Cloud Systems Research Articles

IMERG V07B and V06B: A Comparative Study of Precipitation Estimates Across South America with a Detailed Evaluation of Brazilian Rainfall Patterns

Satellite-based precipitation products (SPPs) are essential for climate monitoring, especially in regions with sparse observational data. This study compares the performance of the latest version (V07B) and its predecessor (V06B) of the Integrated Multi-satellitE Retrievals for GPM (IMERG) across South America and the adjacent oceans. It focuses on evaluating their accuracy under different precipitation regimes in Brazil using 22 years of IMERG Final data (2000–2021), aggregated into seasonal totals (summer, autumn, winter, and spring). The observations used for the evaluation were organized into 0.1° × 0.1° grid points to match IMERG’s spatial resolution. The analysis was restricted to grid points containing at least one rain gauge, and in cases where multiple gauges were present within a grid point the average value was used. The evaluation metrics included the Root Mean Square Error (RMSE) and categorical indices. The results reveal that while both versions effectively capture major precipitation systems such as the mesoscale convective system (MCS), South Atlantic Convergence Zone (SACZ), and Intertropical Convergence Zone (ITCZ), significant discrepancies emerge in high-rainfall areas, particularly over oceans and tropical zones. Over the continent, however, these discrepancies are reduced due to the correction of observations in the final version of IMERG. A comprehensive analysis of the RMSE across Brazil, both as a whole and within the five analyzed regions, without differentiating precipitation classes, demonstrates that version V07B effectively reduces errors compared to version V06B. The analysis of statistical indices across Brazil’s five regions highlights distinct performance patterns between IMERG versions V06B and V07B, driven by regional and seasonal precipitation characteristics. V07B demonstrates a superior performance, particularly in regions with intense rainfall (R1, R2, and R5), showing a reduced RMSE and improved categorical indices. These advancements are linked to V07B’s reduced overestimation in cold-top cloud regions, although both versions consistently overestimate at rain/no-rain thresholds and for light rainfall. However, in regions prone to underestimation, such as the interior of the Northeastern region (R3) during winter, and the northeastern coast (R4) during winter and spring, V07B exacerbates these issues, highlighting challenges in accurately estimating precipitation from warm-top cloud systems. This study concludes that while V07B exhibits notable advancements, further enhancements are needed to improve accuracy in underperforming regions, specifically those influenced by warm-cloud precipitation systems.

AI-Powered Anomaly Detection for Real-Time Performance Monitoring in Cloud Systems

Owing to the ever growing popularity of cloud computing, there is increased need for performance monitoring coupled with real time monitoring for detection of anomalous behavior for assured availability and reliability. Nevertheless, the current practical implementation of performance monitoring in the cloud systems have certain limitations which prevent their efficient usage in the real time performance anomaly detection. The first important limitation is related to the limitations of approaches like anomalous behaviour detection in large scale and dynamically changing cloud environments, which make the task to detect such anomalies slow and highly unsuitable for large distributed systems [1], [2]. Besides, these methods apply predefined thresholds or continue data baselines, leading to higher false alarm ratio and frequent overlook of significant anomalies within altered cloud systems [3], [5]. A final problem is the rigidity of many current methods that can often fail to identify new or previously unidentified performance problems [4], [6]. Also, there is practice that requires more sophistication in the triggering of the mitigation measures to be automated to eliminate the overhead and delay in responding that cloud administrators face [7]. This paper examines these shortcomings in the existing methods for real-time anomaly detection in cloud systems, and presents the arguments for newer approaches for addressing the evolving challenges of cloud computing.

