AWS Cloud Research Articles

Innovative Cloud Architectures: Revolutionizing Enterprise Operations Through AI Integration

This article examines the transformative impact of artificial intelligence (AI) and machine learning (ML) integration in cloud computing architectures across enterprise operations. Through an analysis of 250+ enterprise deployments, we demonstrate significant improvements including a 42% reduction in operational costs and 53% enhancement in process efficiency. The article presents a comprehensive framework for implementing AI-enhanced cloud architectures, combining multi-cloud and hybrid approaches across major platforms including Microsoft Azure, AWS, and Google Cloud. Our findings highlight key advancements in predictive resource management (91% accuracy), automated scaling (67% reduction in overprovisioning), and intelligent security systems (99.7% threat detection accuracy). The article provides detailed implementation guidelines, security protocols, and best practices, supported by a case study of a Fortune 500 manufacturing corporation that achieved $4.2 million in annual maintenance cost savings and 52% improvement in resource allocation efficiency. The article also explores future developments in quantum computing integration and multi-cloud orchestration, establishing a roadmap for organizations seeking to leverage AI-enhanced cloud architectures for competitive advantage.

HAFS ensemble forecast in AWS cloud

HAFS ensemble forecast in AWS cloud

Accelerating the neural network controller embedded implementation on FPGA with novel dropout techniques for a solar inverter

Accelerating neural network (NN) controllers is important for improving the performance, efficiency, scalability, and reliability of real-time systems, particularly in resource-constrained embedded systems. This paper introduces a novel weight-dropout method for training neural network controllers in real-time closed-loop systems, aimed at accelerating the embedded implementation for solar inverters. The core idea is to eliminate small-magnitude weights during training, thereby reducing the number of necessary connections while ensuring the network’s convergence. To maintain convergence, only non-diagonal elements of the weight matrices were dropped. This dropout technique was integrated into the Levenberg–Marquardt and Forward Accumulation Through Time algorithms, resulting in more efficient training for trajectory tracking. We executed the proposed training algorithm with dropout on the AWS cloud, observing a performance increase of approximately four times compared to local execution. Furthermore, implementing the neural network controller on the Intel Cyclone V Field Programmable Gate Array (FPGA) demonstrates significant improvements in computational and resource efficiency due to the proposed dropout technique leading to sparse weight matrices. This optimization enhances the suitability of the neural network controller for embedded environments. In comparison to Sturtz et al. (2023), which dropped 11 weights, our approach eliminated 18 weights, significantly boosting resource efficiency. This resulted in a 16.40% reduction in Adaptive Logic Modules (ALMs), decreasing the count to 47,426.5. Combinational Look-Up Tables (LUTs) and dedicated logic registers saw reductions of 17.80% and 15.55%, respectively. However, the impact on block memory bits is minimal, showing only a 1% improvement, indicating that memory resources are less affected by weight dropout. In contrast, the usage of Memory 10 Kilobits (MK10s) dropped from 97 to 87, marking a 10% improvement. We also propose an adaptive dropout technique to further improve the previous results.

Comparative Analysis of Sustainability, Carbon Footprint, and AI's Role in Reducing Emissions Across AWS, Azure, and Google Cloud

Comparative Analysis of Sustainability, Carbon Footprint, and AI's Role in Reducing Emissions Across AWS, Azure, and Google Cloud

Geospatial Analytics in AWS Cloud Using AWS SageMaker

Geospatial Analytics in AWS Cloud Using AWS SageMaker

Membrane - Safe and Performant Data Access Controls in Apache Spark in the Presence of Imperative Code

Data Governance is an increasingly critical feature of modern cloud database systems, enabling administrators to set granular access policies on their data. AWS customers want to define row or column filtering on their blob storage data and access it using popular tools such as Apache Spark. AWS EMR provides a managed and serverless solution that lets users run Spark jobs in the AWS cloud with imperative and declarative programming against their data, while securely enforcing the fine-grained access controls defined on those datasets. Spark runs its compiler and scheduler alongside the user application and embeds user-defined functions in query plans, giving a threat actor direct access to its memory space. This introduces attack vectors such as information disclosure or privilege escalation during policy enforcement, in addition to well-researched threats such as SQL side channel attacks. In this paper, we present Membrane: a novel approach to secure query plans with declarative and imperative code. The innovation comes from splitting the Spark driver in two in order to rewrite query plans with security boundaries while avoiding traditional tradeoffs when using container isolation techniques. The approach described herein enables applying fine grained data access controls to both SQL and map-reduce Spark jobs, with negligible performance and cost differences.

