Public Cloud Services Research Articles

A Comprehensive Taxonomy of Modern Public Cloud Services for Infrastructure Selection

The rapid adoption of public cloud services has transformed the digital landscape, offering diverse solutions to meet different business needs. However, this diversity of services also presents a challenge: the need for a structured, comprehensive taxonomy to navigate and understand the offerings. This paper presents a comprehensive taxonomy for public cloud services, aiming to provide clarity and structure in this domain. Leveraging extensive market research, analysis of leading industry reports, and insights from top cloud vendors, we have identified key criteria that form the foundation of our taxonomy. The taxonomy passed the validation process, involving industry experts from Ukraine's largest telco operator, ensuring its practical relevance. The results have proven the taxonomy's effectiveness in classifying a wide range of cloud services, highlighting its potential as a valuable tool for businesses, researchers, and cloud practitioners. This research serves as a foundation for future improvement of complex processes of cloud provider selection.

Navigating The Cloud: Enhancing Human-Robot Collaboration In Industry 4.0 Through Digital Twins And Cloud-Based Systems

Industry 4.0 Is Geared Towards Establishing Sophisticated Industrial Ecosystems That Necessitate Secure And Efficient Collaboration Between Humans And Robots, Termed Human-Robot Collaboration (HRC). Within This Framework, HRC Leverages Cutting-Edge Collision Avoidance Technology Capable Of Detecting Objects And Foreseeing Possible Collisions, Thereby Calculating Alternative Pathways To Avert Any Direct Contact. Furthermore, The Concept Of Digital Twins Emerges As An Innovative Solution, Offering A Platform To Simultaneously Evaluate The Consequences Of Various Control Strategies Within A Digitally Simulated Environment. While Cloud Computing Infrastructure Can Furnish The Requisite Computational Prowess, It May Simultaneously Introduce Challenges Such As Increased Latency And Variability In Communication, Known As Jitter, Which Could Potentially Hinder System Performance. Cloud Technologies Are Renowned For Their Inventive Approaches To Ease The Workload For Software Developers And System Operators, Raising Questions About Their Integration With Digital Twin Methodologies And The Resilience Of Robotic Systems Against The Potential Delays Incurred From Cloud-Based Services. This Research Endeavors To Navigate These Complexities By Integrating A Blend Of Both Public And Private Cloud Services, Which Are Distinguished By Their Unique Parallel Processing Capabilities. This Study's Contributions Are Threefold. Firstly, It Introduces A Novel Method Designed To Gauge The Efficacy Of Diverse Strategies By Focusing On Their Associated Latency Impacts. Secondly, It Delineates A Tangible Application Of HRC, Illuminating Its Practical Benefits And Real-World Applicability. Lastly, It Defines A Crucial Performance Metric Intended To Assess The Efficiency Of These Systems Thoroughly. By Delving Into These Aspects, The Research Aims To Conduct A Comprehensive Analysis Of The Strengths And Weaknesses Inherent In Various Technological Methodologies And Their Consequential Effects On The Performance Of HRC Systems Within The Ambit Of Industry 4.0. Through This Detailed Examination, The Study Seeks To Provide Valuable Insights Into Optimizing Human-Robot Collaboration In Industrial Settings, Ensuring Both Security And Efficiency Are Upheld

Synergies and Challenges in the Integration of Cloud Computing and Deep Learning: Current Status, Interconnectedness, and Future Directions

This article reviews the status and recent developments in the integration of cloud computing and deep learning, as well as the interrelationship between these two technologies. The paper explores the intersection of cloud computing and deep learning in addressing cybersecurity challenges. Amidst the rapid expansion of the worldwide public cloud services market, the vulnerability to cyber-attacks and breaches in data management is on the rise. Different intrusion detection systems use different deep learning techniques to improve the effectiveness of intrusion detection in cloud computing environments. Additionally, the use of encryption technology and the corresponding deep learning retrieval technology further improves the security of cloud data. Moreover, the paper deeply studies how the scheduling mechanism of deep reinforcement learning can optimize the performance of cloud services by efficiently allocating resources and solving the problem of slow cloud service speed. It also derives the optimal energy strategy through deep neural networks to address the energy consumption challenges in cloud computing data centers. This article also reviews the five emerging architectures of cloud computing and explores the role of deep learning within these frameworks. Finally, it analyzes some of the challenges facing the future of cloud computing and deep learning, including the security and confidentiality of cloud computing, as well as low latency and high throughput optimization in the field of deep learning. In summary, this article provides insight into current trends, challenges, and future prospects for the evolving integration between cloud computing and deep learning.

