AWS Cloud Computing Solutions: Optimizing Implementation for Businesses

Coal seam gas (CSG) is a naturally occurring methane gas found in most coal seams and is similar to conventional natural gas. CSG is a major source of energy, accounting for about 15% of Australia's electricity generation. Also, CSG is an integral part of the gas industry in eastern Australia, particularly in Queensland. Origin Energy is the upstream operator for a leading joint venture in Australia and is responsible for the development of its CSG fields in the Surat and Bowen basins as well as the main transmission pipeline that transports the gas to the LNG facility on Curtis Island near Gladstone, Queensland. Origin Energy has been exploring ways to leverage modern technology solutions to increase field staff productivity, reduce operating costs per well, and improve asset reliability while also bringing more wells into production to meet growing customer demands. Field operators drive up to 100km per day and may have up to 20 well visits per day as part of a routine inspection that involves visual checks of the site to gain situational awareness. In recent years, with advancements in technologies such as edge compute (IoT), modern network connectivity options (satellite, narrow-band LTE) and Machine learning (no-code/low-code solutions) leveraging Cloud infrastructure early experiments are suggesting the implementation of safe, reliable, low-cost remote monitoring capabilities at scale is a possibility. This paper discusses how Amazon Web Services (AWS) collaborated with Origin Energy to deploy a trial focusing on remote monitoring of well pads using a camera solution. This trial captures images from the well pad at regular intervals, which when scaled out will help reduce the need for routine inspection of well pads by field operator at the current interval. This trial has also developed a data repository of all the images in the AWS Cloud and is designing a data processing pipeline to label images and use low-code/no-code ML to detect visual anomalies. In addition to the Origin Energy use cases (described in use case section 2.1 and 2.2) AWS has also helped an operator in North America (use case section 2.3) with building Machine learning models using Amazon SageMaker for anomaly detection. Hence, based on these projects it has become evident that using IoT, Machine Learning, and Cloud computing can accelerate innovation that drives down cost and increase operational safety of managing Oil & Gas assets remotely.

The rapid evolution of Amazon Web Services (AWS) cloud technologies continues to reshape enterprise computing environments across global industries. This technical review examines recent innovations in AWS services that are transforming organizational capabilities and competitive positioning. Beginning with computational performance advancements through AWS Graviton processors and specialized High-Performance Computing offerings, the article explores how these ARM-based architectures deliver enhanced efficiency across diverse workloads. The expanding AI and Machine Learning ecosystem, particularly through AWS Bedrock's foundation model integration and SageMaker's democratized ML tools, enables organizations to implement sophisticated intelligence capabilities without extensive expertise. Serverless computing developments, including Lambda Function URLs and SnapStart features, alongside visual workflow orchestration through Step Functions, have simplified application development while improving performance characteristics. Container orchestration enhancements facilitate consistent hybrid deployments across on-premises and cloud environments. Looking forward, AWS continues strategic investments in quantum computing initiatives, sustainability practices, and multi-cloud compatibility tools that position the platform at the forefront of cloud innovation. These advancements collectively enable organizations to achieve greater operational agility, cost efficiency, and innovation capacity while addressing evolving challenges in security, compliance, and technical workforce development.

The rapid evolution of cloud computing has opened new horizons for businesses of all sizes, with Amazon Web Services (AWS) leading the charge as a premier cloud service provider. This study delves into the unique challenges and opportunities faced by small and medium-sized enterprises (SMEs) in maximizing the benefits of AWS to enhance operational efficiency and stimulate innovation. Through a comprehensive analysis of AWS's vast array of services, the research identifies key strategies and best practices tailored specifically for SMEs. These strategies encompass optimizing resource allocation, ensuring cost-effective scalability, and leveraging advanced AWS features to foster a culture of innovation. Additionally, the study provides practical insights into overcoming common obstacles SMEs encounter during cloud adoption and integration. By presenting detailed case studies and expert recommendations, this research aims to serve as an invaluable guide for SMEs seeking to harness the full potential of AWS, ultimately driving growth and competitiveness in an increasingly digital landscape.