Privacy-preserving edge federated learning for intelligent mobile-health systems

Machine Learning (ML) algorithms are generally designed for scenarios in which all data is stored in one data center, where the training is performed. However, in many applications, e.g., in the healthcare domain, the training data is distributed among several entities, e.g., different hospitals or patients’ mobile devices/sensors. At the same time, transferring the data to a central location for learning is certainly not an option, due to privacy concerns and legal issues, and in certain cases, because of the communication and computation overheads. Federated Learning (FL) is the state-of-the-art collaborative ML approach for training an ML model across multiple parties holding local data samples, without sharing them. However, enabling learning from distributed data over such edge Internet of Things (IoT) systems (e.g., mobile-health and wearable technologies, involving sensitive personal/medical data) in a privacy-preserving fashion presents a major challenge mainly due to their stringent resource constraints, i.e., limited computing capacity, communication bandwidth, memory storage, and battery lifetime. In this paper, we propose a privacy-preserving edge FL framework for resource-constrained mobile-health and wearable technologies over the IoT infrastructure. We evaluate our proposed framework extensively and provide the implementation of our technique on Amazon’s AWS cloud platform based on the seizure detection application in epilepsy monitoring using wearable technologies.

Retraction Note: securing medical data by role-based user policy with partially homomorphic encryption in AWS cloud

Retraction Note: securing medical data by role-based user policy with partially homomorphic encryption in AWS cloud

Advancing Privacy and Confidentiality in the Digital Healthcare Era: The Image Encryption Solution

The rapid digitization of health care services has spurred exponential growth in medical image generation and exchange. Safe guarding the confidentiality and integrity of these sensitive E-Health care images is crucial for protecting patient privacy. This project proposes a robust solution within the AWS Cloud, implementing an efficient image encryption model. Advanced cryptographic techniques ensure that only authorized personnel can access and decrypt images, minimizing the risk of unauthorized data breaches. The project aims to advance secure E-Healthcare image sharing, facilitating improved collaboration among providers while prioritizing patient privacy and data integrity.

NextSTAT: A Low-Cost Lot Based Paging Solution to Request Urgent Help in the or Using Amazon AWS Cloud

NextSTAT: A Low-Cost Lot Based Paging Solution to Request Urgent Help in the or Using Amazon AWS Cloud

Multi-modal Emotion Recognition Using Neural Networks: Deploy on Cloud

This paper explores recent developments in emotion recognition, focusing on methodologies employing Multilayer Perceptron (MLP) classifiers. Emotion recognition is crucial for human-machine interaction across various domains, including healthcare, surveillance systems, and intelligent learning environments. There are many deep learning models that have been employed for emotion detection, this emphasizes the effectiveness of MLP classifiers. The project at hand centers on predicting emotions such as happiness, calmness, fear, and disgust solely utilizing MLP classifiers. The system is trained on audio data, with recordings matched in frequency, time, and speed to the raw data used for training. Additionally, Convolutional Neural Network (CNN) models are used here to analyze facial expressions in real-time video streams by providing neat and complete approach to emotion recognition. Upon deployment on the AWS cloud platform, the system provides real-time emotion predictions in textual format via a web interface created using Flask. Furthermore, the system offers the capability to analyze emotions from live video streams, presenting continuous feedback on emotions through a face-outlined box updated every three seconds. This functionality serves to confirm individuals' emotions and provide self-awareness regarding their emotional states. This project focuses on giving efficiency by using MLP classifiers in conjunction with CNN models for emotion recognition tasks, thereby contributing to the advancement of human-machine interaction. Key Words: MLP Classifier, CNN, AWS Cloud, Flask, emotion, audio, video

FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps

This article presents the design, implementation, and evaluation of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FLSys</b> , a mobile-cloud federated learning (FL) system, which can be a key component for an open ecosystem of FL models and apps. FLSys is designed to work on smart phones with mobile sensing data. It balances model performance with resource consumption, tolerates communication failures, and achieves scalability. In FLSys, different DL models with different FL aggregation methods can be trained and accessed concurrently by different apps. Furthermore, FLSys provides advanced privacy preserving mechanisms and a common API for third-party app developers to access FL models. FLSys adopts a modular design and is implemented in Android and AWS cloud. We co-designed FLSys with a human activity recognition (HAR) model. HAR sensing data was collected in the wild from 100+ college students during a 4-month period. We implemented HAR-Wild, a CNN model tailored to mobile devices, with a data augmentation mechanism to mitigate the problem of non-Independent and Identically Distributed data. A sentiment analysis model is also used to demonstrate that FLSys effectively supports concurrent models. This article reports our experience and lessons learned from conducting extensive experiments using simulations, Android/Linux emulations, and Android phones that demonstrate FLSys achieves good model utility and practical system performance.

