Makai AUV, as context for ecogenomic sampling using an onboard Environmental Sample Processor (ESP).

Average of Hash Rate and of Power Consumption over time.

CPU time for different values of α.

Scale of importance.

Ratio index for different number of criteria.

Parameters for alternatives.

Results of rank sum Wilcoxon test (same CPU time considered for both algorithms).