Amazon Web Services Research Articles

Support vector regression-based state of charge estimation for batteries: cloud vs non-cloud

Embracing the potential of cloud technology in the field of electric vehicle advancements, this paper explores the application of support vector regression (SVR) for accurate state of charge (SOC) estimation of lithium-ion batteries in various computational landscapes. This study aims to scrutinize and compare the performance of SOC estimation, with a specific focus on precision, computational efficiency, and execution speed. The investigation is conducted across diverse environments, including a traditional non-cloud setup and two cloud-based platforms-a standard cloud environment employing Amazon web services (AWS) EC2 servers and an enhanced configuration utilizing the MATLAB production server. The investigation not only emphasizes the effectiveness of cloud integration but also provides valuable insights into the strengths and weaknesses of the proposed methodology. The experimental results contribute to a nuanced understanding of the methodology’s performance, shedding light on its potential implications for advancing electric vehicle technologies. This study thus extends its significance beyond technical considerations, providing a broader perspective on its relevance to global electrification initiatives.

Cloud Storage Cost Optimization: Advanced Techniques and Case Studies

The growing usage of cloud storage has revolutionized data management by offering unparalleled scalability and accessibility. However, optimizing cloud storage prices remains challenging, sometimes leading to significant unforeseen costs. This article explores a wide range of advanced techniques and practical case studies that enable enterprises to effectively navigate the intricacies of cost optimization across top cloud providers, including Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS). Beyond selecting the most appropriate storage type for individual workloads (block, blob, or file storage), we explore advanced strategies that unlock substantial cost savings. From leveraging contractual negotiations for high-volume storage users to utilizing dedicated data transfer devices for streamlined on-premises migration, this paper equips readers with a diverse arsenal of optimization tools. We explore best practices for data lifecycle management, such as tiered backups and granular retention policies, to optimize beyond merely technical solutions. This way, we ensure that data is held at the most affordable tier depending on its frequency of access and compliance with legal requirements. By consistently analyzing cloud storage consumption and adapting responsive strategies to changing requirements, enterprises may retain optimal performance while ensuring long-term financial sustainability. This paper serves as a valuable resource for organizations seeking to master the intricacies of cloud storage cost optimization. By implementing the advanced techniques and best practices outlined herein, cloud users can confidently unlock significant cost savings and maximize the return on their cloud storage investments.

DEVOPS OPTIMIZED NAVIGATION: BUILDING A DEVOPS CI/CD PIPELINE

DevOps Optimized Navigation: Building a DevOps CI/CD pipeline aims to implement devops practices via building campus navigation for our college. The application displays different blocks of our college where the user can navigate. This system will be useful especially for new comers and people those who are unfamiliar with the campus layout. People equipped with any mobile phone can easily scan the QR Code to open the website and select the destination for navigation. The optimal path is visible via google maps. For DevOps, we have used Amazon Web Services(AWS) .GitHub is used as version control platform. A tool called Jenkins is used for continuous integration and continuous deployment which is a open source tool. Our Project is a flask application which is deployed using Docker container. Our project provides a solution that not only simplifies the navigation but also ensures responsiveness to different screen sizes and global accessibility to provide user friendly experience. KEYWORDS – DevOps, GitHub, AWS Cloud, Jenkins, Docker, Flask, Navigation

Implementation of cloud computing in the digital accounting system of logistics companies

The aim of the article is to determine the capabilities of cloud services for meeting the needs of logistics companies in the management of supply chains and digital accounting. The study provides a review of current academic publications, analysis of official documentation of cloud service provider companies, and expert opinion of the authors of the study. The study was based on information about the 5 most popular cloud services used by companies to perform mathematical calculations. Amazon Web Services focus on reliability and scalability, providing a wide range of data storage and processing services, as well as performing serverless mathematical calculations. Microsoft Azure stands out for its integrated solutions, as well as data management and analytics services. Google Cloud offers a wide range of development and analytics tools, including data visualization and data sharing. Oracle Cloud provides comprehensive financial and database management solutions. SAP Cloud Platform specializes in financial management and analytics solutions. The results of the research open up prospects for studying the problems of integrating services built on different platforms and finding optimal solutions for combining the existing system of mathematical calculations in a logistics company with the offered cloud-based services.