Cloud storage cost: a taxonomy and survey

Cloud service providers offer application providers with virtually infinite storage and computing resources, while providing cost-efficiency and various other quality of service (QoS) properties through a storage-as-a-service (StaaS) approach. Organizations also use multi-cloud or hybrid solutions by combining multiple public and/or private cloud service providers to avoid vendor lock-in, achieve high availability and performance, and optimise cost. Indeed cost is one of the important factors for organizations while adopting cloud storage; however, cloud storage providers offer complex pricing policies, including the actual storage cost and the cost related to additional services (e.g., network usage cost). In this article, we provide a detailed taxonomy of cloud storage cost and a taxonomy of other QoS elements, such as network performance, availability, and reliability. We also discuss various cost trade-offs, including storage and computation, storage and cache, and storage and network. Finally, we provide a cost comparison across different storage providers under different contexts and a set of user scenarios to demonstrate the complexity of cost structure and discuss existing literature for cloud storage selection and cost optimization. We aim that the work presented in this article will provide decision-makers and researchers focusing on cloud storage selection for data placement, cost modelling, and cost optimization with a better understanding and insights regarding the elements contributing to the storage cost and this complex problem domain.

Data Security on Cloud Services: Threats and Mitigation

We try to examine the state of cloud computing today and the difficulties in protecting data across several cloud services. The rapid increase in the adoption of public and private cloud services by organizations poses a significant security risk for organizations. We examine various security challenges unique to the cloud environment and various mitigation techniques to deal with the threats. Our paper delves into the collaborative aspect of cloud security, highlighting the shared responsibility model between cloud providers and customers. Additionally, we also investigate various mechanisms adopted by cloud service providers to mitigate security risks. Keywords— Cloud computing, Data threats, Data Protection, Cloud security,

Navigating The Cloud: Enhancing Human-Robot Collaboration In Industry 4.0 Through Digital Twins And Cloud-Based Systems

Industry 4.0 Is Geared Towards Establishing Sophisticated Industrial Ecosystems That Necessitate Secure And Efficient Collaboration Between Humans And Robots, Termed Human-Robot Collaboration (HRC). Within This Framework, HRC Leverages Cutting-Edge Collision Avoidance Technology Capable Of Detecting Objects And Foreseeing Possible Collisions, Thereby Calculating Alternative Pathways To Avert Any Direct Contact. Furthermore, The Concept Of Digital Twins Emerges As An Innovative Solution, Offering A Platform To Simultaneously Evaluate The Consequences Of Various Control Strategies Within A Digitally Simulated Environment. While Cloud Computing Infrastructure Can Furnish The Requisite Computational Prowess, It May Simultaneously Introduce Challenges Such As Increased Latency And Variability In Communication, Known As Jitter, Which Could Potentially Hinder System Performance. Cloud Technologies Are Renowned For Their Inventive Approaches To Ease The Workload For Software Developers And System Operators, Raising Questions About Their Integration With Digital Twin Methodologies And The Resilience Of Robotic Systems Against The Potential Delays Incurred From Cloud-Based Services. This Research Endeavors To Navigate These Complexities By Integrating A Blend Of Both Public And Private Cloud Services, Which Are Distinguished By Their Unique Parallel Processing Capabilities. This Study's Contributions Are Threefold. Firstly, It Introduces A Novel Method Designed To Gauge The Efficacy Of Diverse Strategies By Focusing On Their Associated Latency Impacts. Secondly, It Delineates A Tangible Application Of HRC, Illuminating Its Practical Benefits And Real-World Applicability. Lastly, It Defines A Crucial Performance Metric Intended To Assess The Efficiency Of These Systems Thoroughly. By Delving Into These Aspects, The Research Aims To Conduct A Comprehensive Analysis Of The Strengths And Weaknesses Inherent In Various Technological Methodologies And Their Consequential Effects On The Performance Of HRC Systems Within The Ambit Of Industry 4.0. Through This Detailed Examination, The Study Seeks To Provide Valuable Insights Into Optimizing Human-Robot Collaboration In Industrial Settings, Ensuring Both Security And Efficiency Are Upheld

Leveraging public cloud infrastructure for real-time connected vehicle speed advisory at a signalized corridor

