Cloud Computing Platform Research Articles

Improving the characterization of global aquatic land cover types using multi-source earth observation data

The sustainable management of aquatic resources requires spatially explicit information on the water and vegetation presence of aquatic ecosystems. Previous Global Aquatic Land Cover (GALC) mapping has been focused on water bodies while lacking information on vegetation, and aquatic types have always been characterized by low accuracies in global land cover products, calling for specific attention to improve GALC mapping. The availability of a wealth of open Earth Observation (EO) data on cloud-computing platforms provides opportunities to map aquatic land cover globally. This study aims to evaluate the potential of multi-source freely available EO data, including optical, Synthetic Aperture Radar (SAR), and various ancillary datasets, for improving the characterization of aquatic land cover comprising both water and vegetation types on a global scale. Using different combinations of features derived from these data, the classification performance of five land cover classes (i.e., trees, shrubs, herbaceous cover, bare/sparsely vegetated lands, and water bodies) in aquatic areas was cross-validated. Results showed that Sentinel-2 data alone achieved similarly good overall accuracy as those combining multi-source data. However, the single-sensor Sentinel-2 data cannot discriminate highly mixed and spectrally similar types, such as shrubs, trees, and herbaceous vegetation. Integrating SAR features from the ALOS/PALSAR mosaic and Sentinel-1 data with optical features provided by Sentinel-2 data could help address this limitation to some extent. Although with a lower spatial and temporal resolution, the ALOS/PALSAR mosaic had a stronger impact on GALC classification than Sentinel-1 data when they were synergistically used. Features provided by ancillary datasets did not lead to significant improvement in the overall GALC classification. At class-level, topographic and soil features helped to reduce the commission error of shrubs, and climate variables were useful to improve the characterization of bare aquatic lands. The Global Ecosystem Dynamics Investigation (GEDI) forest canopy height dataset helped to characterize trees but also resulted in a decrease in accuracies of shrubs. By assessing multi-source earth observation data, this research represents an important step forward in the global mapping of comprehensive aquatic land cover types at high spatial resolution (i.e., 10 m).

Impact and Implications of Big Data Analytics in Cloud Computing Platforms

Abstract: Big Data and Cloud Computing as two mainstream technologies, are at the center of concern in the IT field. Cloud Computing refers to the processing of anything, including Big Data Analytics, on the “cloud”. The “cloud” is just a set of highpowered servers from one of many providers. They can often view and query large data sets much more quickly than a standard computer could. Essentially, “Big Data” refers to the large sets of data collected, while “Cloud Computing” refers to the mechanism that remotely takes this data in and performs any operations specified on that data. Cloud Computing services largely exist because of Big Data. Likewise, the only reason that we collect Big Data is because we have services that are capable of taking it in and deciphering it, often in a matter of seconds. The two are a perfect match, since neither would exist without the other. The combination of both yields beneficial outcome for the organizations. Not to mention, both the technologies are in the stage of evolution but their combination leverages scalable and cost-effective solution in big data analytics. Big data and Cloud computing are perfect combination. Besides that, there are also some real-time challenges to deal with. In this paper, discribes both the aspects. This paper introduces the characteristics, trends and challenges of big data. In addition to that, it investigates the benefits and the risks that may rise out of the integration between big data and cloud computing. KEYWORDS: Cloud Computing, Big Data, Scalable, Analytics.

DEVELOPING TRANSFERABLE SPATIAL PREDICTION MODELS: A CASE STUDY OF SATELLITE BASED LANDCOVER MAPPING

Abstract. The mapping of environmental information based on remote sensing requires a workflow that involves image processing, model training usually based on machine learning, as well as model application and validation. Remote sensing data processing capabilities are nowadays simplified by cloud computing platforms. State of the art machine learning methods for spatial data which involve a reduction of spatial overfitting, handling of extrapolation situations and a spatially explicit error assessment, however, are currently mostly implemented in local computation frameworks. Here we present a workflow that combines the improved processing capabilities of the cloud computation platform openEO with state-of-the-art machine learning model development in R. OpenEO is used for standardized imagery acquisition and preprocessing to provide predictors for model training. To reduce overfitting, predictors which are meaningful for the mapping are identified via spatial variable selection as implemented in R packages. The mapping accuracy is assessed via spatial cross-validation and predictions are limited to the ’Area of Applicability’ of the model. The workflow is designed to enhance and assess the spatial transferability of machine learning models which is demonstrated by a case study of a landcover classification based on Sentinel-2 imagery.