A Novel Edge Platform Streamlining Connectivity between Modern Edge Devices and the Cloud

This study presents a newly developed edge computing platform designed to enhance connectivity between edge devices and the cloud in the agricultural sector. Addressing the challenge of synchronizing a central database across 850 remote farm locations in various countries, the focus lies on maintaining data integrity and consistency for effective farm management. The incorporation of a new edge device into existing setups has significantly improved computational capabilities for tasks like data synchronization and machine learning. This research highlights the critical role of cloud computing in managing large data volumes, with Amazon Web Services hosting the databases. This paper showcases an integrated architecture combining edge devices, networks, and cloud computing, forming a seamless continuum of services from cloud to edge. This approach proves effective in managing the significant data volumes generated in remote agricultural areas. This paper also introduces the PAIR Mechanism, which is a solution developed in response to the unique challenges of agricultural data management, emphasizing resilience and simplicity in data synchronization between cloud and edge databases. The PAIR Mechanism’s potential for robust data management in IoT and cloud environments is explored, offering a novel perspective on synchronization challenges in edge computing.

Optimizing Big Data Insights with Serverless Architecture

Big data is the huge amount of data, which can be structured, semi-structured, or unstructured, that is required for current commercial processes. Big Data efforts and technologies are used to analyze large amounts of data in order to gain insights critical for strategic decision-making. Data size is constantly rising, reaching petabytes, exabytes, zettabytes, and even yottabytes, offering substantial management and processing issues. In practice, managing massive amounts of data involves several obstacles, such as server management, storage, clustering, and algorithm deployment. Manual intervention hampers the creation of successful Cloud-based data analysis platforms. Serverless computing provides a solution by offering clients pay-per-use backend services, reducing the need for users to manage server operations. This article describes a serverless architecture for large data analytics, including implementation, maintenance, and governance on Amazon Web Services (AWS). Furthermore, it investigates the differences between traditional and big data analytics in a serverless system

A Review on Searchable Encryption Functionality and the Evaluation of Homomorphic Encryption

Cloud Service Providers, exemplified by industry leaders like Google Cloud Platform, Microsoft Azure, and Amazon Web Services, deliver a dynamic array of cloud services in an ever-evolving landscape. This sector is witnessing substantial growth, with enterprises such as Netflix and PayPal heavily relying on cloud infrastructure for various needs such as data storage, computational resources, and various other services. The adoption of cloud solutions by businesses not only facilitates cost reduction but also fosters flexibility and supports scalability. Despite the undeniable advantages, concerns surrounding security and privacy persist in the realm of Cloud Computing. Given that Cloud services are accessible via the internet, there is a potential vulnerability to unauthorized access by hackers or malicious entities from anywhere in the world. A crucial aspect of addressing this challenge is the implementation of robust security measures, particularly focusing on data protection. To safeguard data in the Cloud, a fundamental recommendation is the encryption of data prior to uploading. Encryption should be maintained consistently, both during storage and in transit. While encryption enhances security, it introduces a potential challenge for data owners who may need to perform various operations on their encrypted data, such as accessing, modifying, updating, deleting, reading, searching, or sharing them with others. One viable solution to balance the need for data security and operational functionality is the adoption of Searchable Encryption (SE). SE operates on encrypted data, allowing authorized users to perform certain operations without compromising the security of sensitive information. The effectiveness of SE has notably advanced since its inception, and ongoing research endeavors aim to further enhance its capabilities. This paper provides a comprehensive review of the functionality of Searchable Encryption, with a primary focus on its applications in Cloud services during the period spanning 2019 to 2023. Additionally, the study evaluates one of its prominent schemes, namely Fully Homomorphic Encryption (FHE). The analysis indicates an overall positive trajectory in SE research, showcasing increased efficiency as multiple functionalities are aggregated and rigorously tested.

