Cloud Computing Platform Research Articles

Application of Cloud Computing in Network Platform

With the explosive development of knowledge and the rapid development of various information technology, cloud computing technology, resource allocation and management technology, cloud computing is a business computing model that allocates computing work to a large number of resources. Under this circumstance, the marketing model has undergone tremendous changes, and the traditional profit model is difficult to continue. This has forced the major marketing platforms to change their development thinking and look for new points of profit growth. This article aims to study the application of cloud computing in the network marketing platform. This article mainly uses questionnaire survey method and practice survey method. First, it analyzes the status quo of cloud computing and online marketing platforms at home and abroad, and then conducts online marketing on the platform based on cloud computing. Marketing strategy design and analysis of information needs. Experimental research shows that, whether in terms of business processing time or customer satisfaction, online marketing under cloud computing performs better than traditional marketing, ensuring quality and achieving high efficiency.

Ding Xilin's Chinese Translation of The Man of Destiny

ABSTRACT This article examines Ding Xilin's (丁西林) (1893–1974) Chinese translation of The Man of Destiny by making use of artificial intelligence and machine learning functionalities on IBM Cloud Computing Platforms to decipher Ding's cultural translation. While an untrained model of IBM Natural Language Understanding reveals Ding's using the translation to criticize imperialism, a trained machine learning model using IBM Watson Knowledge Studio shows the qualities of the Shavian Great Man. The IBM artificial intelligence and machine learning platforms, drawing attention to words and the semantic relations between them, uncover a subtle relationship between Ding Xilin's Chinese translation and the personality cult around China's Great Man, Mao Zedong (1893–1976), around the time of Ding's translation. The artificial intelligence platforms show that there may be subtle connections between Shaw's play and the most dominant figure in China at the time of the translation.

Trends in Cloud Computing

In today's world cloud computing is widely used in many it industries. Cloud computing is an emerging model of business computing. Cloud computing which is based on internet has the most powerful architecture of computation. Because of the newly in innovative technologies in cloud computing, the number of labs is transformed into cloud named as the virtual labs. It considers a collection of integrated and networked hardware, software and internet infrastructure. Cloud computing is a development of parallel computing, distributed computing, grid computing and virtualisation technologies from cloud computing adoption accept and also focuses on cloud interoperability issues. We also present the popular platforms of cloud computing. In this paper, we explore several cloud computing system providers about their concerns on security and privacy issues. We also present the characteristics and applications of different well-known cloud computing platforms. We also purpose to recognise the challenges and issues of cloud computing. However, security and privacy issues present a strong roadblock for users to adapt into cloud computing systems.

A Review Study on Security Issues, Benefits, Risks and Various Challenges in Cloud Computing Platform and Proposed Model for Enhancing Security for Sensitive Data

In this recent and technological epoch, Cloud Computing(CC) is the generally promising and upcoming trend in the Universe which aims at the high performance supercomputing. It is the on-request openness of computer system assets, in particular storage space and computing supremacy, with no forthright dynamic supervision by the user. The expression is by and large used to illustrate Data Centers (DC) available to many users over the Internet. Nowadays, most of the organizations move into cloud because it helps the end users to store their personal data into the cloud which can be accessed by the user at anytime and anywhere across the globe. The users data is stored and maintained in DC of the cloud providers viz.. Google, Amazon, Microsoft etc. Despite several advantages of using the cloud computing technology, the end users hesitate to utilize the various cloud services and business organizations are unwilling to deploy/set up their businesses in the cloud because of several security threats and issues involved in cloud computing. The main emphasis of our study shall discuss the cloud computing technology , its characteristics, deployment models, service models and the various security issues, challenges related to cloud computing. We shall also present a schematic idea/algorithm which could be used to ensure the security of sensitive data in cloud storage.