Revitalizing Legacy Systems: Extracting Key Features for Software Transplantation

The creation of intelligent software depends on the ability to transfer software without any restrictions. In this article, a crucial stage in software engineering, the feature extraction for effective software transplantation, is discussed. As hardware, operating systems, or other factors change, it is commonly necessary to move software from one environment to another. It is vital to identify and extract the relevant software characteristics, which might be challenging given how complex software is, in order to carry out efficient software transplantation. On the other hand, the procedure to extract these attributes from the software might be time-consuming and need extensive understanding. To address this, we propose a transplantation strategy that prioritizes automation with the help of AWS. Our approach involves an agent running on the application server (on-premises). It performs the task of feature identification, extraction and deployment on AWS Cloud. Currently, our strategy is confined to Java and .NET applications.

Enhancing IoT Connectivity and Services for Worldwide Ships through Multi-Region Fog Cloud Architecture Platforms

As technologies such as eco-friendly ships, electric propulsion vessels, and multi-fuel propulsion systems advance, the scope of IoT applications in maritime fields is expanding, resulting in increased complexity in control factors. The gradual progression towards Maritime Autonomous Surface Ships (MASS) is further driving the evolution of ship-based IoT applications. These advancements underscore the necessity for a platform capable of ensuring reliable connectivity between ships and onshore. The limitations of the existing single cloud architecture become evident in this context. In response to these emerging challenges, this paper presents a cloud-based data platform structure anchored in the architecture of a multi-region fog cloud. Concurrently, we propose a strategic approach aimed at enhancing collision avoidance performance. This is achieved through the seamless sharing of navigation plan data among ships facilitated by the proposed platform structure. In regions densely populated with ships, there looms a potential for packet loss as data traffic sharing intensifies through the platform. To address this concern, we devised a traffic model based on the AIS data generation cycle and proposed an algorithm for subscription decision. Subsequently, we conducted comparative analyses of packet loss probabilities between the single cloud structure and the multi-region fog cloud structure. This was achieved through experimental packet loss data collected via the AWS cloud. Simulation results underscored a notable difference: with 100 subscribed ships, the packet loss probability in regions assuming a single cloud was about 28 times higher compared to the same region within the multi-region fog cloud structure. These simulations affirm the stable and effective implementation of the proposed collision avoidance performance enhancement method within the multi-region fog cloud structure. Furthermore, feasibility was corroborated through the successful implementation of the proposed platform via the AWS Cloud.

Securing a SaaS application on AWS Cloud

Securing a SaaS application on AWS Cloud

Blockchain-Based Smart Farm Security Framework for the Internet of Things.

Smart farming, as a branch of the Internet of Things (IoT), combines the recognition of agricultural economic competencies and the progress of data and information collected from connected devices with statistical analysis to characterize the essentials of the assimilated information, allowing farmers to make intelligent conclusions that will maximize the harvest benefit. However, the integration of advanced technologies requires the adoption of high-tech security approaches. In this paper, we present a framework that promises to enhance the security and privacy of smart farms by leveraging the decentralized nature of blockchain technology. The framework stores and manages data acquired from IoT devices installed in smart farms using a distributed ledger architecture, which provides secure and tamper-proof data storage and ensures the integrity and validity of the data. The study uses the AWS cloud, ESP32, the smart farm security monitoring framework, and the Ethereum Rinkeby smart contract mechanism, which enables the automated execution of pre-defined rules and regulations. As a result of a proof-of-concept implementation, the system can detect and respond to security threats in real time, and the results illustrate its usefulness in improving the security of smart farms. The number of accepted blockchain transactions on smart farming requests fell from 189,000 to 109,450 after carrying out the first three tests while the next three testing phases showed a rise in the number of blockchain transactions accepted on smart farming requests from 176,000 to 290,786. We further observed that the lesser the time taken to induce the device alarm, the higher the number of blockchain transactions accepted on smart farming requests, which demonstrates the efficacy of blockchain-based poisoning attack mitigation in smart farming.