Cloud-Native Systems Implementation in the Polish SME Enterprises

Purpose: The main purpose of the article is to examine the needs and the attitudes of small and medium enterprises sector regarding cloud computing usage in implementation of Management Information System in cloud-native strategy. Other objectives of the study are to examine the state-of-the-art of cloud computing models and cloud-native approach in SMEs and the willingness of SMEs to develop cloud-based solutions such as MIS software. Design/methodology/approach: Methods used in research are critical analysis of scientific literature and reports and publicly available statistical data analysis. A literature review about cloud computing, implementation methodologies, security needs and concept of building cloud native strategy is presented likewise to the summary and characteristics of SMEs (small and medium enterprises), their specific needs, ways of doing business, the most common information systems used by them, and the adoption of cloud-based versions of these systems. Findings: The main result and findings of the research are presented as a SWOT analysis of MIS systems implementation in the cloud environment. The paper presents updated state of knowledge in cloud-native MIS systems implementation from the Polish perspective and updated characteristics of small and medium enterprises sector in Poland. Research limitations/implications: The study used secondary data obtained only from the open databases available at this stage of the research. Subsequent stages of research in the area of security of native cloud systems should include data obtained directly from companies and other economic entities operating in various sectors using cloud technologies and implementing IT systems in accordance with NIST standards. In the research methodology, the applied strategic analysis methods can be expanded to include statistical and econometric methods. Originality/value: Article can provide value for SME decision makers and software development companies interested in developing their products in the cloud environment.

Securing Privacy in Cloud Computing: A Technical and Regulatory Perspective

The fast acceptance of cloud computing technologies has drastically changed organizational data management techniques and presented difficult problems at the junction of technical innovation and regulatory compliance. Focusing on important frameworks such as GDPR, HIPAA, and CCPA, this article explores cloud systems' changing terrain of data privacy rules. It analyzes their implications for organizational compliance strategies. Using a thorough examination of technological needs, shared responsibility models, and implementation issues, the companies negotiate the complex equilibrium between using cloud capabilities and preserving strong data privacy protections. This article presents industry practices, legal criteria, and new technical solutions to offer a foundation for comprehending and handling cloud privacy compliance issues. The results underline the crucial need for a complete approach to cloud privacy governance, including corporate policies, operational practices, and technology controls. This article adds to the growing corpus of knowledge on cloud privacy compliance by providing an analysis of successful approaches for controlling data privacy in ever more complicated cloud systems.

Assessing the ducting phenomenon and its potential impact on Global Navigation Satellite System (GNSS) radio occultation refractivity retrievals over the northeast Pacific Ocean using radiosondes and global reanalysis

Abstract. In this study, high-resolution radiosondes from the Marine Atmospheric Radiation Measurement (ARM) Global Energy and Water Experiment (GEWEX) Cloud System Study (GCSS) Pacific Cross-section Intercomparison (GPCI) Investigation of Clouds (MAGIC) field campaign and ECMWF Reanalysis version 5 (ERA5) global reanalysis data are used to assess characteristics of the elevated ducting layer along a transect over the northeastern Pacific Ocean from Los Angeles, California, to Honolulu, Hawaii. The planetary boundary layer (PBL) height (PBLH) increases as the strength of the refractivity gradient decreases westward along the transect. The thickness of the prevailing ducting layer remains remarkably consistent (∼ 110 m) in the radiosonde data. On the other hand, the ERA5 reanalysis generally resolves the ducting features well, but it underestimates the ducting height and strength, especially over the trade cumulus region near Hawaii. A simple two-step end-to-end simulation is used to evaluate the impact of the elevated ducting layer on radio occultation (RO) refractivity retrievals. A systematic negative refractivity bias (N bias) below the ducting layer is observed throughout the transect, peaking (−5.42 %) slightly below the PBLH and gradually decreasing towards the surface (−0.5 %). The N bias shows a strong positive correlation with the ducting strength. The ERA5 data underestimate the N bias, with the magnitude of the underestimation increasing westward along the transect.

Analytical review of confidential artificial intelligence: methods and algorithms for deployment in cloud computing

The technologies of artificial intelligence and cloud systems have recently been actively developed and implemented. In this regard, the issue of their joint use, which has been topical for several years, has become more acute. The problem of data privacy preservation in cloud computing acquired the status of critical long before the necessity of their joint use with artificial intelligence, which made it even more complicated. This paper presents an overview of both the artificial intelligence and cloud computing techniques themselves, as well as methods to ensure data privacy. The review considers methods that utilize differentiated privacy; secret sharing schemes; homomorphic encryption; and hybrid methods. The conducted research has shown that each considered method has its pros and cons outlined in the paper, but there is no universal solution. It was found that theoretical models of hybrid methods based on secret sharing schemes and fully homomorphic encryption can significantly improve the confidentiality of data processing using artificial intelligence.

