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This study describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module is designed to facilitate interactive learning of whole-genome bisulfite sequencing (WGBS) data analysis utilizing cloud-based tools in Google Cloud Platform, such as Cloud Storage, Vertex AI notebooks and Google Batch. WGBS is a powerful technique that can provide comprehensive insights into DNA methylation patterns at single cytosine resolution, essential for understanding epigenetic regulation across the genome. The designed learning module first provides step-by-step tutorials that guide learners through two main stages of WGBS data analysis, preprocessing and the identification of differentially methylated regions. And then, it provides a streamlined workflow and demonstrates how to effectively use it for large datasets given the power of cloud infrastructure. The integration of these interconnected submodules progressively deepens the user's understanding of the WGBS analysis process along with the use of cloud resources. Through this module, we can enhance the accessibility and adoption of cloud computing in epigenomic research, speeding up the advancements in the related field and beyond. This manuscript describes the development of a resource module that is part of a learning platform named ''NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [ 1 ] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. [ABSTRACT FROM AUTHOR]

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on protein quantification in an interactive format that uses appropriate cloud resources for data access and analyses. Quantitative proteomics is a rapidly growing discipline due to the cutting-edge technologies of high resolution mass spectrometry. There are many data types to consider for proteome quantification including data dependent acquisition, data independent acquisition, multiplexing with Tandem Mass Tag reporter ions, spectral counts, and more. As part of the NIH NIGMS Sandbox effort, we developed a learning module to introduce students to mass spectrometry terminology, normalization methods, statistical designs, and basics of R programming. By utilizing the Google Cloud environment, the learning module is easily accessible without the need for complex installation procedures. The proteome quantification module demonstrates the analysis using a provided TMT10plex data set using MS3 reporter ion intensity quantitative values in a Jupyter notebook with an R kernel. The learning module begins with the raw intensities, performs normalization, and differential abundance analysis using limma models, and is designed for researchers with a basic understanding of mass spectrometry and R programming language. Learners walk away with a better understanding of how to navigate Google Cloud Platform for proteomic research, and with the basics of mass spectrometry data analysis at the command line. This manuscript describes the development of a resource module that is part of a learning platform named ''NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [ 1 ] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. [ABSTRACT FROM AUTHOR]

Multi-cloud security is a comprehensive cloud security solution that protects applications and data through various platforms. Multi-cloud security contains public and private clouds like Amazon Web Service (AWS), Google Cloud Platform, Azure, and the infrastructure of Oracle Cloud. However, it is difficult to protect data in the environment of multi-cloud, and it is challenging to secure and track data stored through various cloud providers. In this research, the Improved Rivest-Shamir Adleman (IRSA) approach is proposed for multi-cloud security to protect confidential data. The user-owned data files are initially assumed to vary in size, typically falling within the range of 100 to 1000 MB. AWS S3 is utilized to allow users to retrieve and store any amount of data at any time. Then, the AWS Identity and Access Management (AWS-IAM) is employed to make secure management of access to the AWS resources for verification. IRSA is designed for encryption to prevent the decomposition of the prime factor from the public key. After the successful encryption of the various file parts, each file part is uploaded separately to a distant cloud using a file naming scheme. When compared to the existing methods like Enhanced Symmetric Key Encryption Algorithm (ESKEA), Homomorphic Bloom Filter-based Data Security (HBDaSeC), Triple Data Encryption Standard (TDES), Efficient Ciphertext-Policy Attribute Based Encryption (E-CP-ABE), hybrid cryptographic technique, and Advanced Encryption Standard (AES), the proposed IRSA achieves better encryption and decryption times of 103 ms and 110 ms in 100 data size respectively. [ABSTRACT FROM AUTHOR]