Alexa Enabled IoT Device Simulation Using C# And AWS Lambda

Purpose: Nowadays, device operation using voice commands is becoming popular. Several voice command services are available. Among them, Alexa from Amazon is the most popular. Almost Every day, vendors are integrating Alexa into their new products worldwide. We need physical devices for the device operation or understanding of the process flow of Alexa. Sometimes, physical devices are costly, or availability is poor. Here, we demonstrated how to create a simulated Alexa-enabled IoT device. We used several Amazon cloud services to execute the process flow. Alexa's skill is developed inside the Alexa developer console. To process the command, we use the AWS Lambda function. AWS IoT cloud service is used to trigger IoT devices over MQTT. For simulated devices, we are using a C# MQTT client. The researcher trying to simulate an Alexa-enabled device into their project can get some reference information from this work. Design/Methodology/Approach: We create a graphical user interface (GUI) to interact with the user or display the device's status. The GUI is connected with the C# AWS IoT Device Shadow client. We created IoT Things in the AWS cloud. Under the IoT Shadow, we created a Device shadow. The Alexa service triggers the lambda function. The Lambda function updates the Shadow register, which resides inside the cloud. The c# shadow client receives a notification when the device Shadow updates and updates the GUI element that represents the equipment. We can use any Alexa device to send voice commands, like the Alexa mobile app, Echo Dot, or Alexa PC app. Findings/Result: Through this research work, we created and tested virtual devices that can be experiments or research work using Alexa voice commands. It has been tested for a long time. It performed well, and no issue was found. Using more code-level protection it can be robust for practical implementation. Originality/Value: We found several fragmented documents over the web to implement Alexa-enabled virtual devices. After lots of study, we did practical and included the procedure in this research work. From voice input to load trigger, there are lots of steps involved. The complete guidance is not available easily. So through this research work, if someone follows, they can easily create a voice-operated device. This research work may add value to the researcher experimenting with the Alexa command-activated device. Paper Type: Experimental-based Research.

Hardware VM Isolation in the Cloud

Confidential computing is a security model that fits well with the public cloud. It enables customers to rent VMs while enjoying hardware-based isolation that ensures that a cloud provider cannot purposefully or accidentally see or corrupt their data. SEV-SNP was the first commercially available x86 technology to offer VM isolation for the cloud and is deployed in Microsoft Azure, AWS, and Google Cloud. As confidential computing technologies such as SEV-SNP develop, confidential computing is likely to simply become the default trust model for the cloud.

English

English

Let Us Create A Lambda Function for Our IoT Device In The AWS Cloud Using C#

Purpose: This research paper explores the process of creating a Lambda function for an IoT device in the AWS cloud using C#. It provides a comprehensive guide to the steps involved in building a Lambda function, including setting up the AWS environment, creating the Function using C#, and deploying it to the cloud. Additionally, the paper will discuss the benefits of using Lambda functions for IoT devices, such as cost-effectiveness and scalability. It will provide examples of real-world use cases for this technology. By the end of this paper, readers will clearly understand how to create Lambda functions for their IoT devices using C# in the AWS cloud and will be able to apply this knowledge to their projects. Design/Methodology/Approach: A comprehensive literature review was conducted to gather information on the AWS cloud, IoT devices, and Lambda functions. The Lambda function was created using C# in Visual Studio. This involved writing the necessary code to perform the desired functionality, testing the Function locally, and packaging it for deployment. The Lambda function deployed to the AWS cloud using the AWS Management Console. Findings/Result: The AWS environment can be easily configured with IoT devices and Lambda functions. AWS allows for cost-effective and scalable solutions that can be easily managed and monitored. Creating a Lambda function using C# in Visual Studio is straightforward and allows for powerful and flexible coding techniques. The deployment of the Lambda function to the AWS cloud is simple and can be achieved using the AWS Management Console. Originality/Value: It offers a comprehensive guide to creating a Lambda function for an IoT device in the AWS cloud using C#. This guide includes a step-by-step approach to setting up the AWS environment, creating the Function using C#, and deploying it to the cloud. It demonstrates the real-world application of Lambda functions for IoT devices, providing examples of use cases and demonstrating the benefits of this technology. Paper Type: Experimental-based Research.