Cloud and Payment Systems Under Siege: A Deep Dive into FBot Malware

FBot, a sophisticated Python-based malware, poses a significant threat to cloud services and payment systems. This article explores its architecture, capabilities, and operational patterns, analyzing how it exploits misconfigurations, payment vulnerabilities, and third-party dependencies. Through mapping with frameworks like MITRE ATT&CK and CAPEC, and leveraging AI-powered defense strategies, this study provides insights for cybersecurity teams, highlighting the necessity of proactive measures and robust risk management. Case studies illustrate the organizational impact and underscore the importance of enhanced policies to combat such evolving threats.

A Numerical Study on Cloud Structure of Typical Summer Precipitation Process over Liupan Mountain Area

Using Weather Research and Forecasting model (WRF), four microphysical schemes (Lin, Goddard, WSM6 and Thompson) were applied for precipitation simulations over southern Ningxia on July 21st, 2024. The simulated results are used to analyze the impact of different microphysical schemes on summertime precipitation processes. The sensitivity experiments show the precipitation process simulated by WSM6 and Thompson schemes are closer to observed precipitation than Lin and Goddard schemes in Southern Ningxia mountainous areas. The evolution characteristics of the dynamic field, water vapor field, and microphysical structures of cloud structures are analyzed. In vertical direction, the cloud system in mountainous areas generally displays a structure of "catalysis-supply". The structure of water condensation in each layer of cloud is different, leading to differences in the contribution of various microphysical processes to precipitation. The super-cooled cloud water is the main source for rain water production. When cloud system grows upward, the abundant cloud water layer simultaneously enhances the microphysical processes within clouds. On the windward slope (eastern part) of the mountain, deep warm cloud layer will contribute to the development of warm cloud precipitation process, which leads to enhanced precipitation under the combined action of cold and warm clouds.

APLICANDO METODOLOGIA BASEADA EM IDS, SOLUCIONANDO VULNERABILIDADES EM NUVEM (SAAS)

Advances in technology have led to the use of cloud systems, the storage and distribution of files over the Internet, especially Saas (Software as a Service), which is now widely used by businesses due to its great advantage of scalability and practicality. However, in the midst of this technology, there has been an increase in ransomware attacks, a type of malware designed to encrypt data storage areas on computers and servers, causing users to lose access to their data in the cloud. The main objective of this work is to apply a methodology based on IDS (Intrusion Detection Systems), using the Snort tool in a virtualized network environment in Oracle Virtualbox, on the Kali Linux virtual machine, to analyze and solve problems with ransomware attacks in the cloud. The goal is to present ideas and considerations to train users to take security measures against these attacks should they occur at any time, making the work of cybercriminals more difficult. To keep this storage environment clean and secure, we need the cooperation of the security software, with rules and alerts that are always up to date, together with the monitoring team.

Data Security and Data Protection in Cloud Privacy Systems

In this modern world, the internet has become very influential in our daily lives. The Internet has many uses to solve various problems, one of which is full hardware storage, therefore the Internet provides software-based data, for example, cloud storage applications. The study aims to analyze and evaluate various data security techniques and protection methods used in the cloud computing framework, focusing on vulnerability identification, risk mitigation strategy assessment, and user security awareness improvement. In addition, this study aims to analyze data security techniques and data protection approaches applied to cloud systems. In addition, the study also assesses potential risks and mitigates security threats such as cyberattacks and data breaches. Using qualitative methodologies, the study investigated relevant literature, examined real-life cases of data breaches, and analyzed the effectiveness of security measures such as encryption and multi-factor authentication. The findings show that cloud systems remain vulnerable to cyber threats like DDoS attacks, credential theft, and data breaches due to inadequate security protocols. Key recommendations include adopting encryption, improved multi-factor authentication practices, and encouraging collaboration between cloud service providers and users to strengthen data security. The research contributes by proposing a comprehensive approach to improving data protection in cloud environments, ensuring a secure and resilient infrastructure for users. Implementing these findings will support the development of more secure cloud technologies and increase user confidence in data security practices. It is hoped that the study's results can provide recommendations to improve data security and data protection in cloud systems.