Design of Cloud Computing Platform for Large-Scale Multimedia Communication

In order to enlarge the scale of multimedia communication, based on the design of cloud computing platform, this paper makes an in-depth study on the design of multimedia communication platform. Firstly, we study from three methods: extracting high-quality content, broadening multimedia profit channels, and enhancing copyright awareness. Among them, the big data diversion system under the cloud computing platform is designed to optimize data clustering and pattern recognition. The traditional big data shunting system under the cloud computing platform adopts the open-source embedded system design method. The shunting system is connected to the switch of the cloud computing platform through the coupling coil, resulting in missing points and wrong points in the data in the shunting system. Using this method, it is concluded that the cloud computing platform cannot achieve the optimal allocation in terms of SLA violation rate, resource consumption, and power consumption. After experiments and research, the current development trend of media shows a deviation from this phenomenon. The development of media itself, that is, the self-appreciation of media, is gradually becoming the main purpose of media development. A power simulation cloud computing platform architecture with dynamic expansion, efficient computing, and convenient resource scheduling should be given to face large-scale multimedia communication.

A Novel Discrete Grey Wolf Optimizer for Scientific Workflow Scheduling in Heterogeneous Cloud Computing Platforms

There are several scientific workflow applications which need vast amount of processing so the cloud offerings give the sense of economic. Workflow scheduling has drastic impact on gaining desired quality of service (QoS). The main objective of workflow scheduling is to minimize the makespan. The workflow scheduling is formulated to a discrete optimization problem which is NP-Hard. This paper presents a novel discrete grey wolf optimizer (D-GWO) for scientific workflow scheduling problems in heterogeneous cloud computing platforms in the aim of minimizing makespan. Although the original GWO had great achievements in continuous optimization problems, it seems clear gap in utilizing GWO for combinatorial discrete optimization problems. It revolves around the fact that the continuous changes in search space during the course of discrete optimization lead inefficient or meaningless solutions. To this end, the proposed algorithm is customized in such a way to optimize discrete workflow scheduling problem by leveraging some new binary operators and Walking Around approaches to balance between exploration and exploitation in discrete search space. Scientific unstructured workflows were investigated in different circumstances to prove effectiveness of proposed novel meta-heuristic algorithm. The simulation results witnessed the superiority of proposed D-GWO against other state-of-the-arts in terms of scheduling metrics.

State of the Vietnamese Coast—Assessing Three Decades (1986 to 2021) of Coastline Dynamics Using the Landsat Archive

Vietnam’s 3260 km coastline is densely populated, experiences rapid urban and economic growth, and faces at the same time a high risk of coastal hazards. Satellite archives provide a free and powerful opportunity for long-term area-wide monitoring of the coastal zone. This paper presents an automated analysis of coastline dynamics from 1986 to 2021 for Vietnam’s entire coastal zone using the Landsat archive. The proposed method is implemented within the cloud-computing platform Google Earth Engine to only involve publicly and globally available datasets and tools. We generated annual coastline composites representing the mean-high water level and extracted sub-pixel coastlines. We further quantified coastline change rates along shore-perpendicular transects, revealing that half of Vietnam’s coast did not experience significant change, while the remaining half is classified as erosional (27.7%) and accretional (27.1%). A hotspot analysis shows that coastal segments with the highest change rates are concentrated in the low-lying deltas of the Mekong River in the south and the Red River in the north. Hotspots with the highest accretion rates of up to +47 m/year are mainly associated with the construction of artificial coastlines, while hotspots with the highest erosion rates of −28 m/year may be related to natural sediment redistribution and human activity.