Enterprise IT infrastructures experience a transformation because of rapid cloud computing adoption, especially Amazon Web Services (AWS), which delivers scalability, flexibility, and cost efficiency. AWS users encounter difficulties optimizing their performance because they face problematic situations combining workload variability with complex resource distribution systems and delay issues. The implementation of Artificial Intelligence constructs an evaluation process to boost performance optimization in Amazon Web Services (AWS) cloud systems. AI-powered insights implemented through machine learning algorithms with predictive analytics capabilities and automation functionality improve workload distribution and enhance auto-scaling efficiency while reducing operational expenses. Processor analysis of historical data combined with real-time metrics allows AI algorithms to make proactive decisions about resource administration, detect anomalies, and perform innovative management. The research evaluates performance-altering AI-based approaches that enhance AWS services, including EC2, Lambda, and Sage Maker. Implementing AI-based optimizations generates copious improvements in computational efficiency and cost performance based on experimental data. Thanks to AI, cloud performance management strategies will fundamentally transform since they endorse efficient AWS environments that align with business requirements.

Abstract—In this article I have deployed a web-based applica- tion to Docker using Amazon web service (EC2 Instance). Elastic Cloud Compute service is used to the run the docker applications on scalable clusters. Amazon EC2 is fully managed and secured Container orchestration service that makes to deploy, manage and scale Dockerized web application. Docker containers on an EC2 instance to run a university management system website on the Amazon Web Services (AWS) cloud. For educational institutions, a university management system is a crucial tool that enables administrators to manage student records, course information, and other crucial data. The introduction to Docker, a containerization technology that makes it simple to build and manage web applications, follows a discussion of the advantages of hosting a university management system website on the cloud. The next section of the paper focuses on the AWS EC2 service and its capabilities, such as its capability for flexible computing and scalability. The paper also offers a step-by-step tutorial on how to use Docker to deploy a university administration system website on an EC2 instance, including instructions on how to set up the EC2 instance, install and configure Docker, and deploy the website code in a Docker container. Elastic Load Balancer and Auto Scaling, two AWS services, are used in the process of setting up security and scalability for the website. The benefits of using Docker to host a university administration system website on AWS EC2 are outlined in the paper’s conclusion, along with predictions for the development of cloud-based web application hosting. This research paper offers a thorough overview of the steps involved in hosting a university management system website using Docker on AWS EC2, making it a useful tool for educational institutions looking to improve their information management procedures and take advantage of cloud computing and Docker- Containerization.

Cloud computing is increasingly becoming a norm in the global industrial landscape with web services using it as a critical backbone service provider including significant video streaming services. The purpose of this paper is to present an overview of some of the Amazon Web Service (AWS) tools and features. The paper also discusses how AWS contributes to a range of industries making them dependent on AWS’s agility, scalability and deliverability of services. Implementation using information available on AWS leading portals as well as the practical experiments involving the discussed features are given. As more and more industries become dependent on Big data and AI, one can state that AWS will play a key role in the future developments across myriads of industries.

During the last decade the emergence of Internet of Things (IoT) based applications inspired the world by providing state of the art solutions to many common problems. From traffic management systems to urban cities planning and development, IoT based home monitoring systems, and many other smart applications. Regardless of these facilities, most of these IoT based solutions are data driven and results in small accuracy values for smaller datasets. In order to address this problem, this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control system in the smart cities. This hybrid model consists of; convolution neural network and binary long short term model for the object detection to ensure safety of the homes while IoT based hardware components like; Raspberry Pi, Amazon Web services cloud, and GSM modems for remotely accessing and controlling of the home appliances. An android application is developed and deployed on Amazon Web Services (AWS) cloud for the remote monitoring of home appliances. A GSM device and Message queuing telemetry transport (MQTT) are integrated for communicating with the connected IoT devices to ensure the online and offline communication. For object detection purposes a camera is connected to Raspberry Pi using the proposed hybrid neural network model. The applicability of the proposed model is tested by calculating results for the object at varying distance from the camera and for different intensity levels of the light. Besides many applications the proposed model promises for providing optimum results for the small amount of data and results in high recognition rates of 95.34% compared to the conventional recognition model (k nearest neighbours) recognition rate of 76%.