The harmful code application known as a rootkit is designed to be loaded and run directly from the operating system's (OSs') Kernel. Rootkits deployed in the Kernel, called Kernel-mode rootkits, can alter the OS. The intention behind these Kernel changes is to conceal the hack. Detecting a Kernel rootkit in a target machine is found to be quite challenging. Numerous techniques can be employed to modify the Kernel of a system. Kernel rootkits also create hidden access for attacks, enabling unauthorized entry to be gained by attackers on the machine. The ultimate consequence is that essential computer data can be modified, personal information can be gathered, and hackers can observe behavior. Synthetic neural networks support artificial intelligence, a branch of deep learning that models the human brain and operates on large datasets. This study proposed the Kernel rootkit detection multi-class deep learning techniques (KRDMCDLT). Deep learning algorithms are utilized to recognize the Kernel rootkit from a batch of data by selecting essential properties for learning tracking models. Thus, by identifying the OS malware, trojan assaults can be stopped before they can access infected data. This Kernel rootkit detection was tested in a Google Cloud Platform (GCP) computing system. [ABSTRACT FROM AUTHOR]

This research paper focuses on developing a complete system for daily automation testing of comprehensive web applications implemented on cloud environments, encompassing the execution of automated API tests, real-time monitoring and results visualization of the testing environments. Despite the tools for developing automated API tests, the study uses containerization tools as Docker and Kubernetes, showcasing their integration into a cohesive testing framework. Furthermore, the implementation leverages the potential of the Google Cloud Platform (GCP) to demonstrate the usage of cloud computing services, emphasizing scalability and efficiency. Additionally, the paper details the integration of monitoring tools, specifically Elasticsearch, to assess and visualize the health and performance of the underlying Kubernetes cluster. Through a comprehensive approach, encompassing a wide variety of tools, the research establishes a continuous and automated testing environment essential for cutting-edge software applications. Results showcase the successful orchestration of all the technologies, highlighting their collective impact on achieving a robust and efficient system for continuous automation testing and monitoring.

Through an examination of ethnographic fieldwork data, this paper explores the ways in which cloud‐based collaborative technologies created by Google mediate (Latour 1994) teachers' discussions around, agreement of and enactments of their classroom practices. Bringing together concepts from actor‐network theory and literacy studies, this paper argues for greater consideration of the role(s) of increasingly‐used technologies in shaping teachers' practices and suggests a framework for exploring this issue further. [ABSTRACT FROM AUTHOR]

Abstract Background As genome sequencing becomes better integrated into scientific research, government policy, and personalized medicine, the primary challenge for researchers is shifting from generating raw data to analyzing these vast datasets. Although much work has been done to reduce compute times using various configurations of traditional CPU computing infrastructures, Graphics Processing Units (GPUs) offer opportunities to accelerate genomic workflows by orders of magnitude. Here we benchmark one GPU-accelerated software suite called NVIDIA Parabricks on Amazon Web Services (AWS), Google Cloud Platform (GCP), and an NVIDIA DGX cluster. We benchmarked six variant calling pipelines, including two germline callers (HaplotypeCaller and DeepVariant) and four somatic callers (Mutect2, Muse, LoFreq, SomaticSniper). Results We achieved up to 65 × acceleration with germline variant callers, bringing HaplotypeCaller runtimes down from 36 h to 33 min on AWS, 35 min on GCP, and 24 min on the NVIDIA DGX. Somatic callers exhibited more variation between the number of GPUs and computing platforms. On cloud platforms, GPU-accelerated germline callers resulted in cost savings compared with CPU runs, whereas some somatic callers were more expensive than CPU runs because their GPU acceleration was not sufficient to overcome the increased GPU cost. Conclusions Germline variant callers scaled well with the number of GPUs across platforms, whereas somatic variant callers exhibited more variation in the number of GPUs with the fastest runtimes, suggesting that, at least with the version of Parabricks used here, these workflows are less GPU optimized and require benchmarking on the platform of choice before being deployed at production scales. Our study demonstrates that GPUs can be used to greatly accelerate genomic workflows, thus bringing closer to grasp urgent societal advances in the areas of biosurveillance and personalized medicine.

