Cloud Computing for Big Data Analytics: Scalable Solutions for Data-Intensive Applications

<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

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 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.

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.

This research aims at examining the strategies of performance tuning in cloud computing with emphasis on the optimization of applications, minimized response time, and optimal, affordable resource utilization. The research therefore includes a systematic literature review together with quantitative findings through empirical testing of the proposed model on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), in addition to qualitative insights of experts. Auto-scaling, load balancing caching, database optimizing, integration with edge computing and predictive workloads using Artificial Intelligence are the aspects that are also studied as key performance tuning latter. Soon, the investigation, which was made based on the results from applying the four techniques on different applications and two clouds, demonstrates the strength of each technique in achieving different goals. While auto-scale and load balance feature is very helpful in control of workload fluctuations, the caching and database optimization helps in the efficient retrieval of the data. Edge computing reduces latency in response to real-time applications, and the application of artificial intelligence in workload forecast smoothes resource utilization in environments with a rapidly changing workload. Accordingly, the research has shown need for careful choosing of suitable performance-oriented interventions to enhance the application’s interactions, decrease CPU utilization, and cut costs in a cloud environment. Lastly, this work offers practical knowledge about the methods of cloud performance tuning to support better application deployment in the cloud environments.

Big data systems handle vast volumes of structured and unstructured information using distributed computing frameworks like Apache Hadoop, Spark, and Flink. These platforms rely heavily on computational resources, and CPU utilization becomes a critical factor in determining the performance and efficiency of data processing operations. Effective CPU usage directly impacts throughput, latency, and the ability to meet service-level objectives. In large-scale clusters, workloads vary from CPU-intensive tasks such as real-time analytics, data mining, and machine learning to more I/O-driven operations like data ingestion or storage management. Poor CPU distribution can lead to overloaded nodes while others remain underutilized, ultimately reducing system efficiency. Traditional scheduling mechanisms in big data platforms are often rule-based and may not respond well to dynamic or unpredictable workloads, which contributes to suboptimal CPU usage. When CPU utilization remains consistently high, it can trigger job slowdowns, task failures, and increased power consumption, negatively affecting overall performance. Conversely, low CPU utilization indicates underused resources, leading to waste in computational capacity and energy. Additionally, container technologies and control groups (cgroups) support fine-tuned CPU allocation and isolation, ensuring fair usage across concurrent users or tasks in multi-tenant environments. In more advanced implementations, machine learning algorithms are used to forecast CPU needs and schedule jobs more efficiently by learning from historical patterns and usage behavior. Reinforcement learning approaches have also shown potential in achieving balanced CPU usage by adapting policies based on feedback and environment changes. These intelligent mechanisms allow big data systems to optimize CPU allocation continuously, adapting to fluctuating demand without manual intervention. Efficient CPU management contributes significantly to faster job execution, reduced operational costs, and higher cluster reliability. As data volumes and complexity continue to grow, maintaining optimal CPU utilization remains a priority for organizations aiming to derive timely insights while maximizing infrastructure value. Basic CPU utilization approaches are exhibiting performance limitations. This paper addresses performance limitations related to CPU utilization.

Cloud computing is a transformational force in the dynamic world of contemporary technology, altering how people and organizations can harness the potential of digital innovation. Cloud Service Providers (CSPs), who provide a wide range of resources and services and have shaped the digital era, are at the center of this paradigm change. The crucial part CSPs play in the constantly changing cloud ecosystem is highlighted in this chapter. As the cloud revolution gathers steam, CSPs act as catalysts for digital transformation, promoting quick growth and innovation. Different cloud service delivery methods, including IaaS, PaaS, and SaaS, give businesses the flexibility to customize their solutions to meet their particular requirements. We also explore the impact of significant CSPs, like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform, showing how they drive digital innovation and have an impact across numerous industries. Understanding the changing cloud landscape, where CSPs are important orchestrators of the digital symphony, begins with this chapter.

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.

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.

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.

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.

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.