In this study, we developed a real-time connected vehicle (CV) speed advisory application that uses public cloud services and tested it on a simulated signalized corridor for different roadway traffic conditions. First, we developed a scalable serverless cloud computing architecture leveraging public cloud services offered by Amazon Web Services (AWS) to support the requirements of a real-time CV application. Second, we developed an optimization-based real-time CV speed advisory algorithm by taking a modular design approach, which makes the application automatically scalable and deployable in the cloud using the serverless architecture. Third, we developed a cloud-in-the-loop simulation testbed using AWS and an open-source microscopic roadway traffic simulator called Simulation of Urban Mobility (SUMO). Our analyses based on different roadway traffic conditions showed that the serverless CV speed advisory application meets the latency requirement of real-time CV mobility applications. Besides, our serverless CV speed advisory application reduced the average stopped delay (by 77%) and the aggregated risk of collision (by 21%) at the signalized intersections of a corridor. These prove the feasibility as well as the efficacy of utilizing public cloud infrastructure to implement real-time roadway traffic management applications in a CV environment.

THE STRUCTURE OF INFORMATION AND EDUCATION ENVIRONMENT OF A MEDICAL UNIVERSITY

The authors examine the process of transformation of the educational infrastructure of medical universities during the transition to a mixed form of studying while COVID – 19 quarantine. Analysis of scientific publications on the digital transformation of the higher education system has shown the effectiveness of a hybrid model for organizing a virtual learning environment, which combines the ground-based infrastructure of the university and can include private and public cloud services. Hybrid model allows providing appropriate technological conditions for quality organization of teachers' work in online mode of distance learning system. Cloud services allow organizing a virtual group educational environment - a virtual classroom and a personal environment for students. The authors analyzed the changes in the infrastructure of Zaporizhzhia State Medical University in the process of digital transformation of the educational process during the period of COVID-19 quarantine, which became the ground for the development of a structural model of the information and educational environment of the university. The work focuses on the fact that one of the features of the educational process in medical universities is the usage of special learning tools: software and hardware systems for examination of a patient's condition, technical means of telemedicine, classroom self-training sessions for Licensing Integrated Exams “KROK” 1,2,3 on the basis of licensed software, etc. The authors conducted a systematic analysis and identified the technological components of the unified information educational environment of medical HEIs, namely: corporate computer network; workplaces of participants in the development of online courses (designer, lecturer of videoconferencing, moderator of video lectures or webinars, designer of video processing; specialized modeling systems; simulation complexes; software and hardware complexes for processing biological signals; specialized classrooms: computer room of the library, lecture halls for remote video lectures with interactive communication, lecture halls for interactive lectures, media centers for webinars and telemedicine consultations; cloud services . The proposed structure of information and educational environment of a medical university was implemented in the educational process of Zaporizhzhia State Medical University which made possible the creation of a cloud-oriented educational environment. The usage of hybrid cloud model allowed to solve the issue of integration of virtual environments of departments, in particular, territorially remote ones, and the ground infrastructure of the university.

High-cycle and low-cycle fatigue life prediction under random multiaxial loadings without cycle counting

A novel fatigue life prediction method under random multiaxial loadings is proposed in this paper. One unique benefit of the proposed method is that it is based on the time-derivative fatigue crack growth formulation and does not need cycle counting under arbitrary loadings. First, a brief review of subcycle fatigue crack growth (FCG) analysis and the equivalent initial flaw size (EIFS) concept for life prediction is introduced. Next, the existing subcycle fatigue crack growth model is extended to near-threshold conditions. An intrinsic fatigue threshold of the material is introduced, and a hypothesis is proposed that the crack only grows when the local stress intensity factor is beyond the intrinsic threshold. Following this, the analogy of stress intensity factor and strain intensity factor is used to extend the fatigue life prediction model to strain-based fatigue analysis. Correction for large plastic deformation is included to handle low-cycle fatigue life prediction. The novelties of this model are that it considers the FCG at the threshold and near-threshold region and it is able to predict both HCF and LCF conditions using FCG-based life prediction. Following this, extensive in-house and literature data for AL-7075-T6 under uniaxial and multiaxial, constant, and random loadings are used for model validation. Discussions for the effect of model parameters are provided based on parametric analysis. Conclusions and future work are mentioned. Code and data are released in public cloud service for interested readers.