Background: As genome sequencing becomes better integrated into scientific research, government policy, and personalized medicine, the primary challenge for researchers is shifting from generating raw data to analyzing these vast datasets. Although much work has been done to reduce compute times using various configurations of traditional CPU computing infrastructures, Graphics Processing Units (GPUs) offer opportunities to accelerate genomic workflows by orders of magnitude. Here we benchmark one GPU-accelerated software suite called NVIDIA Parabricks on Amazon Web Services (AWS), Google Cloud Platform (GCP), and an NVIDIA DGX cluster. We benchmarked six variant calling pipelines, including two germline callers (HaplotypeCaller and DeepVariant) and four somatic callers (Mutect2, Muse, LoFreq, SomaticSniper). Results: We achieved up to 65 × acceleration with germline variant callers, bringing HaplotypeCaller runtimes down from 36 h to 33 min on AWS, 35 min on GCP, and 24 min on the NVIDIA DGX. Somatic callers exhibited more variation between the number of GPUs and computing platforms. On cloud platforms, GPU-accelerated germline callers resulted in cost savings compared with CPU runs, whereas some somatic callers were more expensive than CPU runs because their GPU acceleration was not sufficient to overcome the increased GPU cost. Conclusions: Germline variant callers scaled well with the number of GPUs across platforms, whereas somatic variant callers exhibited more variation in the number of GPUs with the fastest runtimes, suggesting that, at least with the version of Parabricks used here, these workflows are less GPU optimized and require benchmarking on the platform of choice before being deployed at production scales. Our study demonstrates that GPUs can be used to greatly accelerate genomic workflows, thus bringing closer to grasp urgent societal advances in the areas of biosurveillance and personalized medicine. [ABSTRACT FROM AUTHOR]

Cloud servers are generally used for data storage and remote office activities, but it can be applied for gaming purposes, where cloud servers can be paired with virtual machines and gaming platform that can be accessed by users via an internet connection. This makes the device used by user no longer needs to process resources because the workload is carried out by virtual machines on cloud server. The author designs a cloud gaming system using Google cloud platform as a cloud server and parsec as an optimizer that is attached to a virtual machine for game computing purposes. Author takes measurements of the cloud gaming system using 2 test games varying from low to middle specifications. Resource testing on central processing unit (CPU) and random-access memory (RAM) usage on the user side is below 40% when running game 1 and below 44% when running game 2, while on the system it reaches a capacity above 40% for CPU and RAM and 99% maximum on graphics processing unit (GPU). Quality of service testing of the system is carried out at bandwidths of 5, 10, and 30 Mbps with a minimum bandwidth of 10 Mbps. In general, there are a little difference that occurred between test game and different bandwidths. [ABSTRACT FROM AUTHOR]

Issues of relevance, cyber security of data, confidentiality is quite relevant today, as a minor «hole» in data can lead an enterprise to bankruptcy. But not all enterprises have automated their processes at all to date, not to mention their digitalization, and therefore for them the issue of transition to the adjustment of the entire data collection process is acute and on the verge of competitiveness. Therefore, the article considers the concept of SMAC, which acts as a basis for the transition of an enterprise from e-entrepreneurship to digital entrepreneurship and is based on the need to use social networks, mobility, analytics and cloud technologies. The characteristics of each stage are given and it is indicated what benefit the data obtained in the SMAC concept and used in the digital analytics process can bring to the enterprise. Based on the fact that a comfortable method for enterprises in the context of cloud technologies and digital analytics, Google Cloud Platform was reviewed, which is able to adapt to various requirements and provides many products capable of satisfying enterprises in the context of transformation and modernization. And the issue of data confidentiality and the possibility of having rights to data obtained from a website, a mobile application, etc. is solved by using the server container presented by Google within the framework of Google Tag Manager. Server-Side is capable of completely inverting the perception of data while satisfying the main legislation on data privacy and security (CPRA, CCPA, GDPR). The advantages of the company's transition to Google Tag Manager Server-Side are given, as well as the principle of operation of the server container. In the context of the study, it was proposed to create a full-fledged complex capable of combining not only operational but also strategic activities of the enterprise. [ABSTRACT FROM AUTHOR]

