Cloud Event Programming Paradigms: Applications and Analysis

The article investigates the impact of modern cloud technologies on the efficiency of DevOps processes, which are key to the automation, flexibility and reliability of software development. With the rapid development of information technology, cloud platforms play an important role in accelerating software product development, testing and deployment. Implementing DevOps in the cloud environment reduces the time to market, increases the stability of software systems and optimizes the use of computing resources, which is critical for modern companies. This paper discusses DevOps teams' main challenges, including the need to scale infrastructure rapidly, ensure continuous integration and delivery (CI/CD), automate testing, monitor performance, and optimize costs. Particular attention is paid to security problems, configuration management and the human factor minimization when implementing program code changes. A comparative analysis of the three leading cloud platforms - Amazon Web Services, Google Cloud Platform, and Microsoft Azure – is carried out in the context of their impact on DevOps processes. The possibilities of automation, support for container technologies, infrastructure scalability, monitoring tools, and integration with CI/CD pipelines are evaluated. AWS was found to offer the broadest set of DevOps tools with a high level of automation, making it an attractive choice for organizations focused on complex enterprise solutions. Google Cloud Platform demonstrates the best support for Kubernetes and containerization, an essential factor for teams working with microservices architecture. Microsoft Azure provides the most profound integration with the Microsoft ecosystem and is the best choice for companies that use Windows and related products. An experimental study has shown that the choice of a cloud platform significantly impacts the speed of release cycles, the stability of the deployed infrastructure, the ability to respond quickly to changes in load, and the productivity of DevOps teams in general. Recommendations for choosing the optimal cloud platform are proposed depending on the specifics of the project, the scale of the organization, the level of system load, and automation requirements.

<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.

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

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.

Infrastructures for High-Performance Computing: Cloud Infrastructures

Mainframe systems have a track record of reliability, but they need help in today’s fast-changing business world. These include high upkeep expenses and limited room for growth. This paper looks at three top cloud platforms—Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)—to update old mainframe programs. We compare them in detail, looking at what each does best. AWS has a wide range of tools to move data. Azure works well with cloud and on-site systems and fits into company setups. GCP leads in AI and machine learning. Azure is the top option for updating mainframes, offering compatibility with various cloud configurations, integration with current company infrastructure, and sophisticated data analysis driven by AI. These conclusions provide valuable information for companies transitioning mainframe systems to the cloud, enabling expansion, reduced expenses, and future-ready IT systems.

Cloud computing has revolutionized analysis of large datasets. This tutorial provides a comprehensive, practical guide for research groups seeking to leverage cloud platforms for data analysis. The tutorial covers the foundations of cloud computing, including its history, rationale, and use cases for research, followed by detailed comparisons of the 3 major platforms: Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure. Side-by-side comparisons of services, costs, ease of use, and selection guidelines assist researchers in choosing the most appropriate platform. A complete step-by-step example using hospital price transparency data demonstrates the entire workflow from account creation through results retrieval, enabling researchers to begin productive cloud-based analysis within hours.

Cloud technologies are becoming pervasive and available for private companies or public institutions in different flavors, mostly public cloud or private clouds. Our focus in this work is on the usage of public cloud technologies by the most popular sites in the Internet. While some studies have described the nascent landscape of public cloud computing 5 years ago, surprising little effort has been put to study the recent evolution of this domain.Using DNS data that enables us to map domains (e.g., netflix.com) and their subdomains (e.g., api.netflix.com) with the cloud providers actually used, we refresh our understanding of this ecosystem. We focus on the dominant four cloud providers, namely Amazon Web Services, Microsoft Azure, Google Cloud Computing and IBM Bluemix. We demonstrate that cloud penetration has clearly increased since 2013, reaching almost 50% of the top 1000 domains, from the Alexa list. Furthermore, a significant fraction of domains use multiple cloud providers simultaneously. Still, domain owenrs remain cautious when it comes to choose which subdomain is actually hosted in the cloud and only 17.8% on average of the subdomains are actually hosted in the cloud. In terms of performance, preliminary results indicate that hosting a subdomain in the cloud pays off as compared to private hosting with a decrease of application level latency of 28%.

Recently, more and more companies are using machine learning platforms offered by cloud service providers to build sentiment analysis models that can then be used to analyze public opinions via social media. This paper aims to conduct a comparative analysis of two of the most popular cloud computing platforms, namely Amazon Web Services (AWS) and Microsoft Azure, in terms of their sentiment detection services through the complex analysis of multiple texts. The comparative analysis was carried out by implementing an application that integrates both the sentiment analysis (SA) solutions provided by Amazon Web Services and those offered by Microsoft Azure. To evaluate the services offered by the two platforms, different evaluation metrics were analyzed and compared, such as accuracy, precision, recall, and other relevant characteristics. Also, the paper examines the costs and limitations of the two platforms, Amazon Comprehend and Azure AI Language Text, when they are used to implement solutions for analyzing the sentiments of product reviews. The results obtained highlighted the advantages and disadvantages between the two platforms from several perspectives, such as performance, the quality of the answers provided, or their accuracy. All these aspects help to obtain a clear picture of the advantages and limitations of each service offered by the two cloud platforms.

