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Data-intensive applications must be run on systems with high-performance processing and enough storage. When compared to conventional distributed systems like the data grid, cloud computing offers these features on a platform that is more flexible, scalable, and inexpensive. Moreover, retrieving data files is crucial to operate these services. Typically, accessing data causes the entire cloud workflow system to experience a bottleneck, thus significantly reducing system performance. Two key strategies that can enhance the efficiency of data-intensive applications are task scheduling and data replication. This research proposes a novel Data replication and scheduling in the cloud. Initially, the workflow management process is performed with 3 phases (1) workflow placement, (2) clustering of tasks, and (3) scheduling and replication. Initially, the workflow placement takes place. Then, the clustering of tasks is performed via an improved K-means algorithm. Finally, the tasks and datasets are replicated during the scheduling and replication phase. Further, the scheduling and replication are performed using the Self Modified Pelican Optimization Algorithm (SM-POA) based on the execution cost, migration cost, storage cost and replication. [ABSTRACT FROM AUTHOR]

Aerosol–cloud interactions play a crucial role in shaping Earth's climate and hydrological cycle. Observing these interactions with high precision and accuracy is of the utmost importance for improving climate models and predicting Earth's climate. Over the past few decades, lidar techniques have emerged as powerful tools for investigating aerosol–cloud interactions due to their ability to provide detailed vertical profiles of aerosol particles and clouds with high spatial and temporal resolutions. This review paper provides an overview of recent advancements in the study of ACI using lidar techniques. The paper begins with a description of the different cloud microphysical processes that are affected by the presence of aerosol, and with an outline of lidar remote sensing application in characterizing aerosol particles and clouds. The subsequent sections delve into the key findings and insights gained from lidar-based studies of aerosol–cloud interactions. This includes investigations into the role of aerosol particles in cloud formation, evolution, and microphysical properties. Finally, the review concludes with an outlook on future research. By reporting the latest findings and methodologies, this review aims to provide valuable insights for researchers engaged in climate science and atmospheric research. [ABSTRACT FROM AUTHOR]

Space-based atmospheric backscatter lidars provide critical information about the vertical distribution of clouds and aerosols, thereby improving our understanding of the climate system. They are additionally useful for detecting hazards to aviation and human health, such as volcanic plumes and man-made pollution events. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, 2006–2023), Cloud-Aerosol Transport System (CATS, 2015–2017), and Advanced Topographic Laser Altimeter System (ATLAS 2018–present) are three such lidars that operated within the past 20 years. The signal-to-noise ratio (SNR) for these lidars is significantly lower in daytime data compared with nighttime data due to the solar background signal increasing the detector response noise. Averaging horizontally across profiles has been the standard way to increase SNR, but this comes at the expense of resolution. Modern, deep learning-based denoising algorithms can be applied to improve the SNR without coarsening resolution. This paper describes how one such model architecture, Dense Dense U-Net (DDUNet), was trained to denoise CATS 1064 nm raw signal data (photon counts) using artificially noised nighttime data. Simulated CATS daytime 1064 nm data were then created to assess the model's performance. The denoised simulated data increased the daytime SNR by a factor of 2.5 (on average) and decreased minimum detectable backscatter (MDB) to ~ 7.3 × 10 − 4 km−1sr−1, which is lower than the CALIOP 1064 nm night MDB value of 8.6 × 10 − 4 km−1sr−1. Layer detection was performed on simulated 2 km horizontal resolution denoised and 60 km averaged data. Despite the finer resolution input, the denoised layers had more true positives, fewer false positives, and an overall Jaccard Index of 0.54 versus 0.44 when compared to the layers detected on averaged data. Layer detection was also performed on a full month of denoised daytime CATS data (Aug. 2015) to detect layers for comparison with CATS standard Level 2 (L2) product layers. The detection on the denoised data yielded 2.33 times more, higher-quality bins within detected layers at 2.7–33 times finer resolution than the CATS L2 products. [ABSTRACT FROM AUTHOR]

