Scale Cloud Research Articles

Boosting point cloud understanding through graph convolutional network with scale measurement and high-frequency enhancement

Graph-based methods have exhibited exceptional performance in point cloud understanding by capturing local geometric relationships. However, existing approaches often struggle to characterize the overall spatial scale of local graphs. In addition, they fail to capture the differences between nodes effectively, which is crucial for distinguishing different classes. This study introduces SM-HFEGCN, a novel graph convolutional network that addresses these limitations through two key innovations: scale measurement and high-frequency enhancement. First, we introduce a spatial scale feature derived from the diagonal vectors of the neighborhood, which serves as a unique graph-specific property related to the geometry and density of the local point cloud. This feature can characterize the overall spatial scale of the local point cloud. Second, we enhance the high-frequency information to capture node variations and integrate it with smoothed information to represent the differences and similarities between nodes simultaneously. Extensive experiments demonstrate the effectiveness of SM-HFEGCN in point cloud classification and segmentation tasks.

The SDSS-V Local Volume Mapper (LVM): Scientific Motivation and Project Overview

We present the Sloan Digital Sky Survey V Local Volume Mapper (LVM). The LVM is an integral-field spectroscopic survey of the Milky Way, Magellanic Clouds, and a sample of local volume galaxies, connecting resolved parsec-scale individual sources of feedback to kiloparsec-scale ionized interstellar medium (ISM) properties. The 4 yr survey covers the southern Milky Way disk at spatial resolutions of 0.05–1 pc, the Magellanic Clouds at 10 pc resolution, and nearby large galaxies at larger scales totaling >4300 deg2 of sky and more than 55M spectra. It utilizes a new facility of alt–alt mounted siderostats feeding 16 cm refractive telescopes, lenslet-coupled fiber optics, and spectrographs covering 3600–9800 Å at R ∼ 4000. The ultra-wide-field integral-field unit has a diameter of 0.°5 with 1801 hexagonally packed fibers of 35.″3 apertures. The siderostats allow for a completely stationary fiber system, avoiding instability of the line-spread function seen in traditional fiber feeds. Scientifically, LVM resolves the regions where energy, momentum, and chemical elements are injected into the ISM at the scale of gas clouds, while simultaneously charting where energy is being dissipated (via cooling, shocks, turbulence, bulk flows, etc.) to global scales. This combined local and global view enables us to constrain physical processes regulating how stellar feedback operates and couples to galactic kinematics and disk-scale structures, such as the bar and spiral arms, as well as gas in- and outflows.

AMP: Total Variation Reduction for Lossless Compression via Approximate Median-based Preconditioning

With the increasing scale of cloud computing applications of next-generation embedded systems, a major challenge that domain scientists are facing is how to efficiently store and analyze the vast volume of output data. Compression can reduce the amount of data that needs to be transferred and stored. However, most of the large datasets are in floating-point format, which exhibits high entropy. As a result, existing lossless compressors cannot provide enough performance for such applications. To address this problem, we propose a total variation reduction method for improving the compression ratio of lossless compressors (namely, FPC + and FPZIP + ), which employs a median-based hyperplane to precondition the data. In particular, we first try to exploit the space-filling curve (SFC), a well-known technique to preserve data locality for a multi-dimensional dataset. We show and explain why a raw SFC, such as Hilbert and Z-order curves, cannot improve the compression ratio. Then, we explore the opportunity and theoretical feasibility of the proposed total variation reduction-based algorithm. The experiment results show the effectiveness of the proposed method. The compression ratios are improved up to 48.2% (20.6% on average) for FPZIP and 42.4% (18.4% on average) for FPC. Moreover, through observing the time composition of the proposed method, it is found that the median finding holds a high percentage of the execution time. Hence, we further introduce an approximate median finding algorithm, providing a linear-time overhead reduction scheme. The experiment results clearly demonstrate that this algorithm reduces execution time by an average of 56.7% and 40.7% compared to FPC + and FPZIP + , respectively.

