Related papers: Data-Intensive Supercomputing in the Cloud: Global…
Infrastructure as a Service (IaaS) clouds have become the predominant underlying infrastructure for the operation of modern and smart technology. IaaS clouds have proven to be useful for multiple reasons such as reduced costs, increased…
Upcoming and future astronomy research facilities will systematically generate terabyte-sized data sets moving astronomy into the Petascale data era. While such facilities will provide astronomers with unprecedented levels of accuracy and…
Cloud-based overlays are often present in optical remote sensing images, thus limiting the application of acquired data. Removing clouds is an indispensable pre-processing step in remote sensing image analysis. Deep learning has achieved…
We analyze a recently published dataset of 396 real-world cloud architectures deployed on AWS, from companies belonging to a wide range of industries. From this dataset, we identify those architectures that contain HPC or edge components…
In this report, we have analyzed available cloud detection technique using sentinel hub. We have also implemented spatial attention generative adversarial network and improved quality of generated image compared to previous solution [7].
Recently, a number of cloud platforms and services have been developed for data intensive computing, including Hadoop, Sector, CloudStore (formerly KFS), HBase, and Thrift. In order to benchmark the performance of these systems, to…
With the rapid growth and increasing complexity of industrial big data, traditional data processing methods are facing many challenges. This article takes an in-depth look at the application of cloud computing technology in industrial big…
The Cloud Computing paradigm consists in providing customers with virtual services of the quality which meets customers' requirements. A cloud service operator is interested in using his infrastructure in the most efficient way while…
Cloud and cloud shadow masking is a crucial preprocessing step in hyperspectral satellite imaging, enabling the extraction of high-quality, analysis-ready data. This study evaluates various machine learning approaches, including gradient…
Cloud-computing shares a common pool of resources across customers at a scale that is orders of magnitude larger than traditional multi-user systems. Constituent physical compute servers are allocated multiple "virtual machines" (VM) to…
We show that distributed Infrastructure-as-a-Service (IaaS) compute clouds can be effectively used for the analysis of high energy physics data. We have designed a distributed cloud system that works with any application using large input…
Future networks are anticipated to enable exciting applications and industrial services ranging from Multisensory Extended Reality to Holographic and Haptic communication. These services are accompanied by high bandwidth requirements and/or…
In our former works we have made serious efforts to improve the performance of medical image analysis methods with using ensemble-based systems. In this paper, we present a novel hardware-based solution for the efficient adoption of our…
We investigate the feasibility of high performance scientific computation using cloud computers as an alternative to traditional computational tools. The availability of these large, virtualized pools of compute resources raises the…
Public clouds are one of the most thriving technologies of the past decade. Major applications over public clouds require world-wide distribution and large amounts of data exchange between their distributed servers. To that end, major cloud…
Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its…
Adapting the cloud for high-performance computing (HPC) is a challenging task, as software for HPC applications hinges on fast network connections and is sensitive to hardware failures. Using cloud infrastructure to recreate conventional…
In this paper, we investigate the issue of satellite-terrestrial computing in the sixth generation (6G) wireless networks, where multiple terrestrial base stations (BSs) and low earth orbit (LEO) satellites collaboratively provide edge…
Big earth science data offers the scientific community great opportunities. Many more studies at large-scales, over long-terms and at high resolution can now be conducted using the rich information collected by remote sensing satellites,…
The widespread deployment of large-scale, compute-intensive applications such as high-performance computing, artificial intelligence, and big data is leading to convergence between cloud and high-performance computing infrastructures. Cloud…