Along with the significant growth of applications and facilities provided by the Internet of Things (IoT) in recent years, security challenges and related issues to privacy become considerable interest of researchers. On the other hand, the de facto IoT routing protocol for low-power and lossy networks called RPL is vulnerable to various types of routing attacks. Many researchers have investigated RPL security solutions focusing on effective detection of prevalent and destructive routing attacks such as blackhole attack, selective forwarding attack, rank attack and so on. Recent studies are proposing trust-based mechanisms with the aim of replacing traditional cryptography-based operations with lightweight security models in order to cover the inherent challenges of IoT devices, including energy and computational limitations. Therefore, in this paper, focusing on the problem of RPL vulnerability against well-known routing attacks, we have proposed a trust-based attack detection model, which investigates traffic behavior in different attack scenarios and detects malicious nodes relying on behavior deviation exactly at the same time as the start of any attack activity. Expected behavior is predicted by our learning model trained from the historical routing behavior pattern, using recurrent neural networks as a powerful deep learning method, which leads to attack detection with high-level accuracy and precision. Both mathematical analysis and simulation results on multiple RPL attack scenarios show clearly that the proposed trust-based defense mechanism is an effective approach capable of timely and precisely detection of routing behavior pattern deviation of malicious nodes exactly at the start time of the attack occurrence, which leads to attack detection and attacker identification based on trust scores extracted from the detected fluctuations between expected and real routing behavior patterns. [ABSTRACT FROM AUTHOR]

This paper discusses the radiation budget and its temporal variability over Poland. The data analysis is based on the MERRA-2 reanalysis for the years 1980–2020. During the last four decades, the enhancement of climate warming has been observed, which coincides with the changes in the radiation budget. Positive and statistically significant trends at the top of the atmosphere (TOA; 0.7 ± 0.2 W/m2/10 year) and on the Earth's surface (1.5 ± 0.2 W/m2/10 year) radiation budget (net downward flux) are mainly a consequence of changes in the amount of aerosol and greenhouse gases (GHG). According to MERRA-2, the AOD during this period decreased by − 0.19 (at 550 nm), which is 87% of the long-term (1980–2021) mean value (0.22). The reduction of AOD is due mainly to the decline of non-absorbing sulfate particles, which leads to a reduction of single-scattering albedo (SSA) by − 0.008 per decade and Angstrom exponent (AE) by − 0.06 per decade (both trends statistically significant). On the other hand, the GHG concentration increased by 4.9%/10 year and 3%/10 year, respectively, for CO2 and CH4. The total column of water vapor increased (1.3%/10 year), while ozone decreased (− 1%/10 year). Despite the fact that cloud cover and cloud optical depth (COD) decreased (− 1.8%/10 year and − 1.0%/10 year), the impact of cloud on temporal variability radiation budget is small. It can be explained by nearly compensated shortwave (SW) cooling and longwave (LW) heating effects. During the analysis period, near-surface air temperature increased by 2.0 °C. The estimated increase in SW net surface radiation (7.9 W/m2) leads to climate warming by 0.8 °C, which is a consequence mainly of the reduction of aerosol (0.4 °C) and cloud cover (0.2 °C). The impact of the change of SW radiation on air temperature is more pronounced during the warm season, while during the cold, air temperature change is controlled mainly by the variability of air mass advection. [ABSTRACT FROM AUTHOR]

Current cloud computing expects to face huge traffic costs, data loads, and high latency due to the explosion of data from devices as the IoT and 5G technology evolve. Fog computing has emerged to overcome these issues. It deploys small fog servers at the edge of the network to process critical data in real time while sending the remaining secondary tasks to the central cloud, instead of sending massive amounts of data to the cloud. With the rise in fog computing, among traditional security threats, distributed denial-of-service (DDoS) attacks have become the major threat to availability. This is especially true for fog computing, where real-time processing is critical; there are many fog servers, and the processing power is relatively low. Distributed reflection denial-of-service (DRDoS), one of the frequently used DDoS attack techniques, is an amplification attack that can be used on a small or large scale. It is widely used in attack tools due to its easy configuration. This study analyzes the characteristics of fog computing, the characteristics of DRDoS attacks, and the advantages and disadvantages of existing countermeasures. Based on these analyses, this study proposes a model that could effectively mitigate attacks even on low-specification fog servers by combining a modified Snort module with reduced functionality, simple pattern matching, and filtering distribution using Anycast. This mitigation algorithm has a simple structure rather than a complex filtering structure. To achieve this goal, this study virtually implemented the corresponding fog IoT environment. In spite of its simple structure, it proved that the fog server could secure availability even under DRDoS attacks by implementing and validating the mitigation model. [ABSTRACT FROM AUTHOR]