Stream Processing Systems (SPSs) dynamically process input events. Since the input is usually not a constant flow, presenting rate fluctuations, many works in the literature propose to dynamically replicate SPS operators, aiming at reducing the processing bottleneck induced by such fluctuations. However, these SPSs do not consider the problem of load balancing of the replicas or the cost involved in reconfiguring the system whenever the number of replicas changes. We present in this paper a predictive model which, based on input rate variation, execution time of operators, and queued events, dynamically defines the necessary current number of replicas of each operator. A predictor, composed of different models (i.e., mathematical and Machine Learning ones), predicts the input rate. We also propose a Storm-based SPS, named PA-SPS, which uses such a predictive model, not requiring reboot reconfiguration when the number of operators replica change. PA-SPS also implements a load balancer that distributes incoming events evenly among replicas of an operator. We have conducted experiments on Google Cloud Platform (GCP) for evaluation PA-SPS using real traffic traces of different applications and also compared it with Storm and other existing SPSs. • A prediction-based model for dynamically adapting SPS with highly variable traffic rate environments. • A new event grouping strategy that exploits operators' use. • The design and implementation of an elastic stream processing system based on Apache Storm with autonomic behaviour. • Performance evaluation of the proposed SPS through extensive experiments using real-world datasets on Google Cloud Platform. [ABSTRACT FROM AUTHOR]

"Amazon Big Data", conducts a thorough analysis on the e-commerce industry using big data and how certain trends can affect the functioning of the organizations delving in the field. With the growth of e-commerce, there has been a significant rise of the online consumers' footprint. Companies such as Amazon, Flipkart and other e-commercial platforms have accrued huge chunks of consumer information, especially since the start of the pandemic. In this industry, reviews and ratings given to a product play a crucial role in determining the sentiments of the customers associated towards making the final purchase. Such factors account for the brand's sales and image. In today's landscape, a careful customer goes through the ratings of the product, its reviews which serve as a medium of screening. In a tie between two similar products, customers purchase a product with higher ratings and better reviews. Therefore, this leads us to the development of an ideal rating metric that is significant for the sales of the product. Moreover, become a tool for product differentiation. This manuscript is a method to standardize the ratings of customers and preserve the sanctity of the data. We discuss models which are an amalgamation of customer ratings, their respective reviews and a sentiment scored derived from the same review. These models also help us define customer clusters with different personalities based on their reviews and ratings. In addition to this, customer segmentation is a future scope to deep dive into the sales data and understand the financial behavior of a customer. [ABSTRACT FROM AUTHOR]

This article describes the use of cloud services. The analysis of the most popular cloud services and comparison of their basic functionality is carried out. Examples of practical application of cloud services among well-known companies around the world are given. Basic recommendations to secure access organization to cloud-based services are presented. [ABSTRACT FROM AUTHOR]

Introduction. The widespread use of the Internet leads to a fast increase of the quantity of data that goes into it. This generates interest in intruders which try different approaches to steal this data. One of the most popular approaches is SQL injection. There are a lot of measures which help to prevent and decrease the risk of being subjected to this attack: usage of code analysis tools, usage of firewalls which can filter dangerous traffic etc. Usage of reverse proxy is analysed in this article, which with the help of machine learning algorithms checks requests for SQL injections and based on the result passes or forbids the request to go. It is worth mentioning that such a solution is not a replacement of human expertise but addition to it, which with the help of big data can give an accurate result in most cases. The purpose of the paper is to analyse and show effectiveness of usage of machine learning in information system security provisioning tasks with the system working in serverless architecture. Results. A system is designed and developed which with the help of machine learning classifies received requests. The system is deployed to the cloud hosting Google Cloud Platform and integrated into an application which is designed according to the serverless architecture principles. Multiple algorithms were used to compare effectiveness of the system and percentage of successful results were calculated for each of them. Also, an average time of request execution is calculated for each algorithm. Conclusions. Each algorithm’s result of successful request classification is above 90% which is considered to be more than acceptable. The result can be improved using more data to train machine learning models. The system fits for work in serverless applications thanks to the simplicity of its integration but it should be considered if it fits from a hardware rent point of view.