Deep Learning is a subfield of machine learning methods based on artificial neural networks. Thanks to the increased data availability and computational power, such as Graphic Process Units (GPU), training deep networks - a time-consuming process - became possible. Cloud computing is an excellent option to acquire the computational power to train these models since it provides elastic products with a pay-per-use model. Amazon Web Services (AWS), for instance, has GPU-based virtual machine instances in its catalog, which differentiates themselves by the GPU type, number of GPUs, and price per hour. The challenge consists in determining which instance is better for a specific deep learning problem. This paper presents the implications, in terms of runtime and cost, of running two different deep learning problems on AWS GPU-based instances, and it proposes a methodology, based on the previous study cases, that analyzes instances for deep learning algorithms by using the information provided by the Keras framework. Our experimental results indicate that, despite having a higher price per hour, the instances that contain the NVIDIA V100 GPUs (p3) are faster and usually less expensive to use than the instances that contain the NVIDIA K80 GPUs (p2) for the problems we analyzed. Also, the results indicate that the performance of both applications did not scale well with the number of GPUs and that increasing the batch size to improve scalability may affect the final model accuracy. Finally, the proposed methodology provides accurate cost and estimated runtime for the tested applications on different AWS instances with a small cost.

<span>Crimeware-as-a-service (CaaS) presents a growing cybersecurity threat by facilitating the acquisition of tools for website phishing attacks. Detecting these attacks requires effective techniques to obtain accurate results in real time. Cloud machine learning (CML) emerged as a promising solution with the powerful tools of amazon web services (AWS). This study proposes a novel approach combining cyber kill chain concept with AWS technologies to enhance website phishing detection, using AWS SageMaker to preprocess an 11,430 uniform resource locators (URL) dataset and train 3 algorithms, which are: decision tree (DT), random forest (RF), and support vector machine (SVM), evaluate their performance through batch transform, and deploy them as separate endpoints. Prediction functions are then conducted on each endpoint and compared to batch transform results. Our findings demonstrate that the combination of the cyber kill chain concept and AWS CML significantly enhances website phishing detection by achieving results of 97% for RF in 0.48 seconds, 94% for SVM in 0.94 seconds, and 93% for DT in 0.52 seconds. By leveraging CML algorithms and breaking down attacks into stages, our approach identifies and disrupts attacks earlier, preventing damage. This research highlights the value of our approach in improving cybersecurity and protecting against website phishing attacks.</span>

The increasing migration of enterprise operations to Amazon Web Services (AWS) has amplified the need for robust, scalable, and intelligent cybersecurity solutions. Security Information and Event Management (SIEM) systems have become vital for detecting threats, correlating logs, and maintaining compliance in cloud environments. This systematic review explores the integration of SIEM tools within AWS-based infrastructures, focusing on their effectiveness in threat detection and log correlation. It examines leading SIEM solutions such as Splunk, IBM QRadar, Sumo Logic, and AWS-native services like Amazon GuardDuty, AWS CloudTrail, and AWS Security Hub. Emphasis is placed on key integration approaches, including API-based ingestion, agentless data capture, and real-time event streaming through AWS services like Kinesis and S3. The review critically analyzes studies published between 2018 and 2023, highlighting trends in the automation of log management, enrichment of security alerts through machine learning, and orchestration via Security Orchestration, Automation, and Response (SOAR) platforms. Challenges such as data normalization, scalability limitations, cross-service visibility, and compliance adherence in multi-account AWS architectures are discussed. The findings indicate that SIEM integration enhances threat detection efficiency by enabling proactive anomaly detection, facilitating rapid incident response, and improving forensic investigation capabilities. However, the review identifies gaps, particularly in cost optimization, handling high-velocity log streams, and adapting traditional SIEM models to dynamic, serverless AWS architectures. Best practices for successful SIEM deployment include leveraging AWS-native integrations, prioritizing event prioritization algorithms, applying continuous tuning, and aligning with security frameworks like NIST and CIS AWS Foundations Benchmark. Future research directions propose the development of AI-driven adaptive SIEM systems tailored for cloud-native environments, advanced correlation engines for serverless and containerized workloads, and strategies to optimize licensing and resource utilization. This systematic review provides cybersecurity practitioners, cloud architects, and researchers with a comprehensive understanding of SIEM integration complexities and evolving practices in AWS infrastructures, ultimately contributing to improved cloud security postures and operational resilience in the face of sophisticated cyber threats.