Emergency Scheduling Optimization Simulation of Cloud Computing Platform Network Public Resources

Emergency scheduling of public resources on the cloud computing platform network can effectively improve the network emergency rescue capability of the cloud computing platform. To schedule the network common resources, it is necessary to generate the initial population through the Hamming distance constraint and improve the objective function as the fitness function to complete the emergency scheduling of the network common resources. The traditional method, from the perspective of public resource fairness and priority mapping, uses incremental optimization algorithm to realize emergency scheduling of public resources, neglecting the improvement process of the objective function, which leads to unsatisfactory scheduling effect. An emergency scheduling method of cloud computing platform network public resources based on genetic algorithm is proposed. With emergency public resource scheduling time cost and transportation cost minimizing target, initial population by Hamming distance constraints, emergency scheduling model, and the corresponding objective function improvement as the fitness function, the genetic algorithm to individual selection and crossover and mutation probability were optimized and complete the public emergency resources scheduling. Experimental results show that the proposed method can effectively improve the efficiency of emergency resource scheduling, and the reliability of emergency scheduling is better.

On-the-Fly Fusion of Remotely-Sensed Big Data Using an Elastic Computing Paradigm with a Containerized Spark Engine on Kubernetes.

Remotely-sensed satellite image fusion is indispensable for the generation of long-term gap-free Earth observation data. While cloud computing (CC) provides the big picture for RS big data (RSBD), the fundamental question of the efficient fusion of RSBD on CC platforms has not yet been settled. To this end, we propose a lightweight cloud-native framework for the elastic processing of RSBD in this study. With the scaling mechanisms provided by both the Infrastructure as a Service (IaaS) and Platform as a Services (PaaS) of CC, the Spark-on-Kubernetes operator model running in the framework can enhance the efficiency of Spark-based algorithms without considering bottlenecks such as task latency caused by an unbalanced workload, and can ease the burden to tune the performance parameters for their parallel algorithms. Internally, we propose a task scheduling mechanism (TSM) to dynamically change the Spark executor pods’ affinities to the computing hosts. The TSM learns the workload of a computing host. Learning from the ratio between the number of completed and failed tasks on a computing host, the TSM dispatches Spark executor pods to newer and less-overwhelmed computing hosts. In order to illustrate the advantage, we implement a parallel enhanced spatial and temporal adaptive reflectance fusion model (PESTARFM) to enable the efficient fusion of big RS images with a Spark aggregation function. We construct an OpenStack cloud computing environment to test the usability of the framework. According to the experiments, TSM can improve the performance of the PESTARFM using only PaaS scaling to about 11.7%. When using both the IaaS and PaaS scaling, the maximum performance gain with the TSM can be even greater than 13.6%. The fusion of such big Sentinel and PlanetScope images requires less than 4 min in the experimental environment.

ФОРМУВАННЯ КОМПЕТЕНТНОСТЕЙ УЧАСНИКІВ ОСВІТНЬОГО ПРОЦЕСУ НА ОСНОВІ ХМАРО ОРІЄНТОВАНИХ ІНФОРМАЦІЙНО-ОСВІТНІХ СИСТЕМ

Представлено основні результати виконання комплексу наукових досліджень, що здійснювалися у 2018-2020 рр. в Інституті інформаційних технологій і засобів навчання НАПН України, з проблем формування компетентностей учасників освітнього процесу на основі хмаро орієнтованих інформаційно-освітніх систем: 1. «Адаптивна хмаро орієнтована система навчання та професійного розвитку вчителів закладів загальної середньої освіти» (фундаментальне, керівник — д-р пед. наук, с.н.с. М.П. Шишкіна); 2. «Система комп’ютерного моделювання пізнавальних завдань для формування компетентностей учнів з природничо-математичних предметів» (фундаментальне, керівник — д-р пед. наук, с.н.с. С.Г. Литвинова); 3. «Методика використання відкритих електронних науково-освітніх систем для розвитку інформаційно-дослідницької компетентності наукових і науково-педагогічних працівників» (прикладне, керівники — канд. пед. наук, с.н.с. А.В. Яцишин, канд. пед. наук С.М. Іванова). Особливу увагу в доповіді приділено впровадженню результатів досліджень у практику закладів загальної середньої освіти, вищої та післядипломної педагогічної освіти. Презентовано розроблені та апробовані на практиці методики, навчально-методичні матеріали. Системоутворювальними чинниками комплексу наукових досліджень є: загальна мета, єдиний базовий методологічний фундамент досліджень (системний, компетентнісний і проєктний підходи), комплексний характер їх одночасного проведення і впровадження, взаємозумовленість основних кінцевих результатів (сформованих цифрових компетентностей учасників освітнього процесу), хмарна комп’ютерно-технологічна платформа досліджень і реалізацій освітніх застосувань.

