Advanced Security Concepts and the Shared Responsibility Model

Serverless computing has fundamentally transformed cloud technology by enabling developers to deploy applications without the burden of managing server infrastructure. This paper delves into the serverless offerings from major cloud providers—AWS (Amazon Web Services), Azure (Microsoft Azure), and GCP (Google Cloud Platform)—and explores how each contributes to this paradigm shift. The discussion highlights the core advantages of serverless computing, including cost efficiency through a pay-as-you-go model, automatic scaling that adjusts to changing demands, and reduced operational complexity, which allows developers to focus more on coding and less on infrastructure concerns. However, serverless computing is not without its challenges. Notable issues include "cold start" delays, where initial function executions may experience latency, impacting overall performance. Additionally, the shared nature of serverless environments raises potential security concerns, and the limited visibility into the execution environment can make debugging and optimization more complex. The paper also considers future trends in serverless computing across AWS, Azure, and GCP. It discusses emerging approaches such as combining serverless with traditional computing models to create hybrid architectures that leverage the strengths of both. Furthermore, it examines ongoing advancements aimed at improving support for complex, stateful applications, seeking to address current limitations and enhance the capabilities of serverless platforms. This exploration provides a detailed look at the current state of serverless computing and anticipates future developments, focusing on how AWS, Azure, and GCP are shaping the evolution of this technology

This chapter focuses on designing and building scalable and resilient architectures on Google Cloud Platform (GCP). The chapter begins by introducing the principles of scalable and resilient design, including fault tolerance, elasticity, and scalability. You will learn about the key components of scalable and resilient architectures on GCP, including load balancing, autoscaling, and distributed databases. The chapter also covers best practices for building scalable and resilient architectures on GCP, including using managed services, designing for failure, and monitoring and alerting. Overall, this chapter provides a comprehensive guide to building scalable and resilient architectures on GCP, with practical examples and real-world use cases.

Cloud computing is growing wide and first-rate promising technology. There are three forms of cloud computing: infrastructure as a service (IaaS), software as a service (SaaS), and platform as a service (PaaS). There are many cloud service providers. Among these, AWS (Amazon Web Service), Azure (Microsoft Cloud), IBM Cloud, Oracle Cloud, and VMware are immensely popular. In this paper, the authors study the Google Cloud Platform (GCP) and a few principally used popular services. Google Cloud Platform could be a cloud provider; they supply servers and services that are used on-demand and at scale. Google Cloud Platform is the fastest growing cloud service supplier now.

A comparison of using proprietary cloud platform Google Cloud Functions and open source platform Apache OpenWhisk for writing serverless applications based on the Google Cloud Platform was made. The following criteria were chosen for comparison: supported programming languages, convenience and speed of development and deployment of functions, possible ways to pass parameters to functions, options for monitoring the status of functions, speed of the cold start. As a result, main advantages and disadvantages of each platform were identified. Recommendations for use cases are given based on the features of the platforms. The results of the study can be used for further studies in the field of FaaS technologies, as there are a lot of solutions in this area that also need to be compared.

<title>Abstract</title> Serverless computing reduces management overhead and scales automatically, yet its latency and performance differ significantly across cloud platforms. This study evaluates serverless function execution and infrastructure-level characteristics on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). We benchmark function invocation latency in edge deployments, conduct regional latency comparisons across AWS and Azure, and use PerfKit Benchmarker to measure network throughput, network latency, and storage I/O on AWS and GCP. Results show that Azure recorded the lowest minimum latency (555 ms) but suffered from high tail delays, while GCP delivered the most consistent average performance (1.14 s). AWS exhibited moderate latency with a relatively stable distribution. At the infrastructure level, GCP nearly doubled single-stream throughput (9.35 Gb/s) compared to AWS (4.97 Gb/s), whereas AWS achieved slightly lower round-trip network latency (60 vs. 67 $\mu$s). Storage I/O performance was nearly identical across both providers. These findings link infrastructure characteristics to observed serverless behavior, providing actionable insights for latency-sensitive and multi-cloud application deployments.