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Early detection of diseases and pests is a key factor in eradicating or minimising the damage that these may cause. In this work, a comprehensive solution is presented that is based on the composition of existing cloud solutions and mobile tools to detect in-situ issues. The platform presented was used for the detection of powdery mildew and Cladosporium diseases in tomatoes. The results of using the approach to carry out this task were more than satisfactory since it managed to correctly detect the symptoms, having mAP of 0.41 in at least some of these symptoms. We analysed the performance of our dataset, on the one hand, and the combination of PlantDoc dataset, on the other hand. This shows that the platform can be used in the agriculture sector, as an additional tool for detecting diseases and pests in order to combat the problem and reduce its consequences. [ABSTRACT FROM AUTHOR]

Weekly cycles (WCs) in cloud properties have been reported and linked to aerosol effects. Yet the extent to which human activities contribute to their occurrence remains unclear. Here, we revisit aerosol–cloud interactions from the WCs over central Europe using long‐term satellite and reanalysis data. Significant WCs in aerosol and cloud droplet number concentration (Nd) are detected with minima/maxima on Monday/Friday, indicating a clear signal of the Twomey effect. Notably, Nd–to–aerosol sensitivity from WCs is found to decrease at larger aerosol concentrations, confirming the nonlinear behavior of the aerosol–Nd relation (in log–log space) reported previously, but from a distinct perspective. Nevertheless, no discernible WCs in liquid water path are found. The pronounced WCs in cloud cover are demonstrated to be driven by natural variability. Our results indicate that the WCs offer a useful pathway for investigating the Twomey effect, but are not as effective for detecting cloud adjustments. Plain Language Summary: Aerosol–cloud interactions are the largest uncertainty in the anthropogenic forcing of climate. Specifically, an increase in aerosols increases cloud droplet number concentration (the Twomey effect), which further changes liquid water path and cloud cover (cloud adjustments), ultimately alters radiations. Weekly cycles would be a useful tool for such study, assuming no 7‐day periodicity in meteorological dynamics. In this study, we revisit aerosol–cloud interactions from weekly cycles using long‐term satellite observations and reanalysis data. The analysis is restricted to central Europe—a region with strong weekly cycles in anthropogenic emissions. We find significant weekly cycles in aerosol and cloud droplet number concentration with minima/maxima on Monday/Friday. Importantly, the sensitivity of cloud droplet number concentration to aerosol is found to decrease in polluted conditions, confirming the behavior of the nonlinear cloud response to aerosol as reported previously. It is demonstrated that the weekly cycle in liquid water path is negligible; though a pronounced cycle in cloud cover detected, it is predominately caused by natural variability. Hence, caution is warranted when attributing observed weekly cycle in cloud cover to aerosol effects. Conclusively, weekly cycles are useful for detecting the Twomey effect but less effective for cloud adjustments. Key Points: The analysis of weekly cycles reveals a reduced sensitivity of cloud droplet number concentration to aerosol at larger aerosol loadingThe strong weekly cycle in cloud cover is mainly a result of natural variability rather than being attributed to aerosol effectsWeekly cycles offer a useful pathway to investigating the Twomey effect, but are not as effective for detecting cloud adjustments [ABSTRACT FROM AUTHOR]

As a typical information system, a cloud file system enables the storage, retrieval, and management of data on remote servers or server clusters. The reliable design of such systems is critical to ensure the security of data and availability of services. However, designing correct-by-construction systems is challenging due to the complexity of and concurrency inherent in cloud file systems. Further, existing works on cloud file system analysis often focus on specific systems or lack formal modeling and verification, leading to potential design flaws and security vulnerabilities. To address these issues, we propose MSCFS-RP, which is a formal analysis model based on colored Petri nets. Leveraging the strengths of colored Petri nets in representing diverse information types with colored tokens and defining explicit rules for concurrent interactions, our model captures the writing and reading processes of clients, meta servers, and clusters. With strong formalism and support for verification using CPN Tools, we rigorously evaluate key properties such as replication consistency under various scenarios. The results demonstrate that MSCFS-RP satisfies these properties, validating its effectiveness and trustworthiness in managing information within cloud storage systems. [ABSTRACT FROM AUTHOR]