The Rapid Assessment of Avoidable Blindness survey: Review of the methodology and protocol for the seventh version (RAAB7)

The Rapid Assessment of Avoidable Blindness (RAAB) is a population-based cross-sectional survey methodology used to collect data on the prevalence of vision impairment and its causes and eye care service indicators among the population 50 years and older. RAAB has been used for over 20 years with modifications to the protocol over time reflected in changing version numbers; this paper describes the latest version of the methodology–RAAB7. RAAB7 is a collaborative project between the International Centre for Eye Health and Peek Vision with guidance from a steering group of global eye health stakeholders. We have fully digitised RAAB, allowing for fast, accurate and secure data collection. A bespoke Android mobile application automatically synchronises data to a secure Amazon Web Services virtual private cloud when devices are online so users can monitor data collection in real-time. Vision is screened using Peek Vision’s digital visual acuity test for mobile devices and uncorrected, corrected and pinhole visual acuity are collected. An optional module on Disability is available. We have rebuilt the RAAB data repository as the end point of RAAB7’s digital data workflow, including a front-end website to access the past 20 years of RAAB surveys worldwide. This website (https://www.raab.world) hosts open access RAAB data to support the advocacy and research efforts of the global eye health community. Active research sub-projects are finalising three new components in 2024-2025: 1) Near vision screening to address data gaps on near vision impairment and effective refractive error coverage; 2) an optional Health Economics module to assess the affordability of eye care services and productivity losses associated with vision impairment; 3) an optional Health Systems data collection module to support RAAB’s primary aim to inform eye health service planning by supporting users to integrate eye care facility data with population data.

PENGUJIAN KINERJA WEB SERVER ELASTIC CLOUD COMPUTE (EC2) FREE TIER PADA AMAZON WEB SERVICE (AWS) MENGGUNAKAN JMETER

The research aims to evaluate the capacity of Amazon Web Service's (AWS) Elastic Cloud Compute (EC2) Free Tier Web Server using the JMeter testing tool. The objective is to ascertain the extent of the server's load-bearing capabilities. Tests were conducted, loading 500 users within a 10-second range and 1000 users within a 1-second range. Despite being a free service, test results showed the server's capability to handle high loads without errors (0% error rate). These findings illustrate the free service's ability to maintain stable performance under significant user loads. The utilization of EC2 Free Tier on AWS demonstrates its capacity to handle heavy tasks without compromising service quality. From about 5 steps in each of two tests, the value for sample time / response time in the initial test of the Amazon Web Service Web server is 59 / ms (millisecond) and the average latency value in the second test, namely 7.213 /ms (millisecond). Both of these gained CPU Utilization from minum, average and maximum with final rate are zero Error. This outcome concludes that the free server possesses robust and reliable capabilities, instilling confidence in an effective and efficient server solution even under high usage levels without additional costs. 

Responsible AI at the Vanderbilt Television News Archive: A Case Study

We provide an overview of the use of machine-learning and artificial intelligence at the Vanderbilt Television News Archive (VTNA). After surveying our major initiatives to date, which include the full transcription of the collection using a custom language model deployed on Amazon Web Services (AWS), we address some ethical considerations we encountered, including the possibility of staff downsizing and misidentification of individuals in news recordings.