Sen2Grass: A Cloud-Based Solution to Generate Field-Specific Grassland Information Derived from Sentinel-2 Imagery

Grasslands are important for their ecological values and for agricultural activities such as livestock production worldwide. Efficient grassland management is vital to these values and activities, and remote sensing technologies are increasingly being used to characterize the spatiotemporal variation of grasslands to support those management practices. For this study, Sentinel-2 satellite imagery was used as an input to develop an open-source and automated monitoring system (Sen2Grass) to gain field-specific grassland information on the national and regional level for any given time range as of January 2016. This system was implemented in a cloud-computing platform (StellaSpark Nexus) designed to process large geospatial data streams from a variety of sources and was tested for a number of parcels from the Haus Riswick experimental farm in Germany. Despite outliers due to fluctuating weather conditions, vegetation index time series suggested four distinct growing cycles per growing season. Established relationships between vegetation indices and grassland yield showed poor to moderate positive trends, implying that vegetation indices could be a potential predictor for grassland biomass and chlorophyll content. However, the inclusion of larger and additional datasets such as Sentinel-1 imagery could be beneficial to developing more robust prediction models and for automatic detection of mowing events for grasslands.

Environmental impacts of Australia's largest health system

Climate change is causing a range of impacts on communities such as more frequent extreme weather, air pollution, changing distribution of infectious diseases, mental health impacts and others. The 2018 report of the Lancet Countdown on health and climate change concluded that “…climate change is the biggest global health threat of the 21st century” (Watts et al. 2018). This study aims to provide a quantitative supply-chain assessment of the environmental impacts of the health system in Australia's largest state, New South Wales (NSW). We integrate a range of environmental indicators, such as greenhouse gas emissions (GHGEs), water use and waste into a cloud-computing platform for constructing a custom-built sub-national multi-regional input-output (MRIO) table for Australia. We then use specific health system data to construct a hybrid life-cycle assessment model featuring detail on the health system, and quantify the environmental impact of interactions of the health system with the rest of the NSW and Australian economy. Our findings show that the health system in Australia's largest state was responsible for 6.6% of the total estimated GHGEs in 2017, 4% of water use, and 8% of waste produced in the NSW economy. The results highlight the need for implementation of targeted policies to reduce not only the direct environmental impacts generated by health systems but also those from its supply chains that connect with many other economic sectors.

Optimal and robust vegetation mapping in complex environments using multiple satellite imagery: Application to mangroves in Southeast Asia

A band selection model was described for efficient and accurate remotely-sensed vegetation mapping in cloudy mixed-vegetation areas, demonstrated with an application on mapping mangroves in Southeast Asia (SE Asia). We show how to use multi-source satellite imagery and Cloud Computing Platforms to improve mapping and computational efficiency in complex environments. A key element of the method relies upon field surveys to establish a detailed sample database that includes easily-confused land cover. The Maximal Separability and Information (MSI) model was developed to select key bands for target land cover classification from multiple satellite imagery based on two principles: 1. maximize separability of the target cover from other land cover; and 2. maximize and prioritize information from band combinations. Application of the MSI model to map mangroves in SE Asia using three optical and SAR data systems (Landsat OLI, Sentinel-2 and Sentinel-1) showed: 1. Sentinel-2 is better at classifying mangrove than Landsat and Sentinel-1; and 2. SWIR, NIR and Red bands (with SWIR in particular) are effective in separating mangrove from other vegetation. The MSI-mapped mangroves showed lower computation cost compared to using all bands from individual satellites, and higher accuracy (above 90%) when applied to SE Asia. It was robust in tolerating smaller sample sizes, thereby demonstrating computational feasibility and substantial improvements with the MSI model for large-scale land cover mapping in complex environments.