Transparent use of commercial cloud resources for scientific experiments is a hard problem. In this article, we describe the first steps of the Data Ocean R&amp;D collaboration between the high-energy physics experiment ATLAS together with Google Cloud Platform, to allow seamless use of Google Compute Engine and Google Cloud Storage for physics analysis. We start by describing the three preliminary use cases that were identified at the beginning of the project. The following sections then detail the work done in the data management system Rucio and the workflow management systems PanDA and Harvester to interface Google Cloud Platform with the ATLAS distributed computing environment, and show the results of the integration tests. Afterwards, we describe the setup and results from a full ATLAS user analysis that was executed natively on Google Cloud Platform, and give estimates on projected costs. We close with a summary and and outlook on future work.

As software engineers, it is not an easy task to build a software solution that customers consider easy to use, cost-benefit balanced, secured, high available and prepared for high demand. There are many solutions for building a monitoring platform with these previous attributes and we need to consider all the technical factors to achieve this goal. We proposed an agile methodology for the project management approach, acquired the necessary equipment and services and hired the staff to design and develop a monitoring platform in Google Cloud Platform (GCP) with edge frameworks and technologies. We implemented a software working solution composed of five applications (app) or services and configured services in the GCP: IoT data web service, frontend web app, backend web app, IoT app, and mobile app. They were developed using Python, ReactJS, and Java and deployed in Google Cloud Platform for being used in personal computers, laptops, mobile devices, and IoT devices. We defined this architecture with its main basis in a cloud platform that used the publish/subscribe pattern for efficient data ingestion and reporting.

Tele-ophthalmology is gaining recognition for its role in improving eye care accessibility via cloud-based solutions. The Google Cloud Platform (GCP) Healthcare API enables secure and efficient management of medical image data such as high-resolution ophthalmic images. This study investigates cloud-based solutions' effectiveness in tele-ophthalmology, with a focus on GCP's role in data management, annotation, and integration for a novel imaging device. Leveraging the Integrating the Healthcare Enterprise (IHE) Eye Care profile, the cloud platform was utilized as a PACS and integrated with the Open Health Imaging Foundation (OHIF) Viewer for image display and annotation capabilities for ophthalmic images. The setup of a GCP DICOM storage and the OHIF Viewer facilitated remote image data analytics. Prolonged loading times and relatively large individual image file sizes indicated system challenges. Cloud platforms have the potential to ease distributed data analytics, as needed for efficient tele-ophthalmology scenarios in research and clinical practice, by providing scalable and secure image management solutions.

As enterprises increasingly migrate to cloud platforms, managing the cost-efficiency of data warehouses in environments such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure has become a critical concern. This paper proposes a conceptual model for optimizing the financial and operational management of cloud-based data warehouses. Through a synthesis of recent peer-reviewed studies, whitepapers, and real-world implementation reports from 2015 to 2024, the model integrates strategic design principles, workload optimization techniques, and governance frameworks across multi-cloud ecosystems. The proposed model emphasizes dynamic workload management, tiered storage optimization, intelligent scaling policies, and metadata-driven governance to ensure cost control without compromising performance. Key architectural components include serverless and autoscaling compute layers, storage lifecycle management, query optimization strategies, and automated performance tuning mechanisms. Particular focus is placed on the unique features and pricing models of AWS Redshift, GCP BigQuery, and Azure Synapse Analytics, detailing how organizations can exploit platform-specific capabilities to enhance cost-efficiency. Furthermore, the model incorporates modern innovations such as FinOps practices, usage-based cost allocation, predictive scaling powered by machine learning, and real-time cost observability dashboards. It also outlines potential pitfalls, such as overprovisioning, inefficient data partitioning, and underutilized reserved instances, and provides mitigation strategies to address them. By aligning technical architecture decisions with proactive financial operations, this conceptual model offers a pathway for organizations to balance performance, scalability, and budget constraints effectively. The study concludes by recommending future directions, including AI-driven autonomous warehouse management, unified billing optimization across multi-cloud deployments, and frameworks for continuous cost-performance evaluation. Mastering cost-efficient warehouse management is increasingly essential for organizations seeking to maximize the value of their data assets while maintaining fiscal responsibility in complex, distributed cloud environments.