Leveraging public cloud infrastructure for real-time connected vehicle speed advisory at a signalized corridor

In this study, we developed a real-time connected vehicle (CV) speed advisory application that uses public cloud services and tested it on a simulated signalized corridor for different roadway traffic conditions. First, we developed a scalable serverless cloud computing architecture leveraging public cloud services offered by Amazon Web Services (AWS) to support the requirements of a real-time CV application. Second, we developed an optimization-based real-time CV speed advisory algorithm by taking a modular design approach, which makes the application automatically scalable and deployable in the cloud using the serverless architecture. Third, we developed a cloud-in-the-loop simulation testbed using AWS and an open-source microscopic roadway traffic simulator called Simulation of Urban Mobility (SUMO). Our analyses based on different roadway traffic conditions showed that the serverless CV speed advisory application meets the latency requirement of real-time CV mobility applications. Besides, our serverless CV speed advisory application reduced the average stopped delay (by 77%) and the aggregated risk of collision (by 21%) at the signalized intersections of a corridor. These prove the feasibility as well as the efficacy of utilizing public cloud infrastructure to implement real-time roadway traffic management applications in a CV environment.

As Hype Fades, LLMs Gaining Acceptance in Upstream as New Age Research and Coding Tool

After using ChatGPT-4 for more than a year, Clinton Lott has made familiarity with the large language model (LLM), a requirement for new software programming hires. The director of Houston-based Spotter Tech gave this reasoning: “Taking 2 hours to do something that ChatGPT can do in 15 seconds is unacceptable today.” It’s a line you’re likely to hear from the most ardent champions of LLMs which have simultaneously reignited the world’s interest in and ire for artificial intelligence (AI). And while Spotter Tech, a small hydraulic fracturing data specialist, may be an exception with its hiring practices, it is far from the only upstream company embracing the new technology. Both BP and Shell have recently given thousands of employees access to Microsoft’s new cloud service called Copilot, which at its core is powered by GPT-4, OpenAI’s latest and most capable LLM. Others including ExxonMobil have placed use of LLMs under lockdown while still signaling a long-term interest in their embrace. As chair of SPE’s Data Science & Engineering Analytics Technical Section, Pushpesh Sharma has spent the past several months speaking with peers in the industry about how and where LLMs are being used. The product manager for field automation firm Aspen Technology told JPT that it’s clear from those conversations that a good number of energy firms are interested in tailoring the technology to their needs, albeit many are doing so quietly as it remains early days. “The big focus right now is on what they call fine tuning,” he said. “You take an existing base model that one of the big tech companies have created and make it more of a fit for your business. It’s kind of like taking a graduate student and then letting them work on their PhD with very specific subject matter.” While OpenAI’s ChatGPT has become the popular symbol of LLMs, there are a growing number of alternatives in the mix. In February, Google introduced its latest model called Gemini 1.5 which seeks to rival GPT-4. There are also open source LLMs that oil and gas companies can use as templates for internal projects. Or they may turn to other big tech firms such as NVIDIA and Amazon Web Services (AWS) which are in the business of helping industrials build and train their own private, custom LLMs. SPE joined the trend after inking a Memorandum of Understanding (MoU) in October with AI firm i2k Connect and Aramco Americas. The collaboration is expected to result in a new domain-specific LLM that industry engineers and researchers can use to answer questions with more technical depth than can be done with the current slate of commercial offerings. One enabling aspect of this current phase of experimentation with LLM technology is retrieval-augmented generation (RAG). The method is considered essential for models to provide more-accurate and actionable responses by extending their scope beyond initial training datasets to include more authoritative and even real-time data.

SAMHSA expands technology services partnership

The Substance Abuse and Men‐tal Health Services Administration (SAMHSA) has expanded its long‐standing partnership with ICF, a global consulting and technology services provider, with five new subcontracts to support mental health programs, including evaluation and communications support for the agency's 988 Suicide & Crisis Lifeline. The agreements have a combined value of $24 million, an ICF news release stated. “These new agreements expand our long‐standing partnership with SAMHSA to help the agency better evaluate the impact of and raise awareness for mental health programs that change and save lives,” said Jennifer Welham, ICF senior vice president for health, people and human services. For three of the five service agreements, ICF will deliver a variety of behavioral health and technology‐based support services, totaling $16.1 million. Each has a term of five years, including a one‐year base and four one‐year options. Services will help address the nation's suicide crisis and include communications and data visualization support for the 988 Lifeline program (which was awarded in the second quarter of 2023) and the evaluation of the Zero Suicide Program, including building a large cloud‐based data collection and reporting system for both on Amazon Web Services.