In satellite imagery, clouds obstruct the ground information, directly impacting various downstream applications. Thus, cloud annotation/cloud detection serves as the initial preprocessing step in remote sensing image analysis. Recently, deep learning methods have significantly improved in the field of cloud detection, but training these methods necessitates abundant annotated data, which requires experts with professional domain knowledge. Moreover, the influx of remote sensing data from new satellites has further led to an increase in the cost of cloud annotation. To address the dependence on labeled datasets and professional domain knowledge, this paper proposes an automatic cloud annotation method for satellite remote sensing images, CloudAUE. Unlike traditional approaches, CloudAUE does not rely on labeled training datasets and can be operated by users without domain expertise. To handle the irregular shapes of clouds, CloudAUE firstly employs a convex hull algorithm for selecting cloud and non-cloud regions by polygons. When selecting convex hulls, the cloud region is first selected, and points at the edges of the cloud region are sequentially selected as polygon vertices to form a polygon that includes the cloud region. Then, the same selection is performed on non-cloud regions. Subsequently, the fast KD-Tree algorithm is used for pixel classification. Finally, an uncertainty method is proposed to evaluate the quality of annotation. When the confidence value of the image exceeds a preset threshold, the annotation process terminates and achieves satisfactory results. When the value falls below the threshold, the image needs to undergo a subsequent round of annotation. Through experiments on two labeled datasets, HRC and Landsat 8, CloudAUE demonstrates comparable or superior accuracy to deep learning algorithms, and requires only one to two annotations to obtain ideal results. An unlabeled self-built Google Earth dataset is utilized to validate the effectiveness and generalizability of CloudAUE. To show the extension capabilities in various fields, CloudAUE also achieves desirable results on a forest fire dataset. Finally, some suggestions are provided to improve annotation performance and reduce the number of annotations. [ABSTRACT FROM AUTHOR]

Function as a Service (FaaS) is highly beneficial to smart city infrastructure due to its flexibility, efficiency, and adaptability, specifically for integration in the digital landscape. FaaS has serverless setup, which means that an organization no longer has to worry about specific infrastructure management tasks; the developers can focus on how to deploy and create code efficiently. Since FaaS aligns well with the IoT, it easily integrates with IoT devices, thereby making it possible to perform event-based actions and real-time computations. In our research, we offer an exclusive likelihood-based model of adaptive machine learning for identifying the right place of function. We employ the XGBoost regressor to estimate the execution time for each function and utilize the decision tree regressor to predict network latency. By encompassing factors like network delay, arrival computation, and emphasis on resources, the machine learning model eases the selection process of a placement. In replication, we use Docker containers, focusing on serverless node type, serverless node variety, function location, deadlines, and edge-cloud topology. Thus, the primary objectives are to address deadlines and enhance the use of any resource, and from this, we can see that effective utilization of resources leads to enhanced deadline compliance. [ABSTRACT FROM AUTHOR]

In this research, we pioneer a novel method to evaluate the maximum admissible load (MAL) for virtual machines (VMs) in physical machines (PMs) in data centre networks (DCNs), without restricting DCN topologies. This unique approach simplifies the issue into a single-source, multiple-sink maximum flow problem. It also resolves the maximum elastic scheduling problem by determining the optimal load for consistent growth without reassigning tasks. An effective strategy for these challenges is introduced and validated through extensive simulations. [ABSTRACT FROM AUTHOR]

Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmental monitoring. Addressing the limitations of conventional convolutional neural networks, we propose an innovative transformer-based method. This method leverages transformers, which are adept at processing data sequences, to enhance cloud detection accuracy. Additionally, we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction, thereby aiding in the retention of critical details often lost during cloud detection. Our extensive experimental validation shows that our approach significantly outperforms established models, excelling in high-resolution feature extraction and precise cloud segmentation. By integrating Positional Visual Transformers (PVT) with this architecture, our method advances high-resolution feature delineation and segmentation accuracy. Ultimately, our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains. [ABSTRACT FROM AUTHOR]

This article explores the struggle for 'digital sovereignty' in the European Union (EU). A seeming contradiction – the internet, after all, spans the globe – digital sovereignty is portrayed as the winning geoeconomic formula to keep the EU secure, competitive and democratic in the digital future. Approaching digital sovereignty as a discursive claim and analysing it through a case study of the European cloud project Gaia‐X, we show that there is no singular understanding of digital sovereignty in the EU. Instead, we identify six different conceptions across the domains of security, economy and rights. This article outlines three scenarios for how the digital sovereignty agenda may develop and thus shape the EU's digital policy and its relations with the rest of the world: constitutional tolerance (where the conceptions co‐exist), hegemony (where one conception dominates) or collapse (where the agenda falls apart due to inbuilt conceptual contradictions). [ABSTRACT FROM AUTHOR]