PLC Control System of Pneumatic Manipulator Automatic Assembly Line Based on Cloud Computing Platform

With the development of science and technology, cloud computing, big data and artificial intelligence and other new technologies have been applied in all aspects of life. The traditional manual tracking and processing production process has many defects. The application of computer technology to the production line to realize automatic production has the characteristics of high efficiency and integration. This paper studies the PLC control system of pneumatic robot automatic assembly line based on cloud computing platform system. In the research, we use PCL system to scan the data of automatic assembly of pneumatic manipulator processed by cloud computing platform, and write the program into the memory of PCL. At the same time, we connect the field input signal and the controlled actuator to the input end of the input module and the output end of the output module. The user can realize the automatic assembly by running the program. Compared with the number of Pneumatic Manipulators produced by manual production line, the system in this paper has high efficiency and superior performance. Thus, the PLC control system of pneumatic manipulator automatic assembly line based on cloud computing platform can improve the production efficiency.

AN EFFICIENT LOAD SCHEDULING TECHNIQUE USING OPPOSITIONAL SPARROW SEARCH ALGORITHM FOR CLOUD COMPUTING ENVIRONMENT

Cloud computing (CC) environment is commonly applied for provisioning effectual services and offers diverse benefits, along with some challenges like load balancing and load scheduling. The load scheduling can be defined as the procedure of assigning and balancing the load like tasks or cloudlets to virtual machines (VMs) in the cloud platform. A main goal of the load scheduling algorithm is to lessen the transfer time and total cost incurred in the system. This paper presents a new oppositional sparrow search algorithm (OSSA) for efficient load scheduling in CC environment. The classical sparrow search algorithm (SSA) is based on the group wisdom, foraging and anti-predation behaviour of sparrows. To improve the convergence speed of SSA, Opposition based learning (OBL) concept is incorporated to it. The proposed OSSA based load scheduler has the ability of scheduling the load effectively in the CC platform. An extensive set of simulations were carried out to ensure the goodness of the OSSA and the results are investigated under several aspects. The experimental results indicated the superior nature of the OSSA over the compared methods on all the applied scenarios.

A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data.

The current exponential increase of spatiotemporally explicit data streams from satellitebased Earth observation missions offers promising opportunities for global vegetation monitoring. Intelligent sampling through active learning (AL) heuristics provides a pathway for fast inference of essential vegetation variables by means of hybrid retrieval approaches, i.e., machine learning regression algorithms trained by radiative transfer model (RTM) simulations. In this study we summarize AL theory and perform a brief systematic literature survey about AL heuristics used in the context of Earth observation regression problems over terrestrial targets. Across all relevant studies it appeared that: (i) retrieval accuracy of AL-optimized training data sets outperformed models trained over large randomly sampled data sets, and (ii) Euclidean distance-based (EBD) diversity method tends to be the most efficient AL technique in terms of accuracy and computational demand. Additionally, a case study is presented based on experimental data employing both uncertainty and diversity AL criteria. Hereby, a a simulated training data base by the PROSAIL-PRO canopy RTM is used to demonstrate the benefit of AL techniques for the estimation of total leaf carotenoid content (Cxc) and leaf water content (Cw). Gaussian process regression (GPR) was incorporated to minimize and optimize the training data set with AL. Training the GPR algorithm on optimally AL-based sampled data sets led to improved variable retrievals compared to training on full data pools, which is further demonstrated on a mapping example. From these findings we can recommend the use of AL-based sub-sampling procedures to select the most informative samples out of large training data pools. This will not only optimize regression accuracy due to exclusion of redundant information, but also speed up processing time and reduce final model size of kernel-based machine learning regression algorithms, such as GPR. With this study we want to encourage further testing and implementation of AL sampling methods for hybrid retrieval workflows. AL can contribute to the solution of regression problems within the framework of operational vegetation monitoring using satellite imaging spectroscopy data, and may strongly facilitate data processing for cloud-computing platforms.