Software Engineering for Developing a Cloud Computing Museum-Guide System

The aim of this article proposes an innovative solution for developing a museum-guide system, which employs a voice-activated assistant paired with 3-D hologram displays, that utilizes Amazon web services (AWS) to enhance the visitor experience at the Bahrain National Museum. The proposed system uses software engineering as a service (SaaS) and involves an agile development process model with microservice architecture that adapts cloud computing capabilities to provide scalability, reliability, and maintainability. The proposed system enhances the existing museum infrastructure and databases through a flexible, API-based architecture. The proposed system is highly adaptable and flexible in different desirable aspects of user experience goals. The implementation results proved that the system is highly reliable, adaptable, and efficient and has the potential to improve the user experience by transforming the way museum visitors explore and interact with user interfaces of the museum-guide system.

Deep Reinforcement Learning for Autonomous Driving in Amazon Web Services DeepRacer

The development of autonomous driving models through reinforcement learning has gained significant traction. However, developing obstacle avoidance systems remains a challenge. Specifically, optimising path completion times while navigating obstacles is an underexplored research area. Amazon Web Services (AWS) DeepRacer emerges as a powerful infrastructure for engineering and analysing autonomous models, providing a robust foundation for addressing these complexities. This research investigates the feasibility of training end-to-end self-driving models focused on obstacle avoidance using reinforcement learning on the AWS DeepRacer autonomous race car platform. A comprehensive literature review of autonomous driving methodologies and machine learning model architectures is conducted, with a particular focus on object avoidance, followed by hands-on experimentation and the analysis of training data. Furthermore, the impact of sensor choice, reward function, action spaces, and training time on the autonomous obstacle avoidance task are compared. The results of the best configuration experiment demonstrate a significant improvement in obstacle avoidance performance compared to the baseline configuration, with a 95.8% decrease in collision rate, while taking about 79% less time to complete the trial circuit.

Built Custom Alexa Skills using AWS

Abstract:The creation of unique Alexa skills using the Amazon Web Services (AWS) infrastructure is the focus of this project. Alexa skills are voice- activated features that improve Alexa, the virtual assistant from Amazon, in terms of functionality. The project's main goal is to demonstrate the flexibility and adaptability of AWS in voice- activated interactions by developing custom skills that are suited to certain use cases or applications. During the development process, the bespoke Alexa skills are hosted and executed using AWS Lambda, an event-driven computing service. With AWS Lambda, code can be seamlessly integrated without requiring server management, guaranteeing a scalable and effective solution. The stability and dependability of the custom Alexa skills are further enhanced by the extensive suite of tools and services offered by AWS, including the Alexa Skills Kit (ASK), DynamoDB for data storage, and IAM (Identity and Access Management) for security. The complexities of voice-activated technologies, AWS infrastructure, and Alexa skill development are investigated through this research. With the help of the Alexa ecosystem and customized skills, companies, developers, and enthusiasts can use AWS's power to create unique and interesting voice experiences. The project's main ideas are summarized in this abstract, which highlights the use of AWS technology to develop customized and inventive Alexa skills. Keywords:Custom Alexa skills, AWS, Voice- activated technology, Alexa Skills Kit, AWS Lambda, DynamoDB, IAM.