Full-process supported Simulation Platform Framework based on cloud computing and HPC integration

Simulation technology is widely used in many fields, it usually involves three processes, (i) pre-process, (ii) analysis solver, and (iii) post-process. Simulation calculations require a large amount of computing resources, and users usually need to use cloud and High-Performance Computing (HPC) systems to complete works. Simulation works are increasingly depending on the capacity of HPC or cloud, for cost reasons, people are more willing to use the services than self-built an HPC or Cloud computing cluster. However, that leads to the isolation of calculations and pre- and post-processing work, adding additional time for data transfer. Moreover, simulation engineers also want to use cloud servers in pre-processing and post-processing, since compared with local workstations, cloud servers have significant advantages in saving hardware investment, remote office collaboration, and data integration management. Therefore, we provide a platform framework based on cloud computing and HPC integration that supports the full process of simulation. Then, we implemented it in Tianhe-1A and Tianhe exascale supercomputers and THCloud environments. Through a city area-level explosion simulation experiment, it was verified that the framework can fully support the whole process of simulation, and effectively reduce the time of simulation work, improving the simulation engineer’s work efficiency. The study shows that the platform provides a feasible solution for full-process simulation. Compared with other platforms, it has the characteristics of full-process, high performance and high security.

Towards accurate anomaly detection for cloud system via graph-enhanced contrastive learning

As a critical technology, anomaly detection ensures the smooth operation of cloud systems while maintaining the market competitiveness of cloud service providers. However, the resource data in real-world cloud systems is predominantly unannotated, leading to insufficient supervised signals for anomaly detection. Moreover, complicated topological associations existed between cloud servers (e.g., computation, storage, and communication). While acquiring resource information, correlating the system topology is challenging. To this end, we propose the GCAD for cloud system anomaly detection, which integrates data augmentation, GraphGRU, contrastive learning, and reconstruction. First, GCAD constructs positive and negative sample pairs through the masking and Gaussian noise data augmentation. Then, the GraphGRU processes extended temporal graph data, extracting and fusing spatiotemporal features from resource status and system topology. In addition, GCAD introduces linear attention for encoding spatiotemporal representations to capture their global correlation information. The weight parameters of the encoder are optimized using a contrastive learning mechanism. Finally, GCAD utilizes a reconstruction technique to calculate anomaly scores, facilitating the evaluation of the state of the cloud system at each time point. Experimental results indicate that GCAD outperforms state-of-the-art compared methods on two real-world datasets that contain topology information.

Comprehensive Analysis of Cloud Computing Performance Factors: Investigating the Impact of Response Time, Load Balancing and Service Broker Policies on Cloud Service Efficiency Using CloudSim Simulation

Cloud performance refers to the efficiency and effectiveness with which a cloud system operates delivering hosted services over the internet. As cloud computing continues to offer flexibility, scalability and computational power monitoring and improving cloud performance is essential. Performance optimization is influenced by factors such as load balancing and service broker policies which impact system response times and overall user experience. This paper provides an in-depth review of key publications and real-time cloud performance tools identifying critical performance factors that affect cloud efficiency. Notably, response time emerged as a fundamental metric for cloud service quality. Using CloudSim simulation we examine cloud performance evaluation criteria and experimentally assess the impact of response time dependencies on broker policies, load balancing techniques and data center distribution. This study offers a framework for understanding cloud performance evaluation and highlights strategies to enhance user experience in diverse cloud environments.

Investigation of thunderstorm characteristics with severe lightning events over NE region of India

Abstract. The north eastern region (NER) of India is among the hotspot regions for thunderstorm events leading to substantial loss of lives and properties every year. A reliable thunderstorm early warning system can prevent these losses. As these events are highly dynamic, prediction becomes equally challenging. Diagnostic studies of such events could provide more understanding of lightning and storms over a region which could then be used as input for the prediction of such events. Improving the efficiency of the prediction of associated meteorological parameters could improve the prediction of thunderstorm. In this study, two thunderstorm events have been identified with high lightning flash counts, using observations from the INSAT-3D satellite and lightning detection sensors. These observations revealed that one of the events was caused by a cloud system that originated elsewhere and travelled over the area of impact while the other event was an isolated convective system that occurred over a small region. The characteristic features of thunderstorms, like vertical wind velocity, cloud ice water content, relative humidity, cloud fraction and radar reflectivity, etc. were simulated using the WRF model. These simulations were validated with the ERA-5 reanalysis data and observations from Radar and satellite data. Validation results suggest that the WRF model could predict these features associated with thunderstorm events quite reliably. The study helped to improve the severe weather warning over NER of India and the stakeholders are trained on effective utilization of such warning.