In order to investigate the variations of cloud and aerosol vertical profiles over the Tibetan Plateau (TP) in winter, we performed ground-based lidar observations in Lhasa, a city on the TP, from November 2021 to January 2022. The profiles of extinction coefficient, depolarization ratio, and signal-to-noise ratio (SNR) were retrieved using the atmospheric echo signals collected by the lidar. Clouds were identified by the range-correction echo signals and classified into water clouds, mixed clouds, horizontally oriented ice crystal clouds (HOICC), and ice clouds by the depolarization ratio and the hourly temperature from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5). The clouds mainly appeared at a height of 3~5 km from 14:00–22:00 Beijing Time throughout the field campaign. The height and frequency (~30%) for cloud appearance were significantly lower than that reported in previous studies in summer. The cloud categories were dominated by mixed clouds and ice clouds during the observation period. The proportions of ice clouds gradually increased with increasing heights. After eliminating profiles influenced by clouds, the aerosol extinction coefficient and depolarization ratio were obtained, and the atmospheric boundary layer height (ABLH) was calculated. The aerosol extinction coefficient decreased with increasing height in the ABLH, and there were no obvious changes for the aerosol extinction coefficient above the ABL. The aerosol extinction coefficients near the Earth's surface presented two peaks, appearing in the morning and evening, respectively. The high aerosols at the surface in the morning continually spread upward for 4–5 h and finally reached an altitude of 1 km with the development of ABLH. In addition, the depolarization ratio of aerosols decreased slowly with increasing altitudes. There was no obvious diurnal variation for depolarization ratios, indicating partly that the source of aerosols did not change significantly. These results are beneficial in understanding the evolution of cloud and aerosol vertical profiles over the TP. [ABSTRACT FROM AUTHOR]

Cloud organization impacts the radiative effects and precipitation patterns of the cloud field. Deviating from randomness, clouds exhibit either clustering or a regular grid structure, characterized by the spacing between clouds and the cloud size distribution. The two measures are coupled but do not fully define each other. Here, we present the deviation from randomness of the cloud‐ and void‐chord length distributions as a measure for both factors. We introduce the LvL representation and an associated 2D score that allow for unambiguously quantifying departure from well‐defined baseline randomness in cloud spacing and sizes. This approach demonstrates sensitivity and robustness in classifying cloud field organization types. Its delicate sensitivity unravels the temporal evolution of a single cloud field, providing novel insights into the underlying governing processes. Plain Language Summary: The spatial arrangement of clouds within a cloud field plays a significant role in influencing the field's radiative effects, cloud properties, and precipitation patterns. Thus, cloud organization, determined by both cloud spacing and sizes, is a critical aspect to consider. Here, we introduce a novel 2D method, termed the LvL space, that assesses cloud field organization by integrating these key factors. This approach utilizes a unified mathematical metric and reference standard to measure both cloud spacing and cloud sizes. Its sensitivity and robustness are evident in accurately classifying different types of cloud field organization and tracking their evolution over time. Such a metric captures intricate dynamic processes and contributes to a deeper understanding of cloud pattern formation and evolution. Through better quantifying cloud organization, the LvL can potentially enhance our understanding of cloud feedback mechanisms. Key Points: Cloud field organization is expressed as a tendency toward regularity or clusteringThe cloud‐chord Length versus void‐chord Length (LvL) space is introduced as a 2D cloud field organization metricThe LvL representation measures organization in terms of spacing between clouds and the cloud size distribution [ABSTRACT FROM AUTHOR]