The explosion of data in the digital era has posed major challenges handling, computing and analyzing enormous and complex datasets. Cloud computing has arisen as a revolutionary solution providing scalable and elastic infrastructure necessary to deal with incoming big data workloads. This study employs an empirical approach to evaluate the performance, cost efficiency, and scalability of the three dominant cloud service models, Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Function-as-a-Service (FaaS) on Amazon Web Services, Microsoft Azure and Google Cloud Platform. Standard big data analytics workloads running real-time stream processing and machine learning activities were then implemented in Apache Spark, Hadoop, and Kafka on harmonized cloud environments. Key performance metrics such as execution time, CPU utilization, memory, cost per task and throughput were taken, analyzed statistically using ANOVA and Tukey’s post hoc tests. Results show that FaaS configurations are always faster in execution speed, memory efficiency and cost compared to IaaS, while IaaS delivers better CPU usage for continual workloads. AWS and GCP platform performed relatively balanced when compared to Azure. It is concluded that serverless architecture is, in fact, optimal for modular and burst-oriented analytics, and hybrid models might be more appropriate for complex pipelines. These results can offer cloud architects practical directions towards scalable and cost-effective big data solutions.

SummaryCloud‐based services have proved useful in several research fields, such as engineering, health science, and astrophysics, to mention a few examples. The computational environmental science community developed a strong need for cloud facilities to store, process, and manage data from observations and numerical models for simulations and forecasts. Weather forecast models and global sensor networks deal with multidimensional geo‐referenced data∖sets. However, environmental data consumer applications usually require a relatively small amount of multidimensional input data slice to analyze a specific area or time interval. Hence, reducing data dimension for information retrieval is mandatory. This paper presents a twofold solution: a technique to load and retrieve the sliced multidimensional data set on different cloud services such as Amazon Web Service (AWS), Google Cloud Platform, and Microsoft Azure. The experimental results performed on these cloud services highlight that the proposed method can significantly speed up the process of loading and retrieving the data slices compared to working with the entire data set in bulk or OPeNDAP server.

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

As we approach the Exascale era, it is important to verify that the existing frameworks and tools will still work at that scale. Moreover, public Cloud computing has been emerging as a viable solution for both prototyping and urgent computing. Using the elasticity of the Cloud, we have thus put in place a pre-exascale HTCondor setup for running a scientific simulation in the Cloud, with the chosen application being IceCube’s photon propagation simulation. I.e. this was not a purely demonstration run, but it was also used to produce valuable and much needed scientific results for the IceCube collaboration. In order to reach the desired scale, we aggregated GPU resources across 8 GPU models from many geographic regions across Amazon Web Services, Microsoft Azure, and the Google Cloud Platform. Using this setup, we reached a peak of over 51k GPUs corresponding to almost 380 PFLOP32s, for a total integrated compute of about 100k GPU hours. In this paper we provide the description of the setup, the problems that were discovered and overcome, as well as a short description of the actual science output of the exercise.

Scientific high throughput computing needs are growing dramatically with time and public Clouds have become an attractive option for occasional bursts, due to their ability to be provisioned with minimal advance notice. The available capacity of both compute and networking is however not well understood. This article presents the results of several production runs of the IceCube collaboration that temporarily expanded its batch system environment with GPU-providing compute instances from the three major Cloud providers, namely Amazon Web Services, Microsoft Azure, and the Google Cloud Platform. The aim of these Cloud bursts was to push the limits of Cloud compute, with a particular emphasis on GPU-providing instances. On the compute side, we showed that it is possible to reach peaks of over 380 fp32 PFLOPS using all available GPU-providing instance types and integrate over 1 fp32 EFLOP hour in a single workday by using only the most cost-effective ones. On the network side, we showed intra-Cloud network throughputs of over 1 Tbps, and 100 Gbps throughputs toward on-prem storage both using shared peering arrangements and dedicated network links.

Modern Software-as-a-Service (SaaS) platforms require a strongand reliable infrastructure that can function seamlessly across various cloud providers, without losing consistency and efficiency in operations. The multi-cloud architecture, however, brings in a lot of complexity due to the differences in provider APIs, resource models, and operational characteristics. When infrastructure management is handled manually, configuration drift becomes a significant issue. The conventional deployment methods are not efficient enough to work at a large scale across different cloud environments. The piece lays out an intricate automation framework that combines Infrastructure as Code (IaC) provisioning, multi-level validation testing, and centralized observability in a flawless manner. Using Terraform, one can carry out declarative infrastructure specifications that can be spread over Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Puppet ensures that the standard configurations are kept in the various node populations. Unit testing is employed to confirm the correctness of the individual modules before their integration. Acceptance testing simulates the end-to-end workflows in temporary environments that are exact replicas of production topologies. Deployment of catalog comparison strategies uncovers unintentional configuration changes prior to implementation. Centralized logging collects the events from distributed systems that are open for cross-cutting analysis. Monitoring dashboards draw the time-series metrics from the infrastructure components and the applications. Automated alerting pinpoints the operational issues facing the threshold conditions and anomaly detection. The combination of the framework fills in the critical gaps of the existing single-cloud automation approaches. Declarative specifications are there to remove the complexities of provider-specific. Automated validation is there to stop the propagation of faulty configurations across distributed systems. Unified observability is the consistency of visibility across fragmented multi-cloud landscapes. The outline exhibits the solutions for the realization of enterprise-scale cloud operations by the integration of strategic automation.