Blockchain Technology: Potential Applications for Public Sector E-Procurement and Project Management

Project implementations normally fail due to sustainable development problems that inhibit the usage levels required to facilitate successful implementations. This paper explores the successful implementation from a Bangladesh perspective. In particular, it identifies the possible applications of Blockchain in project procurement and management, and it develops a guideline for incorporating Blockchain into project management to improve the existing project and the procurement management practices in developing countries. This is a qualitative study with an interpretivist research methodology. By adopting an interpretive approach, this study construes knowledge as that only gained through social constructions, such as language, shared meanings, documents, industry reports, reviews of academic papers, and tools; it is a changing and relative phenomenon. This study reveals that developing countries are suffering in terms of the development of projects, including poor project management, a lack of transparency, poor procurement management, etc. A total of 38 issues were identified through this study, which are the main barriers to the successful implementation of public sector projects. One of the main reasons is due to the practice of awarding the lowest bidder during the procurement process due to the current regulatory requirements. This paper found that the Oracle platform, built on Blockchain technology as a cloud-computing platform, is one of the prominent E-procurement platforms that provides both the Blockchain platform and the cloud-based applications. Therefore, after observing and analysing the platform, it is found that Blockchain technology has the capability to resolve 25 issues out of 38 identified issues.

The evolving roles and impacts of 5G enabled technologies in healthcare: The world epidemic COVID-19 issues.

The latest 5G technology is being introduced the Internet of Things (IoT) Era. The study aims to focus the 5G technology and the current healthcare challenges as well as to highlight 5G based solutions that can handle the COVID-19 issues in different arenas. This paper provides a comprehensive review of 5G technology with the integration of other digital technologies (like AI and machine learning, IoT objects, big data analytics, cloud computing, robotic technology, and other digital platforms) in emerging healthcare applications. From the literature, it is clear that the promising aspects of 5G (such as super-high speed, high throughput, low latency) have a prospect in healthcare advancement. Now healthcare is being adopted 5G-based technologies to aid improved health services, more effective medical research, enhanced quality of life, better experiences of medical professionals and patients in anywhere–anytime. This paper emphasizes the evolving roles of 5G technology for handling the epidemiological challenges. The study also discusses various technological challenges and prospective for developing 5G powered healthcare solutions. Further works will incorporate more studies on how to expand 5G-based digital society as well as to resolve the issues of safety–security–privacy and availability–accessibility–integrity in future health crises.

Data Security and Privacy in Cloud Computing Platforms: A Comprehensive Review

Cloud computing is the on-demand usage of computer resources through the internet, allowing us to relocate application software and databases. Its purpose is to deliver IT services that enable clients to benefit from the pay-per-use model's substantial cost advantages and the ability to flexibly scale up or down without having to make significant investments in new hardware. On the other hand, the provider manages the data and services under this cloud architecture. Consequently, cloud clients have less control over their outsourced data and depend on cloud service providers to safeguard their data and infrastructure from both external and internal threats. In this study, we look at information security in cloud computing. It is the consideration of information on the cloud, considering security concerns. This article will go through data protection strategies utilized all around the globe to provide optimum data security by lowering risks and threats. Information accessibility is beneficial for various cloud applications, but it poses risks by exposing data to apps that already have security escape clauses. In addition, the presentation will outline information security highlights for Data in Transit and Data at Rest.

Data Security and Privacy in Cloud Computing Platforms: A Comprehensive Review

Data Security and Privacy in Cloud Computing Platforms: A Comprehensive Review

Design of Lightweight Authentication Protocol for Fog enabled Internet of Things- A Centralized Authentication Framework

Internet is a large network of networks that spans the entire globe. Internet is playing indispensable role in our daily lives. The physical things are connected to internet with the help of digital identity. With recent advancement of information and communication technologies IoT became vital part of human life. However, IoT is not having standardized architecture. Nowadays IoT is integrated with fog computing which extends platform of cloud computing by providing computing resources on edges of computer network. Fog computing is motivated by IOT and It is decentralized solution for IoT. In addition, Fog computing has supported features like geographic distribution, low latency, location awareness, operate on premise, installed on heterogeneous hardware. IoT with cloud computing does not have such features. Therefore, in this paper, at first we discuss about the distributed fog computing architecture. Subsequently, we address the problem of authentication and design a new authentication framework for fog enabled IOT environment. It is stated that the proposed authentication framework will be useful in many IoT applications such as healthcare system, transportation system, smart cities, home energy management etc.