Structure-based virtual screening protocol to identify potent acetylcholinesterase inhibitors was retrospectively validated. The protocol could be employed to examine the potential of designed compounds as novel acetylcholinesterase inhibitors. In a research project designing short peptides as acetylcholinesterase inhibitors, peptide AEYTR emerged as one of the potential inhibitors. This article presents the design of AEYTR assisted by the validated protocol and guided by literature reviews followed by molecular dynamics studies to examine the interactions of the pentapeptide in the binding pocket of the acetylcholinesterase enzyme. The molecular dynamics simulations were performed using YASARA Structure in Google Cloud Platform. The peptide AEYTR was identified in silico as a potent acetylcholinesterase inhibitor with the average free energy of binding (DG) of -19.138 kcal/mol.

Internet of Things (IoT) aims to connect the real world made up of devices, sensors and actuators to the virtual world of Internet in order to interconnect devices with each other generating information from the gathered data. Devices, in general, have limited computational power and limited storage capacity. Cloud Computing (CC) has virtually unlimited capacity in terms of storage and computing power, and is based on sharing resources. Therefore, the integration between IoT and CC seems to be one of the most promising solutions. In fact, many of the biggest companies that offer Cloud Services are focusing on the IoT world to offer services also in this direction to their users. In this paper we compare the three main Cloud Platforms (Amazon Web Services, Google Cloud Platform and Microsoft Azure) regarding to the services made available for the IoT. After describing the typical architecture of an IoT application, we map the Cloud-IoT Platforms services with this architecture analyzing the key points for each platform. At the same time, in order to conduct a comparative analysis of performance, we focus on a service made available by all platforms (MQTT middleware) building the reference scenarios and the metrics to be taken into account. Finally, we provide an overview of platform costs based on different loads. The aim is not to declare a winner, but to provide a useful tool to developers to make an informed choice of a platform depending on the use case.

The article is devoted to the development of the Web application architecture with the distribution of the application by components into 2 tiers connected by the messaging system, and using of load balancing by horizontal scaling in the Kubernetes cloud cluster. Queue length in the message broker is used for scaling as critirea. This approach allows to increase resource usage efficiency of the system. The relevance of the topic is due to the widespread use of various web services and web applications. When the load of web applications increases it can lead to delays or even failure of these services. Therefore, the issues of creating reliable, fault-tolerant, and scalable systems become extremely important. If the load is greater than the system or service can withstand, it may result in denial of service or termination of service. Also, the load can be unevenly distributed to services over a period of time, and therefore, even if the system has enough resources to withstand high loads, during periods of low load, these resources will not be used, resulting in problems of inefficient use of resources and overspending. The proposed system was deployed in the Google Cloud environment. The components of the server part of the Web application are grouped into 2 tiers. The microlayer components of the first layer analyze HTTP client requests and transmit messages to the components of the second layer using the Google Pub-Sub messaging system. It is proposed to make all relatively "difficult" operations on the components of the second layer. For the numerical experiment, a system was implemented using an algorithm for horizontal scaling of microservices based on the current number of messages in the queue. Load testing of the system was performed, which showed that the created system is capable of processing more than 2 times more requests for the same period of time compared to the system without scaling. Ref. 6, pic. 7, tabl. 1.