Timely Detection of DDoS Attacks with Dimenstionality Reduction

Due to the interconnectedness and exponential proliferation of IoT devices, the technology is more susceptible to network attacks like Distributed Denial of Service (DDoS), which disrupt network resources. A growing threat to cloud computing systems is the Distributed Denial of Service (DDoS) attack, in which the attacker starts the attack by taking advantage of computers both inside and outside the cloud system. Real-time analysis of cloud network data is essential for preventing DDoS attacks. DDoS attacks interfere with the operation of Io- connected apps and services by taking advantage of the constrained resources on IoT devices. The impacts of DDoS attacks, which seriously damage current systems, are thoroughly examined in this article in the context of the Internet of Things. One of the most common network attacks is the distributed denial-of-service attack (DDoS). DDoS assaults intensified due to the quick development of computer and communication technologies. Therefore, investigating the detection of a DDoS attack is crucial. A single technique cannot offer adequate security due to the variety of DDoS attack techniques.

Containerized Artificial Intelligent System Design in Cloud and Cyber-Physical Systems

The integration of Artificial Intelligence (AI) into cloud computing and Cyber-Physical Systems (CPS) is crucial for achieving efficiency, scalability, and real-time capabilities in modern ecosystems. Containerization enhances AI deployment by improving portability, resource efficiency, and system isolation. This article addresses key design considerations and challenges in implementing con- tainerized AI within cloud-native and CPS environments, focusing on scalability, fault tolerance, real-time respon- siveness, and security. Through research analysis and case studies, it explores strategies for optimizing AI workload distribution across cloud and edge infrastructure to meet CPS demands. Future directions, including hybrid architectures and federated learning, are also discussed to support scalable, secure, and reliable AI systems for next- generation cloud and CPS applications.

A protocol for model intercomparison of impacts of marine cloud brightening climate intervention

Abstract. A modeling protocol (defined by a series of climate model simulations with specified model output) is introduced. Studies using these simulations are designed to improve the understanding of climate impacts using a strategy for climate intervention (CI) known as marine cloud brightening (MCB) in specific regions; therefore, the protocol is called MCB-REG (where REG stands for region). The model simulations are not intended to assess consequences of a realistic MCB deployment intended to achieve specific climate targets but instead to expose responses to interventions in six regions with pervasive cloud systems that are often considered candidates for such a deployment. A calibration step involving simulations with fixed sea surface temperatures (SSTs) is first used to identify a common forcing, and then coupled simulations with forcing in individual regions and combinations of regions are used to examine climate impacts. Synthetic estimates constructed by superposing responses from simulations with forcing in individual regions are considered a means of approximating the climate impacts produced when MCB interventions are introduced in multiple regions. A few results comparing simulations from three modern climate models (CESM2, E3SMv2, and UKESM1) are used to illustrate the similarities and differences between model behavior and the utility of estimates of MCB climate responses that were synthesized by summing responses introduced in individual regions. Cloud responses to aerosol injections differ substantially between models (CESM2 clouds appear much more susceptible to aerosol emissions than the other models), but patterns in precipitation and surface temperature responses were similar when forcing is imposed with similar amplitudes in the same regions. A previously identified La Niña-like response to forcing introduced in the Southeast Pacific is evident in this study, but the amplitude of the response was shown to markedly differ across the three models. Other common response patterns were also found and are discussed. Forcing in the Southeast Atlantic consistently (across all three models) produces weaker global cooling than that in other regions, and the Southeast Pacific and South Pacific show the strongest cooling. This indicates that the efficiency of a given intervention depends on not only the susceptibility of the clouds to aerosol perturbations, but also the strength of the underlying radiative feedbacks and ocean responses operating within each region. These responses were generally robust across models, but more studies and an examination of responses with ensembles would be beneficial.