Integration and Sharing of College English Teaching Resources Using Cloud Computing Platform

Intelligent computing has been used in the field of education and teaching for a long time in other countries. China is currently conducting education cloud research, but it has not yet been applied to the sharing of instructional resources in universities. The creation of a university instructional resource sharing network based on intelligent computing will help to alleviate the uneven distribution of educational resources to some extent and achieve the goal of high-quality instructional resource sharing, which is critical for improving China’s tertiary education overall. The information age has arrived, thanks to the advancement of information technology and the growth of communication networks. The impact of instructional resources on English classrooms is growing day by day against the backdrop of digitization. The educational resource sharing service platform can effectively deal with the problems of excessive infrastructure investment and low utilization of educational resources with intelligent computing as the technical support. It is essential for promoting educational resource sharing as well as interregional tertiary education exchanges and collaboration. As a result of the research on intelligent computing and educational resource sharing service platform, this paper discusses specific problems such as platform module design, system inspection, and application form, in order to provide a reference for the establishment of educational resource sharing service platform in universities, based on the overall and functional requirements of the platform, a platform that allows teachers and students to access personalized digital resources on demand for reading, downloading, printing, and sharing.

Scheduling Virtual Machines for Load balancing in Cloud Computing Platform

Scheduling Virtual Machines for Load balancing in Cloud Computing Platform

Construction of an English Research Learning Model Based on Constructivism and Data Mining under a Cloud Computing Platform

This paper attempts to explore and summarize the teaching activities of English research learning based on constructivism and data mining under the guidance of advanced teaching concepts and the support of cloud computing platforms and to explain the problems reflected in the practice of English research learning in the current environment. Constructing a teaching model based on constructivism and data mining on the cloud computing platform is a beneficial attempt to combine the new teaching design theory with teaching practice. Based on constructivist learning theory and data mining technology, combined with current English teaching practice, this paper discusses the integration of English research-based learning and data mining technology. Try to implement it in English teaching to improve English learners’ autonomous inquiry ability. The purpose is to promote students to form a positive learning attitude and good learning strategies and cultivate students’ innovative spirit and practical ability to adapt to the requirements of the times for talents. The combination of cloud computing technology and data mining has created a new English learning model, which can greatly improve the efficiency of English learning and provide a good reference for the English learning model.

A Deep-Neural-Network-Based Aerosol Optical Depth (AOD) Retrieval from Landsat-8 Top of Atmosphere Data

The 30 m resolution Landsat data have been used for high resolution aerosol optical depth (AOD) retrieval based on radiative transfer models. In this paper, a Landsat-8 AOD retrieval algorithm is proposed based on the deep neural network (DNN). A total of 6390 samples were obtained for model training and validation by collocating 8 years of Landsat-8 top of atmosphere (TOA) data and aerosol robotic network (AERONET) AOD data acquired from 329 AERONET stations over 30°W–160°E and 60°N–60°S. The Google Earth Engine (GEE) cloud-computing platform is used for the collocation to avoid a large download volume of Landsat data. Seventeen predictor variables were used to estimate AOD at 500 nm, including the seven bands TOA reflectance, two bands TOA brightness (BT), solar and viewing zenith and azimuth angles, scattering angle, digital elevation model (DEM), and the meteorological reanalysis total columnar water vapor and ozone concentration. The leave-one-station-out cross-validation showed that the estimated AOD agreed well with AERONET AOD with a correlation coefficient of 0.83, root-mean-square error of 0.15, and approximately 61% AOD retrievals within 0.05 + 20% of the AERONET AOD. Theoretical comparisons with the physical-based methods and the adaptation of the developed DNN method to Sentinel-2 TOA data with a different spectral band configuration are discussed.

Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine.