Mass loss of the Greenland Ice Sheet (GrIS) plays a major role in the global sea level rise. The west coast of the GrIS has contributed 1,000 Gt of the 4,488 Gt GrIS mass loss between 2002 and 2021, making it a hotspot for GrIS mass loss. Surface melting is driven by changes in the radiative budget at the surface, which are modulated by clouds. Previous works have shown the impact of North Atlantic transport for influencing cloudiness over the GrIS. Here we used space‐based lidar cloud profile observations to show that a polar low circulation promotes the presence of low clouds over the GrIS west coast that warm radiatively the GrIS surface during the melt season. Polar low circulation transports moisture and low clouds from the sea to the west of Greenland up over the GrIS west coast through the melt season. The concomitance of the increasing presence of low cloud in fall over the Baffin Sea due to seasonal sea‐ice retreat and a maximum occurrence of Polar low circulation in September results in a maximum of low cloud fraction (∼14% at 2.5 km above sea level) over the GrIS west coast in September. These low clouds warm radiatively the GrIS west coast surface up to 80 W/m2 locally. This warming contributes to an average increase of 10 W/m2 of cloud surface warming in September compared to July on the GrIS west coast. Overall, this study suggests that regional atmospheric processes independent from North Atlantic transport may also influence the GrIS melt. Plain Language Summary: The melting of Greenland plays a major role in the global sea level rise. The west coast of Greenland has contributed to 22% of Greenland mass loss between 2002 and 2021, making it a hotspot for Greenland mass loss. Clouds modulate the energy that reaches the surface of Greenland and, therefore, might accelerate or decelerate surface melt. To explain what leads to cloud presence over Greenland, previous studies focused on the connection between the mid‐latitudes and the Greenland cloud cover. Using a combination of satellite cloud observation and models, we highlight that a low‐pressure system over the Arctic brings moisture and low clouds from the sea west of Greenland to the Greenland west coast. Then, we assess the strong surface warming effect of these low clouds. Overall, our study demonstrates that local Arctic processes lead to formation of low clouds where Greenland melts. Key Points: The Greenland Ice Sheet (GrIS) west coast by itself represents 22% of the GrIS mass loss over the last two decadesA polar low promotes the presence of low clouds over the GrIS west coast during the melt seasonThe cloud surface radiative warming on the Greenland west coast is +10 W/m2 larger in September than July due to these low clouds [ABSTRACT FROM AUTHOR]

Pollen can serve as an effective ice‐nuclei (IN), altering cloud microphysical and radiative properties, thus precipitation and cloud life cycles. Here, a nationwide pollen emission inventory with a horizontal resolution of 5 km was established based on a parameterization scheme of mass balance of pollen grain fluxes surrounding the plant crowns, and using satellite observational data sets (including leaf area index and fractional vegetation cover) as well as pollen emission rates. The potential emission is then implemented in RegCM‐pollen model which treated pollen as aerosol tracers. Besides, pollen‐IN parameterization schemes were incorporated in RegCM‐pollen to simulate the interactions between pollen and ice clouds. Investigations show that the mean annual pollen emission in China is 2.65 × 107 grains m−2 yr−1, mainly distributed in the south and northeast of China. The IN magnitude is mainly determined by a combination of temperature and pollen concentration. Notably, an increasing number concentration of pollen grains produces opposite effects in Southern China (SC) and Northern China (NC). The weakened upward motion and vertical transport of water vapor in NC made ice clouds hardly form, resulting in cloud forcing (CF) of +0.86 W/m2. In contrast, it generates a CF of −0.84 W/m2 in SC mainly owing to expanded cloud cover. The changes in shortwave radiative forcing is more significant compared to longwave radiative forcing in the two regions. At the surface, the net radiative forcing in NC is +0.74 W/m2, while it is a −0.51 W/m2 in SC. Among them, downward shortwave radiative forcing is approximately twice that of upward longwave radiative forcing in SC and 1.4 times in NC. Surface temperature shows rising over NC, ranging from 0.05 to 0.25 K. In SC, it is primarily decreasing by −0.12 to −0.03 K. The pollen‐IN effect also causes a decline of precipitation in NC (−0.17 mm/day) and a rise in SC (0.09 mm/day). Our results suggest that the pollen effect on ice clouds is complex, yet significant in understanding its impact on radiation and climate of the atmosphere. Plain Language Summary: Bioaerosols such as pollen are emitted into the atmosphere from the terrestrial biosphere. They can affect cloud formation as ice nuclei, thus influencing radiation and climate. Whereas, up to now little is understood about the ice‐nucleating properties of pollen and how they impact cloud and precipitation formation. In this work, the RegCM‐pollen was utilized to quantitatively estimate vegetation pollen emissions in China and their induced radiative forcing and climate responses. We found that higher airborne pollen is chiefly concentrated in Southern China (SC) and Northern China (NC). The ice nucleation effect of pollen generates contrasting effects in the two areas. A positive effect on cloud forcing (CF) in NC is mainly owing to the inhibition of upward motion and reduced vertical transport of water vapor not conducive to the formation of ice clouds. On the opposite, the negative effect on CF in SC is mostly influenced by the enhanced water vapor, thus, expanding the coverage of the ice cloud. Key Points: Model simulations show that airborne pollen is mainly concentrated in Southern China (SC) and Northern China (NC) with seasonal variationsPollen‐induced IN concentrations decrease with altitude, exhibiting large gradient at a given height, particularly above 7 kmOpposite cloud forcing caused by pollen ice nuclei effect was found in SC (−0.84 W/m2) and NC (+0.86 W/m2) mainly due to water vapor difference in these two regions [ABSTRACT FROM AUTHOR]