Studying the Impacts of Meteorological Factors on Distribution of Cloud Horizontal Scales Based on Active Satellite

AbstractAs a significant macrophysical property, cloud horizontal scales play a role in cloud radiation, precipitation and vertical cloud overlap. Until now, however, the mechanisms behind the variations in cloud scale distribution have received far less attention. This study utilizes active satellite data from 2007 to 2016 to investigate the spatiotemporal distribution of cloud horizontal scales, and explains the variations through two meteorological factors: wind shear and atmospheric stability. Cloud scales exhibit a distinct power‐law behavior when scale break is not considered, and the power‐law exponent β is a characteristic measure of cloud scale distribution. A smaller power‐law exponent β indicates a higher frequency of large clouds. During boreal summer season, the amount of large clouds is extremely large south of the 40°S but rather small between 10°S and 20°S. As wind shear decreases or atmospheric stability increases, more large clouds occur globally. The underlying mechanisms might be associated with cloud entrainment which can be promoted by wind shear but inhibited by atmospheric stability. However, our analysis of the impacts of these two factors on cloud scale distribution across different regions and heights reveals that both wind shear and atmospheric stability play dual roles on the values of the exponent β. The potential physical mechanisms, including the effects of precipitation, are further discussed. It is observed that precipitation also exerts a dual impact on the values of the exponent β. These findings underscore the significance of considering the impacts of meteorological factors on cloud scale distribution in numerical weather prediction models.

Playing with FIRE: A Galactic Feedback-halting Experiment Challenges Star Formation Rate Theories

Stellar feedback influences the star formation rate (SFR) and the interstellar medium of galaxies in ways that are difficult to quantify numerically, because feedback is an essential ingredient of realistic simulations. To overcome this, we conduct a feedback-halting experiment starting with a Milky Way–mass galaxy in the second-generation Feedback In Realistic Environments (FIRE-2) simulation framework. By terminating feedback, and comparing to a simulation in which feedback is maintained, we monitor how the runs diverge. We find that without feedback, the interstellar turbulent velocities decay. There is a marked increase of dense material, while the SFR increases by over an order of magnitude. Importantly, this SFR boost is a factor of ∼15–20 larger than is accounted for by the increased freefall rate caused by higher densities. This implies that feedback moderates the star formation efficiency per freefall time more directly than simply through the density distribution. To probe changes at the scale of giant molecular clouds (GMCs), we identify GMCs using density and virial parameter thresholds, tracking clouds as the galaxy evolves. Halting feedback stimulates rapid changes, including a proliferation of new bound clouds, a decrease of turbulent support in loosely bound clouds, an overall increase in cloud densities, and a surge of internal star formation. Computing the cloud-integrated SFR using several theories of turbulence regulation, we show that these theories underpredict the surge in SFR by at least a factor of 3. We conclude that galactic star formation is essentially feedback regulated on scales that include GMCs, and that stellar feedback affects GMCs in multiple ways.

Magnetic Fields in Ministarburst Complex Sgr B2

We report the first arcsecond-resolution observations of the magnetic field in the ministarburst complex Sgr B2. SMA polarization observations revealed magnetic field morphology in three dense cores of Sgr B2 N(orth), M(ain), and S(outh). The total plane-of-sky magnetic field strengths in these cores are estimated to be 4.3–10.0 mG, 6.2–14.7 mG, and 1.9–4.5 mG derived from the angular dispersion function method after applying the correction factors of 0.21 and 0.5. Combining with analyses of the parsec-scale polarization data from Stratospheric Observatory for Infrared Astronomy, we found that a magnetically supercritical condition is present from the cloud scale (∼10 pc) to core scale (∼0.2 pc) in Sgr B2, which is consistent with the burst of star formation activities in the region likely resulting from a multiscale gravitational collapse from the cloud to dense cores.

Unraveling the Mystery of the Low CO-to-H2 Conversion Factor in Starburst Galaxies: RADEX Modeling of the Antennae