Protecting Sensitive Data with Secure Data Enclaves

A Secure Data Enclave is a system that allows data owners, such as governments and private firms, to control data access and ensure data security while facilitating approved uses of data by other parties. This model of data use offers additional protections and technical controls for the data owner compared to the more commonly used approach of transferring data from the owner to another party through a data sharing agreement. Under the data use model, the data owner retains full transparency and auditing over the other party's access, which can be difficult to achieve in practice with even the best legal instrument for data sharing. We describe the key technical requirements for a Secure Data Enclave, provide a reference architecture for its implementation on Amazon Web Services using managed cloud services, and describe four use cases of this architecture in partnerships with state governments to control access to sensitive administrative data.

Exploring Usage of AWS Lambda in Data Processing

Cloud computing is changing game all around the industry. It’s delivering computing resources, such as storage, processing power without investing in and managing physical infrastructure, offering a more flexible and cost-effective approach to meet computational demands. Serverless computing is a cloud computing execution model where cloud providers automatically manage the infrastructure needed to run applications and scale. This paper examines use of AWS Lambda in Data processing, the leading serverless computing service of Amazon Web Services (AWS). Our study will explore the architecture, integration of AWS Lambda with other AWS services, real-world use cases, proposing best practices for Data processing. This research will provide a complete understanding of AWS Lambda and findings here are a valuable resource for people looking to unleash the benefits of AWS Lambda in a scalable and high-performance cloud-native application architecture.

GNSS Radio Occultation Data in the AWS Cloud

AbstractRadio occultation (RO) by the Earth's atmosphere of the transmitted signals of the Global Navigation Satellite Systems' satellites has improved numerical weather prediction, has benefited atmospheric process studies, and benchmarked climate change by its strong traceability to the international definition of the second. Until now, research with RO has been isolated to the few centers that actually process the data and impracticable elsewhere because of limitations on data formats, organization and volume of the data, download bandwidth, and the lack of a database that can be queried before downloading data. We announce the availability of RO data in the Registry of Open Data of Amazon Web Services (AWS). RO data are freely available for download for the user and free to process and store for the manager of the repository. RO data as processed by the COSMIC Program Office at the University Corporation for Atmospheric Research, by the Radio Occultation Meteorology Satellite Application Facility (ROM SAF), by EUMETSAT, and by the NASA Jet Propulsion Laboratory are available beginning with calibrated (excess) phase, extending to bending angle and refractivity and to retrievals of pressure, temperature and water vapor. The same RO repository also contains a metadata database that permits simple querying, filtering, and download of data using a Python application programming interface provided by this same project. Four tutorial demonstrations are provided as jupyter notebooks to provide representative samples of code to perform common research activities with RO data, including database querying and analysis, inter‐center comparison of RO retrieval performance, and atmospheric process studies.

Optimizing Trial Experiences in Cloud Platforms: Challenges, Strategies, and Impact on User Engagement and Conversion Rate

This article delves into the significance of trial experiences in the context of cloud-based solutions, a crucial aspect of today's digital business landscape. As cloud platforms reshape service delivery, the Freemium and Free Trial models emerge as compelling strategies for customer engagement. These models not only offer a glimpse into the service's capabilities but also serve as a critical touchpoint for building trust and rapport with potential customers. Optimizing trial experiences, however, comes with a set of challenges, including balancing feature accessibility with the need to incentivize paid conversions and tailoring the trial to diverse user needs and expectations. This article extensively examines the optimization of trial experiences within cloud platforms, encompassing hurdles, strategic approaches, and their profound influence on user engagement and conversion rates. It highlights the delicate art of designing trial experiences that are sufficiently feature-rich to demonstrate value yet limited enough to encourage upgrade to paid versions. The article also discusses how personalization and customer feedback can be leveraged to enhance trial experiences. By analyzing key industry players like Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP), the article sheds light on how these frontrunners utilize trial experiences to captivate audiences and fortify their customer base. It explores their distinct approaches in offering trials, the impact on market positioning, and how they balance the need for security and compliance with user accessibility. The insights from these industry giants provide valuable lessons for other players in the cloud computing sphere looking to harness the power of trial experiences for customer acquisition and retention.