Fault tolerance is crucial in ensuring smooth working of distributed and cloud computing. It is challenging to implement because of the constantly changing infrastructure and complex configurations in distributed and cloud computing. Implementation of various fault tolerance methods require domain‐specific knowledge as well as in‐depth understanding of the existing techniques and approaches. Recent surveys on fault tolerance in cloud and distributed environments exist, but they have limitations. This article systematically reviews fault tolerance approaches in distributed and cloud computing and discusses their taxonomy. Based on the taxonomy provided, fault‐tolerance approaches are divided into four types, that is, reactive approaches, proactive approaches, adaptive approaches, and hybrid approaches. Reactive approaches provide a preventive measure after the occurrence of faults in the system. Proactive approaches prevent the system or minimize failure effects by predicting in advance. The adaptive approaches predict, learn, and adapt the changes to deal with new faults in the system. The hybrid approaches combine reactive, proactive, and adaptive approaches. The objective of this article is to give a better understanding of handling faults using suitable approaches and further compare them on various parameters. The paper also presents a promising research direction based on the challenges and issues in multiple approaches. [ABSTRACT FROM AUTHOR]

This paper explores the design and implementation of a cognitive assistant powered by cloud computing. We begin by reviewing existing cloud service providers and assistant solutions. Building on this foundation, we define a set of requirements for our cognitive assistant and utilize them to guide the development process. To validate the functionality of our creation, we compare its outputs with established reference architectures. [ABSTRACT FROM AUTHOR]

Computer systems, cloud networks, and information systems can all be attacked, but intrusion detection systems can find them. It is challenging to demonstrate the security of the information systems and to uphold such security when they are in use. We put forth the Staked Deep Ensemble Model for Intrusion Detection and Classification (SDEIC) as a solution to these issues. The three stages of feature extraction, optimal feature selection, and classification are used to implement this model. Data normalisation, feature conversion, and feature reduction are all included in the feature extraction phase. A better data normalisation procedure in particular normalises the supplied data. After that, feature conversion is applied to the normalised data. The feature reduction process is then subjected to the Principle Component Analysis (PCA) method. The reduced feature set is then used to select the best features, and this study suggests the Self-Upgraded Squirrel Search Optimisation (SUSSO) algorithm for doing so. In order to classify the specified features, the ensemble model—which consists of the models RNN (Recurrent Neural Network), Improved Deep Belief Network (IDBN), and Deep Max-out Network—is introduced. Additionally, the suggested SUSSO algorithm optimises the weights of the Deep Max-out model during training to guarantee accurate classification. The projected model's accuracy is 97.5%, which is 12.5%, 2.3%, 41.9%, 1.6%, 18.8% and 15.8% higher than the previous models like NN, RNN, DBN, GRU, and SVM. Lastly, the effectiveness of the suggested approach outperforms the conventional methods. and has obtained satisfactory results. [ABSTRACT FROM AUTHOR]

The artificial intelligence (AI) industry is increasingly integrating with diverse sectors such as smart logistics, FinTech, entertainment, and cloud computing. This expansion has led to the coexistence of heterogeneous applications within multi-tenant systems, presenting significant scheduling challenges. This paper addresses these challenges by exploring the scheduling of various machine learning workloads in large-scale, multi-tenant cloud systems that utilize heterogeneous GPUs. Traditional scheduling strategies often struggle to achieve satisfactory results due to low GPU utilization in these complex environments. To address this issue, we propose a novel scheduling approach that employs a genetic optimization technique, implemented within a process-oriented discrete-event simulation framework, to effectively orchestrate various machine learning tasks. We evaluate our approach using workload traces from Alibaba's MLaaS cluster with over 6000 heterogeneous GPUs. The results show that our scheduling improves GPU utilization by 12.8% compared to Round-Robin scheduling, demonstrating the effectiveness of the solution in optimizing cloud-based GPU scheduling. [ABSTRACT FROM AUTHOR]