Research of protocols and technologies data transmissions in IoT networks

Important functions of the concept of the Internet of Things are the facilitation of everyday life, improvement of efficiency and quality of work, energy saving, etc. More and more often, data from "things" are transferred to the public communication network and cloud services. Any objects in the physical world that can be assigned IP addresses and are capable of transmitting data can act as "things" in such a network. Various technologies and protocols are used for data transmission, of which there are currently more than twenty. In the near future, a large number of devices will be connected to the Internet of Things. Most of these devices will run on batteries. At the same time, one of the main characteristics is the duration of equipment operation without human intervention. In order to solve this problem, new networks were created specifically for the Internet of Things. These networks are called LPWANs. At the same time, one of the key issues is the optimal selection of the method of information transfer between nodes in the Internet of Things concept. After conducting an analysis of open sources, it is possible to conclude about the prospects of using the following technologies for these purposes: NB-IoT, Weightless, LoRa, SigFox. The subject of research in the article is long-distance data transmission technologies and protocols used to send information from a sensor to a cloud service in the context of the Internet of Things. The purpose of the study is to review and compare the main technologies and protocols for long-distance data transmission in Internet of Things networks. A comparative analysis of the main characteristics of long-distance data transmission protocols and technologies of the LPWAN network was carried out: NB-IoT, Weightless, LoRa, SigFox. The results of the analysis are presented in the form of a table. Experimental results have shown that the effectiveness of the considered protocols and technologies depends on various conditions of the communication network. In this regard, currently the choice of protocols for devices depending on operating conditions helps to solve the problem of saving resources, both energy consumption and guaranteed delivery. This is especially relevant in connection with the spread of the Internet of Things technology and the increase in the total number of "things".

Availability and Reliability Modeling of Mobile Cloud Architectures

In recent years, mobile cloud computing has attracted researchers and industries' interest since it maximizes gains with mobile computing in different areas, such as healthcare, sports, and business. However, the inherent challenges associated with mobile computing (e.g., battery provisioning and wireless network instability) may limit mobile cloud achievements. This paper provides an availability and reliability study of mobile clouds and evaluates distinct architectures and strategies. Our evaluation is based on hierarchical heterogeneous models and focuses on steady-state availability, reliability, annual downtime, and costs of implementation and provisioning of the cloud infrastructure. The results point out that service downtime may be significantly decreased by adopting actions such as spare batteries for mobile devices and the adoption of distinct redundancy strategies. We also propose a way to support decision-making considering private and public clouds by comparing some architectural choices while considering factors such as budget expenses for hardware purchases and power consumption. After 36 months, the private cloud has a cumulative cost that is smaller than the cost of a public cloud service.

Financial Case Study on the Use of Cloud Resources in HEP Computing

An all-inclusive analysis of costs for on-premises and public cloudbased solutions to handle the bulk of HEP computing requirements shows that dedicated on-premises deployments of compute and storage resources are still the most cost-effective. Since the advent of public cloud services, the HEP community has engaged in multiple proofs of concept to study the technical viability of using cloud resources; however, the financial viability of using cloud resources for HEP computing and storage is of greater importance. We present the results of a study comparing the cost of providing computing resources in a public cloud and a comprehensive estimate for the cost of an on-premises solution for HEP computing. Like previous studies, the fundamental conclusion is that for the bulk of HEP computing needs, on premises is significantly more cost effective than public clouds.

AWS Cloud Computing Solutions: Optimizing Implementation for Businesses

This study delves into the optimization strategies of Amazon Web Services (AWS) cloud computing solutions tailored specifically for businesses. It provides an in-depth exploration of the transformative impact that cloud computing has had on modern business operations, emphasizing the pivotal role played by AWS in delivering scalable, flexible, and cost-effective resources. With projections indicating the global public cloud services market's growth to $332.3 billion in 2021, it becomes evident that cloud solutions are increasingly relied upon, with AWS commanding a significant 32% market share. This paper highlights the importance of optimizing AWS solutions, considering its extensive range of services designed to meet diverse business needs. However, transitioning to cloud environments brings challenges related to data security, compliance, integration, and migration complexities, as evidenced by pertinent studies and literature reviews. This research synthesizes insights gleaned from various studies, emphasizing the adaptability and cost-effectiveness of AWS while stressing the critical role of robust security measures in its implementation across industries and geographic regions. Understanding prevalent business requisites and challenges in cloud adoption, including scalability, cost efficiency, reliability, availability, and compliance, is essential for organizations seeking to harness cloud technology effectively. Furthermore, this paper provides an overview of AWS's services encompassing computing, storage, databases, machine learning, and AI, showcasing how these empower businesses to streamline operations, foster innovation, and scale dynamically. It elucidates successful instances where AWS was strategically implemented in Netflix, Airbnb, Jollibee Group, and Capital One, demonstrating its impact on scalability, innovation, and service enhancement. The paper outlines anticipated trends in AWS and cloud computing, focusing on user-friendly tools, advancements in machine learning, infrastructure improvements, increased automation, and deeper integration of AI and advanced analytics. Ultimately, this research emphasizes business's need to optimize AWS solutions, navigate complexities, foster innovation, and achieve operational excellence in an increasingly competitive market.