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SCOPE and the atmospheric RTM 6SV. The retrieval models, named to S3-TOA-GPR-1.0, were directly implemented in Google Earth Engine (GEE) to enable the quantification of the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor.Following good to high theoretical validation results with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI), a three fold evaluation approach over diverse sites and land cover types was pursued: (1) temporal comparison against LAI and FAPAR products obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) for the time window 2016-2020, (2) spatial difference mapping with Copernicus Global Land Service (CGLS) estimates, and (3) direct validation using interpolated in situ data from the VALERI network. For all three approaches, promising results were achieved. Selected sites demonstrated coherent seasonal patterns compared to LAI and FAPAR MODIS products, with differences between spatially averaged temporal patterns of only 6.59%. In respect of the spatial mapping comparison, estimates provided by the S3-TOA-GPR-1.0 models indicated highest consistency with FVC and FAPAR CGLS products. Moreover, the direct validation of our S3-TOA-GPR-1.0 models against VALERI estimates indicated with regard to jurisdictional claims in good retrieval performance for LAI, FAPAR and FVC. We conclude that our retrieval workflow of spatiotemporal S3 TOA data processing into GEE opens the path towards global monitoring of fundamental vegetation traits, accessible to the whole research community.

Development of Cloud Computing Platform Based on Neural Network

Aiming at the problems of small data storage, small platform throughput, and high energy consumption in the development of existing cloud computing platforms, this paper develops a cloud computing platform based on neural networks. In the design of the platform, firstly, the functions of the cloud computing platform are determined. Based on the function design, the hardware and software of the cloud computing platform are designed. In the hardware design, the topology of cloud computing platform, data acquisition module, single-chip microcomputer, node deployment, and other types of functional hardware are designed. In the software design, the neural network is mainly used to remove the redundancy of data stored in a cloud computing platform, design the data processing flow of cloud computing platform, and complete the development of cloud computing platform based on neural network. The experimental results show that the cloud computing platform based on the neural network designed in this paper runs faster, the throughput of platform data has been significantly improved, and the operating energy consumption of the platform is low.

Cybersecurity risk management in the industry 4.0

A referential and abstract model will be presented to be applied in risk management on cloud computing and industry ICS platforms in general. we are looking at the types of assessments that are out there, we explore the different approaches and techniques behind information technology systems risk assessments and complexity of conducting industrial control system safely at-risk control level.

High Throughput Calculation and Screening of High Strength and High Conductivity Cu-Cr-Sn Copper Alloy

In this paper, based on the first principles, the exchange correlation functional (GGA) and modified functional (PBE) are used for the calculation of Cu-Cr-Sn high strength and high conductivity copper alloy. The cloud high-throughput cloud computing and screening platform such as Matcloud and cluster expansion method are mainly used. Starting from the most basic components of micro atoms, electrons, ions and energy bands, the high-throughput crystal structure modeling and screening optimization algorithm are adopted, The electronic structure, electromigration and diffusivity of the matrix and the second phase of the material were studied. The theoretical criteria for the collaborative optimization and control of the key performance parameters of the matrix and the characteristic microstructure were established. The characteristic microstructure and material component sequences with excellent mechanical, electrical and thermodynamic properties were predicted and screened.

An Interactive Strategy for the Training Set Definition Based on Active Self-Paced Learning Implemented on a Cloud-Computing Platform

Supervised classification of remote sensing data requires a large number of high-quality annotated samples. At the operational level, the definition of a large training set by photograph interpretation is costly and time-consuming. The manual annotation activity is typically supported by high-resolution satellite data. Therefore, when working at country or continental scale, it is necessary to efficiently access large archives of remotely sensed data. To address these issues, this letter presents an interactive strategy implemented in a cloud-computing platform for defining effective training sets with significantly reduced human effort. This is achieved by combining active learning (AL) and self-paced learning (SPL) techniques. First, an initial training set is used to classify the pool of unlabeled samples. Then, the method progressively adds high-confidence samples, selected through an SPL strategy, and low-confidence samples selected considering an AL strategy. While the high-confidence sample labels are self-paced, the low-confidence ones are manually assigned. The cloud-computing platform allows the: 1) definition of a complete training set in a fast and efficient way and 2) access to a multipetabyte catalog of satellite imagery. Experiments carried out on the Google Earth Engine (GEE) Platform demonstrate the effectiveness of the proposed strategy compared to the standard manual annotation.