Cloud computing has caused a revolution in how companies do business giving them flexibility, scalability, and cost-efficiency like never before. It lets organizations use computing resources through the internet instead of relying on the usual in-house setup making cloud computing a key part of today's IT plans. From new startups to big global companies, businesses are using cloud solutions more and more to boost their ability to adapt cut down on running costs, and come up with new ideas. The cloud ecosystem is big and varied, with different deployment models and service types to suit various business needs. Public, private, and hybrid clouds are the three main deployment models each with its own pros and cons. public clouds, like those from Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform, offer affordable scalability and global reach. Private clouds, on the other hand, are for organizations that have strict security or compliance needs giving them dedicated environments. Hybrid clouds mix the best parts of both letting businesses find a balance between security and scalability by combining private and public cloud resources

This article explores the transformative role of Amazon Web Services (AWS) in facilitating human-AI collaboration across diverse industry sectors. It begins by establishing AWS's robust AI infrastructure foundation, centered on services like Amazon SageMaker that democratize access to sophisticated machine learning capabilities. It investigates the profound impact of AWS-powered AI systems on decision-making processes in healthcare, where diagnostic accuracy and preventive care have been enhanced, and in education, where personalized learning pathways have revolutionized academic outcomes. The discussion extends to productivity enhancements achieved through AWS AI services including Amazon Lex, Polly, Rekognition, and Comprehend, which have streamlined workflows and reduced operational costs while improving service quality. It further explores how AWS enables unprecedented personalization in e-commerce, entertainment, marketing, and financial services through advanced data processing and AI algorithms. Detailed attention is given to specialized collaborative tools such as Amazon Augmented AI, AWS Ground Truth, and Amazon Kendra, which establish effective human-AI workflows. Industry-specific implementations in manufacturing, financial services, and customer support demonstrate the practical applications of these technologies. It concludes by addressing critical challenges in data privacy, AI transparency, workforce development, and ethical governance that organizations must navigate to fully realize the benefits of AWS-powered human-AI collaboration, while highlighting AWS's ongoing evolution to address these concerns.

This research paper evaluates the advantages of Amazon Web Services (AWS) Cloud compared to conventional on-premise answers, highlighting the important thing services that incorporate the AWS platform. Launched in March 2006, AWS has grown drastically, with 94% of enterprises and 89% of organizations having as a minimum one utility or a part of their infrastructure in the cloud as of 2023. Additionally, 20% of enterprises are expected to adopt cloud programs and systems in the next year. AWS serves over one million active enterprise customers globally, imparting a complete suite of cloud-primarily based products, consisting of computing, data storage, databases, analytics, networking, mobile, developer tools, management tools, Internet of Things (IoT), security, and applications. These offerings are available on-demand, with a pay-as-you-go pricing model, and may be deployed fast without direct capital cost. This flexibility and scalability benefit start-ups, small and medium-sized businesses, and public sector customers, allowing them to respond swiftly to dynamic business requirements

Depending on the requirements, the deployment of containerized applications for monitoring Internet of Things devices can be done in Cloud or on the Edge. This paper discusses the design of a containerized application monitoring temperature and humidity for IoT devices in three implementations: (1) balenaCloud for edge deployment; (2) Amazon Web Services cloud with EC2 instances; and (3) dedicated Amazon Web Services IoT service. The experiments showed that there are no significant performance differences between Edge and Cloud deployments regarding the resource consumption. However, for fast and almost effortless action, Amazon Web Services IoT is the go-to option when it comes to serverless applications based on Message Queuing Telemetry Transport. For all-in-one automated fleet management balenaCloud seems to be a good tool for IoT developers.

Diabetes is a widespread chronic metabolic disorder that affects a substantial portion of the global population and requires diligent management and continuous medical supervision. Traditional glucose monitoring methods often involve invasive procedures that cause discomfort and increase the risk of infection. With the rapid advancements of the Internet of Things (IoT) and cloud computing, healthcare is evolving for early disease detection and personalized treatment solutions. The aim of this research is to develop a system based on the Internet of Things (IoT) for non-invasive diabetes detection using Photoplethysmography (PPG) signals. Our system leverages machine learning models, such as XGBoost, Random Forest, Logistic Regression, and SVM, to accurately classify diabetic patients using statistical features collected from PPG signals and metadata. To ensure model robustness, we employed a fivefold cross-validation approach. The hardware technology includes a portable, cost-effective system with a simple NIR sensor and a high-performing ESP32 module that minimizes user discomfort and ensures easy integration. Deployed on the Amazon Web Services (AWS) cloud platform, our system enables real-time data collection and analysis, providing users with a convenient web application for monitoring their diabetic status. The performance of the developed system has been tested with two different datasets: the PPG-BP dataset and the Mazandaran dataset, achieving 87.88% and 90% accuracy, respectively. The results demonstrate the potential of the designed system to revolutionize diabetes management by providing a non-invasive, accessible, and accurate solution for early-stage detection.