THE RELATIONSHIP BETWEEN INTERNET USE AND LIFE SATISFACTION IN LATER LIFE: A FOCUS ON THIRD-LEVEL DIGITAL DIVIDE

Abstract The third-level digital divide highlights how social factors can shape disparities in real-life experiences derived from digital use. This study aims to examine how different types of internet use are related to the life satisfaction of older adults with varying health and wealth statuses through the lens of the third-level digital divide. The data for this study came from the 2022 Korea Digital Divide Survey. A sample of 1,165 older adults aged between 65 and 96 years old was grouped into four categories based on their health and wealth statuses, considering that these two factors tend to shape the social positions of older adults in Korean society. Life satisfaction was measured using 5 items from the 4-point Satisfaction with Life Scale (SWLS), while different types of internet use were accessed with 13 dichotomous items. The findings showed that a significant positive effect of internet use was observed only among older adults with good health status, indicating a stronger effect for those with good wealth status. Particularly, online public services (d=1.41, p< 0.001), cloud services (d=1.35, p< 0.001), and social networking services (d=1.27, p< 0.001) exhibited significant effects. No significant effect was observed for older adults with poor health status, except for online entertainment content, which was found to be significantly related to the life satisfaction of those with low wealth status (d=0.87, p< 0.01). The study findings imply diversity in terms of internet use and its association with life satisfaction, while highlighting social disparities.

Exploring the General Data Protection Regulation (GDPR) compliance in cloud services: insights from Swedish public organizations on privacy compliance

The adoption of cloud services offers manifold advantages to public organizations; however, ensuring data privacy during data transfers has become increasingly complex since the inception of the General Data Protection Regulation (GDPR). This study investigates privacy concerns experienced by public organizations in Sweden, focusing on GDPR compliance. A qualitative interpretative approach was adopted, involving semi-structured interviews with seven employees from five public organizations in Sweden. Additionally, secondary data were gathered through an extensive literature review. The collected data were analyzed and classified using the seven privacy threat categories outlined in the LINDDUN framework. The key findings reveal several significant privacy issues when utilizing public cloud services, including unauthorized access, loss of confidentiality, lack of awareness, lack of trust, legal uncertainties, regulatory challenges, and loss of control. The study underscores the importance of implementing measures such as anonymization, pseudonymization, encryption, contractual agreements, and well-defined routines to ensure GDPR compliance. The findings emphasize the importance of implementing measures such as anonymization, pseudonymization, encryption, contractual agreements, and well-defined routines to ensure GDPR compliance. Furthermore, this research highlights the critical aspect of digital sovereignty in addressing privacy challenges associated with public cloud service adoption by public organizations in Sweden.

The Proposed Framework for Cloud Computing System Security and Privacy Services

Public cloud computing has emerged as a popular and efficient solution for organizations to store, process, and access their data and applications. However, with the increasing adoption of public cloud services, concerns regarding security have become a critical factor in decision-making processes. This study aims to investigate the security aspects of public cloud computing and identify potential vulnerabilities and threats that organizations may face. The aim of this study is to examine the challenges associated with cloud computing and its security concerns. Cloud computing is a technology that utilizes the internet to share data and resources. It is characterized by five key features: On-Demand self-service, Resource Pooling, Broad network access, Rapid elasticity, and Measured Service. However, despite these benefits, cloud computing has several open issues, including Security, Availability, Scalability, and Interoperability. Traditional security techniques are insufficient in fully securing cloud computing. Security and privacy issues often arise during cloud deployment and delivery models. As a result, researchers are interested in learning more about cloud computing security and its methods. Industries are creating architectural models with a high level of protection for their data center to solve current security challenges. This article examines the security of cloud deployment strategies and analyzes cloud security and privacy challenges. The cloud computing service models are also discussed, along with any security issues. The study emphasizes the significance of Audit as a key factor in offering a security solution that covers all relevant security characteristics as determined by the literature. The study's conclusions present a fresh challenge to cloud service providers and suggest a new security paradigm that explains customer security needs. Overall, this study offers a parameter-by-parameter assessment of available security solutions and makes recommendations for enhancements to address cloud security issues.