Cloud Computing Platform for Mentoring Trainees: The Case of the Professional Training of Midwives in Morocco

The digital transformation invites several solutions to optimize professional training. Its particularity lies in the management, conservation and securing of their data to each of the players. The use of the electronic learning booklet with Cloud computing aims to be simple and intuitive, it creates interaction and makes it possible to strengthen the links of the trinomial (Student, Educational tutor, supervisor on a training)We thought of using a online platform tool using Cloud Computing Technology to overcome the limitations of the "paper" learning booklet. This technical-pedagogical system makes it possible to save all data of the apprentice through evaluation grids and follow-up sheets, both during training periods and in theoretical training, according to periods decided by the training institution. The adoption of this imperative of qualification and technological integration retains the role of each of the actors: the student, the pedagogical supervisor and the supervisor on a training course. In this strategic vision, we have conceptualized and implemented an Electronic Learning Booklet (ELB) for integrative professionalization.Our Research Methodology is based on an engineering of the training devices. Initially we will present the context of the research project ELB. In this framework, we will present our problematic centered on the management of technical invention and pedagogical innovation in the framework of training of Midwives in Morocco. In a second step, we will discuss the qualitative and quantitative results of the statistical analysis of our target sample.By crossing the results of the study with the validation interviews, which confound and invalidate our starting hypothesis. LEA is a powerful vector of quality and academic performance of midwifery training.

Design and Implementation of Virtual Security Function Based on Multiple Enclaves

Network function virtualization (NFV) provides flexible and scalable network function for the emerging platform, such as the cloud computing, edge computing, and IoT platforms, while it faces more security challenges, such as tampering with network policies and leaking sensitive processing states, due to running in a shared open environment and lacking the protection of proprietary hardware. Currently, Intel® Software Guard Extensions (SGX) provides a promising way to build a secure and trusted VNF (virtual network function) by isolating VNF or sensitive data into an enclave. However, directly placing multiple VNFs in a single enclave will lose the scalability advantage of NFV. This paper combines SGX and click technology to design the virtual security function architecture based on multiple enclaves. In our design, the sensitive modules of a VNF are put into different enclaves and communicate by local attestation. The system can freely combine these modules according to user requirements, and increase the scalability of the system while protecting its running state security. In addition, we design a new hot-swapping scheme to enable the system to dynamically modify the configuration function at runtime, so that the original VNFs do not need to stop when the function of VNFs is modified. We implement an IDS (intrusion detection system) based on our architecture to verify the feasibility of our system and evaluate its performance. The results show that the overhead introduced by the system architecture is within an acceptable range.

A Novel Encryption and Decryption of Data using Mobile Cloud Computing Platform

As the need for super-fast mobile devices incorporating cloud computing technology continues to be the need of the hour, Mobile Cloud Computing (MCC) serves as the platform for mobile users to share data with others, store information on the cloud and also compute using the data. Over the years, the most widely preferred encryption that has proven to be reliable is Attribute Based Encryption (ABE). However, this encryption methodology requires expensive pairing operation which makes it unsuitable for MCC. As a result of this, MCC remains slow in reaching the crowd due to the challenge of resource-constrained mobile devices. To tackle this resource-constraint we propose a novel method of outsourcing operations to resource-rich cloud servers so that the constraint on resources does not hinder proper functioning of the mobile device. There are a number of advantages when data sharing is incorporated with lightweight fine-grain data sharing methodology. This method has a number of advantages such as CCA security level, resisting decryption key exposure and supporting verifiable outsourced decryption. Simulation results indicate that the performance analysis and concrete security proof is apt for MCC environment.