This paper presents a real-time traffic flow estimation model that uses open-source travel time data provided by Google Cloud Platform (GCP) in addition to physical/operational attributes of road segments. The inclusion of such attributes was targeted to enhance the accuracy of the estimation process, especially in traffic environments with low level of organization and loose adherence to traffic right-of-way. The adopted case study is the Ring Road of Greater Cairo Region, Egypt. Four attributes were selected based on their observed impact on the expressway speed-flow relationship: number of lanes; alignment curvature; transit stops; and prevailing maneuvers. A data collection plan was executed to obtain GCP-based travel speed data for different road segments with concurrent traffic counts. Data was used to train an ANN model that uses GCP-based travel speeds and road segments' attributes to provide online traffic flow estimates. The developed model reported MAPE of 4.87% compared to 9.41% as best result obtained from a parametric traffic flow model and 6.8% for an extended version of a locally calibrated 3-regime model. Residual analysis reported random distribution of residual errors with 73% of points with residual error between+/-100 pcu/hr/lane. Trained ANN model was used to estimate flow adjustment factors that compare flow estimates at given speeds for different types of road segments to that of a base case one. Estimated adjustment factors ranged from 0.828 to 1.415. Reported results highlight the potential of using open-source GCP travel time data for real-time traffic flow estimation and the significance of including physical/operational attributes of road segments in enhancing estimation accuracy. [ABSTRACT FROM AUTHOR]

Порівняно використання пропрієтарної хмарної платформи Google Cloud Functions з платформою з відкритим вихідним кодом Apache OpenWhisk для написання безсерверних застосунків на базі Google Cloud Platform. Для порівняння обрано такі критерії: підтримувані мови програмування, зручність та швидкість розроблення і розгортання функцій, можливості передавання параметрів до функцій, опції моніторингу стану функцій, швидкість холодного старту. У результаті визначено основні переваги й недоліки кожної платформи. На основі особливостей платформ наведено рекомендації щодо використання. Результати дослідження можуть бути використані для подальшого вивчення FaaS технологій, оскільки в цій царині існує величезна кількість рішень, котрі також необхідно порівнювати.

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En los últimos años el Big Data se abre camino entre las principales herramientas de análisis de mercado, vinculándose a las técnicas de Machine Learning con el fin de aprender sobre los datos que se posee. Una de las áreas de mayor crecimiento es el Procesamiento de Lenguaje Natural que proporciona al investigador datos sobre estructuras y significados de texto. Con el fin de profundizar en esta área, Google ha creado la API Natural Language, permitiendo a los investigadores trabajar con distintos aspectos de las funciones del lenguaje entre ellas el análisis de sentimiento, proporcionando información sobre la opinión emocional predominante de un contenido seleccionado previamente y permitiendo obtener un score que analiza la valencia de las emociones con valores dicotómicos. El objeto de este estudio es analizar los distintos procesos que un investigador tiene que utilizar para obtener información útil para sus investigaciones. Desde la extracción de información, hasta la obtención de datos que ayuden al investigador a obtener conclusiones, se desarrolla un largo proceso del tratamiento de la información. El estudio nos mostrará como las diversas herramientas de las que dispone Google en su plataforma Google Cloud Platform, aportan a un investigador el apoyo necesario para el desarrollo de su trabajo, una vez que ya se cuenta con la información a analizar. Además, se complementará con herramientas de rastreo para la extracción del texto que se desea, en función de donde se encuentre esta.

Experimental chambers play an important role in plant science and agronomy by maintaining and providing ideal conditions so that experimental data can be collected under specific and controlled conditions. To ensure that the environmental parameters inside the chamber are within the required control limits, the best solution is to be able to record and monitor them. This recording and monitoring task are carried out in order to increase the repeatability of the experiments in the future, in addition to providing researchers with real-time information about the conditions in which the trials are found under in vivo and in vitro conditions.