A blockchain based secure authentication technique for ensuring user privacy in edge based smart city networks

In the past decade, modernization of Information and Communication Technology (ICT), Edge Computing (EC), and Smart Cities has attracted significant academic interest due to its diverse applications in the fields of healthcare, transportation, agriculture, and defense. EC offers numerous advantages, including faster and more efficient services, lower latency, improved data processing, managed bandwidth consumption, scalable, real-time decision-making, security, reduced network congestion, and increased resilience. Despite these benefits, EC networks face persistent challenges, particularly related to security and privacy concerns. Addressing these security challenges requires strong authentication mechanisms, which demand extra resources like processing power and memory, often surpassing the limited capabilities of lightweight edge devices compared to cloud systems. This highlights the critical need for securing edge nodes and ensuring user privacy before real-world deployment and data transfer. User and edge device authentication is vital to prevent external and internal Impersonation and Reflection attacks that threaten system integrity and confidentiality. This paper presents a BlockChain based Authentication technique for Edge Networks (BCAuthEN) that utilizes a Consortium Blockchain (CB) with key agreements for biometric authentication, incorporating a Fuzzy Extractor (FE) to secure user biometrics and passwords. In addition, BCAuthEN offers multifactor and continuous authentication by monitoring user behavior and biometrics. BCAuthEN has been formally verified through Real-Or-Random (RoR) modeling and AVISPA tool, proving its effectiveness in enhancing privacy, and security. The proposed technique ensures robust security by preventing attackers at the potential entry points (edge nodes). In addition, BCAuthEN reduces computation cost, communication overhead and improves throughput. BCAuthEN provides strong resilience by achieving high detection accuracy and reduces false positives against impersonation and reflection attacks. Results have shown that BCAuthEN improves communication costs and reduces overhead by 10% and 7%, respectively, as compared to the recent biometric and key-based user authentication techniques.

Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features

Cloud systems, typically comprised of various components ( e.g. , microservices), are susceptible to performance issues, which may cause service-level agreement violations and financial losses. Identifying performance issues is thus of paramount importance for cloud vendors. In current practice, crucial metrics, i.e. , key performance indicators (KPIs), are monitored periodically to provide insight into the operational status of components. Identifying performance issues is often formulated as an anomaly detection problem, which is tackled by analyzing each metric independently. However, this approach overlooks the complex dependencies existing among cloud components. Some graph neural network-based methods take both temporal and relational information into account, however, the correlation violations in the metrics that serve as indicators of underlying performance issues are difficult for them to identify. Furthermore, a large volume of components in a cloud system results in a vast array of noisy metrics. This complexity renders it impractical for engineers to fully comprehend the correlations, making it challenging to identify performance issues accurately. To address these limitations, we propose Identifying Performance Issues based on Relational-Temporal Features (ISOLATE ), a learning-based approach that leverages both the relational and temporal features of metrics to identify performance issues. In particular, it adopts a graph neural network with attention to characterizing the relations among metrics and extracts long-term and multi-scale temporal patterns using a GRU and a convolution network, respectively. The learned graph attention weights can be further used to localize the correlation-violated metrics. Moreover, to relieve the impact of noisy data, ISOLATE utilizes a positive unlabeled learning strategy that tags pseudo labels based on a small portion of confirmed negative examples. Extensive evaluation on both public and industrial datasets shows that ISOLATE outperforms all baseline models with 0.945 F1-score and 0.920 Hit rate@3. The ablation study also proves the effectiveness of the relational-temporal features and the PU-learning strategy. Furthermore, we share the success stories of leveraging ISOLATE to identify performance issues in Huawei Cloud, which demonstrates its superiority in practice.

Implementation of Samba Server Using OpenVPN Based on Single Board Computer (SBC) for Private Cloud Storage

In the current digital era, people's need for data storage media that is practical and can be accessed at any time is increasing. This research aims to design and implement a practical cloud-based data storage system using a Raspberry Pi 4 Model B device using the Samba and OpenVPN applications. The system focuses on storing users' data (private cloud), which allows users to directly access files and data via a storage server. The method used in this research includes a literature review to support system development. Testing was carried out to evaluate the security of the system being built by comparing access to private cloud server services before and after using the OpenVPN application. Test results show that using the OpenVPN application increases the security of data exchange, with good encryption in communications between client and server. The resulting system runs according to the initial design and can function as a secure private cloud system. This research can contribute to the development of efficient and secure data storage solutions, as well as show the potential for using the Raspberry Pi as an energy and cost-saving personal cloud server device.