The rapid development of a new communication technology called the Internet of Things (IoT) has caused this technology to dominate the current technology market. The easy access of IoT to various devices has led to the development of new applications that generate several data from different objects. In its implementation, IoT requires a protocol that has high communication performance, fast response and light size. MQTT is the right protocol for IoT because this protocol is designed for machine-to-machine communication and can send data quickly and with low bandwidth. MQTT consists of three main components, namely Publisher, Broker and Subscriber. Publishers are IoT devices that periodically send sensor data to data subscription applications known as Subscribers. While MQTT Broker is an entity that collects data sent by publishers and then forwards it to subscribers. In previous studies, many studies discussed the comparison of the performance of the HTTP Protocol in Cloud Services. This research discusses the performance of existing MQTT Brokers on AWS, Microsoft Azure and Google Cloud Platform (GCP) cloud services. It was found that Azure is good for using the Average Throughput Subscriber parameter at each QoS level and the Average Throughput Publisher at QoS 1 and 2. Meanwhile, GCP is good for using the Average throughput publisher at QoS level 0 and receives the most messages at QoS levels 1 and 2.

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.

Maintaining data consistency and reliability in distributed databases is a critical challenge for modern applications operating at scale. Traditional approaches often struggle to balance consistency, availability, and performance in geographically dispersed environments. This paper explores strategies for enhancing data consistency and reliability in distributed databases deployed on the Google Cloud Platform (GCP). Leveraging GCP services such as Cloud Spanner, Cloud SQL, and Cloud Storage, we examine techniques for ensuring strong consistency, managing data replication, and implementing robust failover mechanisms. Furthermore, this paper analyzes the trade-offs between different consistency models and their impact on application performance and availability. We present implementation code studies showcasing best practices for achieving high availability and data durability while minimizing latency in distributed database deployments on GCP.

Kubernetes enables deployment and orchestration of containerized applications across server infrastructures and in the cloud. Services running in separate containers are packed together in pods and are placed in Virtual Machines (VMs) or nodes. Application services must be placed in nodes in a way that minimizes the cost of operating an application. The problem often occurs when deploying applications comprising multiple communicating micro-services which may not fit all together in one node. This work suggests that an application is modeled by means of a graph and handles the problem of service placement as a graph clustering one. Service placement must minimize both, the node resources and the bandwidth consumed by the communicating nodes. Several graph clustering methods are deployed in the Kubernetes infrastructure of the Google Cloud Platform (GCP) to support service placement for two real use cases (an IoT architecture and an e-shop). The experimental results demonstrate that the costs of hosting applications on GCP can be significantly reduced compared to the costs of applications placed using the default Kubernetes method.

Classroom Assistant is a publicly distributable Slack application that intelligently answers student questions in place of an instructor. The application creates a personalized knowledge base per Slack workspace and learns based on information and responses provided by an instructor. This provides an automated solution enabling instructors to only answer a question once. Classroom Assistant utilizes Dialogflow, a Natural Language Processing (NLP) service on Google Cloud Platform (GCP), to detect question intent and store workspace-specific knowledge. Dialogflow provides a basic solution for integrating its service with Slack, however, it is not scalable nor flexible as any changes to the knowledge base have to go through the Dialogflow web interface on GCP. We address this limitation by creating a custom integration that is highly scalable and also enables the public distribution of the app. Via a single click installation, our Classroom Assistant allows instructors to deploy the chatbot app to different Slack works paces and auto-update the knowledge base via a direct answer to students' questions inside Slack.

Cloud migration projects are increasingly vital for modernizing legacy applications and enhancing business agility. This paper examines the comprehensive process of migrating an ATG-based e-commerce application to Google Cloud Platform (GCP). The migration strategy encompassed thorough planning, analysis, and implementation stages that addressed key aspects of scalability, performance optimization, and security enhancement. Initially, a detailed assessment of the existing ATG framework identified potential bottlenecks and compatibility issues with cloud environments. Subsequently, a robust migration roadmap was developed to mitigate risks and ensure a seamless transition, emphasizing data integrity and minimal service disruption. The process involved refactoring monolithic components into microservices, integrating containerization technologies, and leveraging GCP’s advanced orchestration, monitoring, and automation tools. Throughout the migration journey, several challenges emerged, including legacy code dependencies, integration complexities, and the necessity to comply with stringent industry standards. Addressing these obstacles required iterative testing, close stakeholder collaboration, and agile project management practices. Ultimately, the outcomes demonstrated improved operational efficiency, enhanced system resilience, and a significant reduction in infrastructure costs. Furthermore, the transition enabled the e-commerce platform to capitalize on cloud-native features such as dynamic scaling