Development of a cloud intelligent machine monitoring and control system

Facing the trend of Industry 4.0, the cloud-based supervisory control and data acquisition (SCADA) system employing cloud computing and IoT technology can help the manufacturing industry reduce software investment and system maintenance costs. However, manufacturers may need to install new sensors and controllers, the connection of SCADA system and shop floor machine controller, monitoring dashboard design and implementation usually need to outsource to an experienced system integration company, which may impede medium-sized manufacturing enterprises (SMEs). This paper aims to develop a cloud-based intelligent machine monitoring and control system (CIM-MCS) framework, the service structure, and approach to deploying the CIM-MCS in a public cloud infrastructure platform and service provider. The package diagram is proposed for building the CIM-MCS’s virtual factory model to improve modeling efficiency and data stability. CIM-MCS and its SCADA application in a leading automatic filling and packaging production line show that the CIM-MCS is easy to implement. The transmission time is short and acceptable for practical application. The integration of CIM-MCS with a cloud-based advanced planning scheduling system has the advantage of real-time monitoring, production progress reporting, scheduling, and dispatching and achieves the goal of anytime, anywhere, anyone, and any platform operating an intelligent factory.

Prototyping of Utilization Model for KOMPSAT-3/3A Analysis Ready Data Based on the Open Data Cube Platform in Multi-Cloud Computing Environment: A Case Study

This study introduces a multi-cloud model that combines private and public cloud services for processing and managing satellite images. The multi-cloud service is established by incorporating private clouds within organizations and integrating them with external public cloud services to utilize the data. Private clouds can maintain data security within an organization or between organizations, while public clouds offer easy processing options for general users with access accounts. The model for the private cloud service utilizes open-source OpenStack software to create virtual machines, allowing users to manage analysis ready data (ARD) of the Korea Multi-Purpose Satellite (KOMPSAT)-3/3A images simultaneously. The public cloud service through Amazon Web Services (AWS) offers four services and uses the Open Data Cube (ODC) to manage data and provide web-based time-series visualization and processing. The model utilizes OpenStack to create virtual machines, and the public cloud service through AWS offers various services using ODC to manage data. A system that handles large amounts of satellite imagery in a multi-cloud environment has benefits such as improved availability, cost savings through open-source, and enhanced scalability. We present a prototyped utilization model that can be used with the ODC user interface (UI) that applies the proposed multi-cloud model. The multi-cloud model of this study can be applied to constructing a country-scale data cube system, that deals with large-scale satellite image data. It can also be applied to systems that need to be built with data that is tailored to a specific user’s needs at any institution.

Work Flow Model, Major Challenges and Issues of Dynamic Load Balancing Scheme in Public Cloud Service

Work Flow Model, Major Challenges and Issues of Dynamic Load Balancing Scheme in Public Cloud Service

An Approach to Managing Public Services that uses Big Data and Cloud Computing

Abstract: Governmental regulations have accelerated the development of information technology and resource grouping. One area of "digital gold" that is extremely practical and scientific is large-scale data mining. This is not simply a contemporary issue; it also calls for the rapid expansion of public service. As a result, it is possible to fully realise the advantages of information, improve service effectiveness, and close the service gap. Due to its speedy accounting, low memory requirements, and effective resource allocation, cloud computing has grown in popularity. Big data was therefore used in this work to analyse the current state of public cloud services, and fuzzy evaluation was used to analyse accurate monitoring. Eventually, the structure of the public service was enhanced, and a strategy for the precise organisation of the complete big data-driven public service process was created. According to a fuzzy comprehensive assessment, the design value and data entropy of public cloud services were increasing exponentially; the average design value of the accurate organisation for cloud-based government services was around 4.35, and the average significance of data entropy was around 0.98. Furthermore, big data-driven precision organisation of social public social welfare outperformed traditional precision monitoring of cloud-based services for the public, with results that suggested effects were 7% higher and overall capacity was 9% higher than the compared techniques.