Swarm: A federated cloud framework for large-scale variant analysis.

Genomic data analysis across multiple cloud platforms is an ongoing challenge, especially when large amounts of data are involved. Here, we present Swarm, a framework for federated computation that promotes minimal data motion and facilitates crosstalk between genomic datasets stored on various cloud platforms. We demonstrate its utility via common inquiries of genomic variants across BigQuery in the Google Cloud Platform (GCP), Athena in the Amazon Web Services (AWS), Apache Presto and MySQL. Compared to single-cloud platforms, the Swarm framework significantly reduced computational costs, run-time delays and risks of security breach and privacy violation.

<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.

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.

As part of NOAA's Big Data Project, near real time GOES-16 and GOES-17 Advanced Baseline Imager (ABI) Level 1b (L1b) and Level 2 (L2), Geostationary Lightning Mapper (GLM) L2, and Solar Ultraviolet Imager (SUVI) L1b data are being provided through cloud-computing platforms such as Google Cloud Platform and Amazon Web Services. This partnership allows data users to access and analyze large amounts of GOES-R Series data without needing to download the data files or use much of their personal computing resources for analysis. Another benefit of cloud data access is the ability to create value-added products and tools from existing GOES-R Series data for downstream users who are more interested in having easily accessible end products for decision making rather than in performing research analyses. This paper will first describe how a user can access GOES-R Series data from a cloud platform service, and will then illustrate the application of a value-added tool using those data.

Pathogen genomic data analysis can be extremely bespoke and diverse. This paper presents our plan and progress towards creating a Scalable Pathogen Pipeline Platform (SP 3 ) providing an efficient and unified process of collecting, analysing and comparing genomic data analysis with the benefit of elastic cloud computing. SP 3 enables container-centric bioinformatic workflows run on personal computers, High-performance computing (HPC) clusters and cloud platforms. We have deployed and tested SP 3 on local HPC, Google Cloud Platform (GCP), Microsoft Azure and OpenStack Platforms. SP 3 allows users to fetch genomic sequencing data from European Nucleotide Archive (ENA) and conduct analysis with open-source bioinformatic pipelines. We believe SP 3 will promote common standards around pathogen genomic data quality, data processing and data analysis, helping answer the challenges of tools divergence and leveraging a pool of public genomic data repository and cloud resources.

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.

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

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.

Hybrid cloud platforms have been adopted to facilitate different parts of services to deliver functionalities to service consumers. Each cloud platform offers elastic resource allocation, which accommodates fluctuating demands on services by automating the provision/deprovision of resources, referred as auto-scaling. In term of service deployment, auto-scaling is usually not interoperable between multiple cloud platforms. As a result, the service level auto-scaling strategy needs to be configured separately on disparate cloud platforms, which incurs difficulties in tracing the configuration and maintaining consistent deployment. This paper presents a model-driven method to connect a cloud platform independent model of services with cloud specific operations. Through the automated transformation from model to the configuration, we use cloud management tools to deliver auto-scaling deployment across clouds. We demonstrate our method with scaling configuration and deployment of an open source benchmark application - Dell DVD store on two cloud platforms, AWS and Rackspace. The experiment demonstrates our proposed method resolves the vendor lock issues by a model-to-configuration-to-deployment automation. The empirical measurement shows our method reduces the effort of deploying auto-scaling services on cloud platforms.

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.

Rapid expansion in cloud event technologies such as Amazon Web Service's Lambda, IBM Bluemix's OpenWhisk, Google Cloud Platform's Cloud Functions, and Microsoft Azure's Functions motivates study of software development in these services and their potential as a disruptive force in commercial cloud technologies. In addition to discussing the current state of cloud event services, this paper presents two real world applications utilizing these platforms: Lambdefy, a library designed to make traditional web application run effectively in AWS Lambda, and a performant media management service designed by Trek10, capable of resizing thousands of images per second. Furthermore, we discuss how cloud event technologies enable and/or limit these applications, motivate new software design paradigms in a cloud event environment, and highlight compelling use case scenarios and barriers to entry for cloud event services. AWS cloud technologies are exclusively used due to their maturity and the recent release of the other platforms, while Node.js and the Serverless Framework are utilized for deployment and application development.

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.

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.

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.