The management of large medical files poses a critical challenge in the health sector, with conventional systems facing deficiencies in security, scalability, and efficiency. Blockchain ensures the immutability and traceability of medical records, while the cloud allows scalable and efficient storage. Together, they can transform the data management of electronic health record applications. The method used was the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to choose and select the relevant studies that contribute to this research, with special emphasis set on maintaining the integrity and security of the blockchain while tackling the potential and efficiency of cloud infrastructures. The study's focus is to provide a comprehensive and insightful examination of the modern landscape concerning the integration of blockchain and cloud advances, highlighting the current challenges and building a solid foundation for future development. Furthermore, it is very important to increase the integration of blockchain security with the dynamic potential of cloud computing while guaranteeing information integrity and security remain uncompromised. In conclusion, this paper serves as an important resource for analysts, specialists, and partners looking to delve into and develop the integration of blockchain and cloud innovations. [ABSTRACT FROM AUTHOR]

This paper analyzes the timing performance of a persistent storage designed for distributed container-based architectures in industrial control applications. The timing performance analysis is conducted using an in-house simulator, which mirrors our testbed specifications. The storage ensures data availability and consistency even in presence of faults. The analysis considers four aspects: 1. placement strategy, 2. design options, 3. data size, and 4. evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet critical deadlines, particularly under specific failure patterns. Comparison results also reveal that, while the method may underperform current centralized solutions in fault-free conditions, it outperforms the centralized solutions in failure scenario. Moreover, the used evaluation method is applicable for assessing other container-based critical applications with timing constraints that require persistent storage. [ABSTRACT FROM AUTHOR]

Load balancing plays a critical role in optimizing resource utilization and enhancing performance in cloud computing systems. As cloud environments grow in scale and complexity, efficient load balancing mechanisms become increasingly vital. This paper presents a comprehensive review of load balancing techniques in cloud computing systems, with a focus on their applicability, advantages, and limitations. The review encompasses both static and dynamic load balancing approaches, evaluating their effectiveness in addressing the challenges posed by cloud infrastructure, such as heterogeneity, scalability, and variability in workload demands. Furthermore, the review examines load balancing algorithms considering factors such as resource utilization, response time, fault tolerance, and energy efficiency. Additionally, the impact of load balancing on cloud performance metrics, including throughput, latency, and scalability, is analyzed. This review aims to provide insights into the state-of-the-art load balancing strategies and serve as a valuable resource for researchers, practitioners, and system designers involved in the development and optimization of cloud computing systems. [ABSTRACT FROM AUTHOR]

Recently, indoor systems for growing plants have emerged as a promising approach to address the problems related to extreme weather conditions outdoors. However, such systems must manage the plants surrounding environments to satisfy the environmental and the economical requirements. In this line, any proposed solution must address challenging factors such as plants diseases and unordinary climate situations. In this paper, propose an internet of things (IoT) indoor system that can be used to facilitate the plant growing process. The proposed system is designed to provide alternatives for outdoor climate dependency such as the vitamins provided through sunlight. Moreover, renewable energy sources (sunlight) are employed to reduce the impact on the environment. With the help of several types of sensors, the system continuously monitors the plants through their growing journey. Whereas actuator devices are employed to control the plant-feeding process based on the sensors' reported values. All the collected data will be uploaded to the cloud for analysis, utilizing a website. Additionally, the architecture of the provided system eliminates the need for human involvement, which has a degrading effect on the plant growing process. [ABSTRACT FROM AUTHOR]

This paper explores the design and implementation of a cognitive assistant powered by cloud computing. We begin by reviewing existing cloud service providers and assistant solutions. Building on this foundation, we define a set of requirements for our cognitive assistant and utilize them to guide the development process. To validate the functionality of our creation, we compare its outputs with established reference architectures.

This study provides empirical relationships between photosynthetically active radiation (PAR) and broadband clear‑sky indices at ground level for both the PAR global irradiance and its direct component. Once multiplied by the irradiance in clear‑sky conditions, the clear‑sky index provides the irradiance under cloudy conditions. The relationships are developed by the means of radiative transfer simulations of various realistic atmospheric states including both ice and water cloud phases. For the direct component, the PAR clear‑sky index is equal to the broadband clear‑sky index. For global irradiance, several linear relationships are proposed depending on the availability of cloud properties namely cloud phase and cloud optical depth. The developed relationships are validated numerically and experimentally by using ground-based measurements from the SURFRAD network in the U.S.A. Overall, it has been found, a squared correlation coefficient R2$R^{2}$ close to 1.00 and a relative bias (relative root mean square error) in absolute value less than 3 % (6 %) with respect to the means of the relevant measurements, demonstrating a high level of accuracy of the proposed relationships.