Privacy‐Guarding Optimal Route Finding with Support for Semantic Search on Encrypted Graph in Cloud Computing Scenario

The arrival of cloud computing age makes data outsourcing an important and convenient application. More and more individuals and organizations outsource large amounts of graph data to the cloud computing platform (CCP) for the sake of saving cost. As the server on CCP is not completely honest and trustworthy, the outsourcing graph data are usually encrypted before they are sent to CCP. The optimal route finding on graph data is a popular operation which is frequently used in many fields. The optimal route finding with support for semantic search has stronger query capabilities, and a consumer can use similar words of graph vertices as query terms to implement optimal route finding. Due to encrypting the outsourcing graph data before they are sent to CCP, it is not easy for data customers to manipulate and further use the encrypted graph data. In this paper, we present a solution to execute privacy‐guarding optimal route finding with support for semantic search on the encrypted graph in the cloud computing scenario (PORF). We designed a scheme by building secure query index to implement optimal route finding with support for semantic search based on searchable encryption idea and stemmer mechanism. We give formal security analysis for our scheme. We also analyze the efficiency of our scheme through the experimental evaluation.

The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform

Development of the Canadian Wetland Inventory Map (CWIM) has thus far proceeded over two generations, reporting the extent and location of bog, fen, swamp, marsh, and water wetlands across the country with increasing accuracy. Each generation of this training inventory has improved the previous results by including additional reference wetland data and focusing on processing at the scale of ecozone, which represent ecologically distinct regions of Canada. The first and second generations attained relatively highly accurate results with an average approaching 86% though some overestimated wetland extents, particularly of the swamp class. The current research represents a third refinement of the inventory map. It was designed to improve the overall accuracy (OA) and reduce wetlands overestimation by modifying test and train data and integrating additional environmental and remote sensing datasets, including countrywide coverage of L-band ALOS PALSAR-2, SRTM, and Arctic digital elevation model, nighttime light, temperature, and precipitation data. Using a random forest classification within Google Earth Engine, the average OA obtained for the CWIM3 is 90.53%, an improvement of 4.77% over previous results. All ecozones experienced an OA increase of 2% or greater and individual ecozone OA results range between 94% at the highest to 84% at the lowest. Visual inspection of the classification products demonstrates a reduction of wetland area overestimation compared to previous inventory generations. In this study, several classification scenarios were defined to assess the effect of preprocessing and the benefits of incorporating multisource data for large-scale wetland mapping. In addition, the development of a confidence map helps visualize where current results are most and least reliable given the amount of wetland test and train data and the extent of recent landscape disturbance (e.g., fire). The resulting OAs and wetland areal extent reveal the importance of multisource data and adequate test and train data for wetland classification at a countrywide scale.

Residential Electricity Classification Method Based On Cloud Computing Platform and Random Forest

Residential Electricity Classification Method Based On Cloud Computing Platform and Random Forest

An Improved Metaheuristic based Node Localization Technique for Wireless Sensor Networks

Cloud computing (CC) becomes a familiar topic in offering unlimited access to services as well as resources via the Internet. A comprehensive CC management system is needed to collect details of the task processing and ensure proper resource allocation with the accomplishment of Quality of Service (QoS). At the same time, virtual machine (VM) migration is a crucial problem in the CC platform which contributes to energy utilization and resource usage. Therefore, this paper presents a new energy-aware elephant herd optimization-based VM migration (EAEHO-VMM) scheme. The EAEHO-VMM algorithm aims to migrate the VMs and prediction failure VMs. At the initial stage, the EHO algorithm is executed to minimize the energy utilization of the VM migration process in the CC environment. In addition, a support vector machine (SVM) model is applied to identify the failure VMs and allows relocation in an effective way. In order to make sure the better performance of the EAEHO-VMM algorithm, a series of simulations take place, and the results are investigated in terms of different aspects. The experimental outcomes ensured the enhanced VM migration performance of the EAEHO-VMM algorithm over the other techniques.