CO emission has been widely used as a tracer of molecular gas mass. However, it is a long-standing issue to accurately constrain the CO-to-H2 conversion factor (α CO) that converts CO luminosity to molecular gas mass, especially in starburst galaxies. We present the first resolved α CO modeling results with multiple Atacama Large Millimeter/submillimeter Array CO and 13CO transition observations at both giant molecular cloud (GMC) scale at 150 pc and kiloparsec scale for one of the closest starburst mergers, the Antennae. By combining our CO modeling results and measurements of 350 GHz dust continuum, we find that most GMCs in the Antennae have α CO values approximately four times smaller than the commonly adopted Milky Way value (4.3). We find that α CO at GMC scales shows a strong dependence on CO intensity, 13CO/CO ratio, and GMC velocity dispersion, which is consistent with various theoretical and simulation predictions. Specifically, we suggest that the 13CO/CO line ratio and the velocity dispersion can be used to infer α CO in starburst regions. By applying our modeled α CO in GMC analyses, we find that GMCs in the Antennae are less gravitationally bound than in normal spiral galaxies, which is more consistent with what is predicted by merger simulations. At kiloparsec scale, we find that our modeled α CO values are smaller than the modeled α CO at GMC scale by 40%, which can be due to inclusion of a diffuse gas component with lower α CO values. We find a similar correlation of α CO and CO intensity at kiloparsec scales to that at GMC scales.

A Temperature or Far-ultraviolet Tracer? The HNC/HCN Ratio in M83 on the Scale of Giant Molecular Clouds

The HNC/HCN ratio is observationally known as a thermometer in Galactic interstellar molecular clouds. A recent study has alternatively suggested that the HNC/HCN ratio is affected by the ultraviolet (UV) field, not by the temperature. We aim to study this ratio on the scale of giant molecular clouds in the barred spiral galaxy M83 towards the southwestern bar end and the central region from Atacama Large Millimeter/submillimeter Array observations, and if possible, distinguish the above scenarios. We compare the high-resolution (40–50 pc) HNC/HCN ratios with the star formation rate from the 3 mm continuum intensity and the molecular mass inferred from the HCN intensities. Our results show that the HNC/HCN ratios do not vary with the star formation rates, star formation efficiencies, or column densities in the bar-end region. In the central region, the HNC/HCN ratios become higher with higher star formation rates, which tend to cause higher temperatures. This result is not consistent with the previously proposed scenario in which the HNC/HCN ratio decreases with increasing temperature. Spectral shapes suggest that this trend may be due to optically thick HCN and optically thin HNC. In addition, we compare the large-scale (∼200 pc) correlation between the dust temperature from the far-IR ratio and the HNC/HCN ratio for the southwestern bar-end region. The HNC/HCN ratio is lower when the dust temperatures are higher. We suggest from the above results that the HNC/HCN ratio depends on the UV radiation field that affects the interstellar medium on the ∼100 pc scale where the column densities are low.

Utilizing Python for Scalable Data Processing in Cloud Environments

In the age of big data and cloud computing, enterprises need to effectively analyze enormous datasets to get meaningful insights and stay ahead. Python, a popular programming language, is a strong tool for cloud-scale data processing. This research study examines Python's integration with cloud platforms and its effects on performance and efficiency in scalable data processing. The study introduces scalable data processing and cloud computing. It then discusses Python's ecosystem, including Dask, Apache Spark with PySpark, TensorFlow, and PyTorch for data processing and machine learning. The study also examines Python's interoperability with cloud services like AWS, Google Cloud Platform, and Microsoft Azure in data input, transformation, and analysis. Many case studies and real-world applications demonstrate how Python has been used in banking, healthcare, and e-commerce. Python is useful for managing massive amounts of data, streamlining processing processes, and scaling cloud applications, as shown in the case studies. The report also analyzes Python-based cloud systems' performance indicators and cost consequences, revealing best practices and possible issues. The article explores Python's involvement in cloud computing trends and technology. Serverless architectures, Docker and Kubernetes, and Python interaction with cloud-native tools and services are examples. These patterns show how data processing is changing and how Python is improving to meet current data needs. This study concludes that Python is a reliable and scalable cloud data processing option. The language's strengths, alignment with cloud technologies, and practical applications in many areas are covered in detail. The results indicate that Python's versatility and cloud scalability provide a robust foundation for handling and analyzing massive datasets, enabling better decision-making and innovation across fields.

Magnetic fields in star-forming environments: how does field strength affect gas on spiral arm and cloud scales?

ABSTRACT We investigate star formation from subpc to kpc scales with magnetohydrodynamic models of a cloud structure and a section of galactic spiral arm. We aim to understand how magnetic fields affect star formation and cloud formation, and how feedback couples with magnetic fields on scales of clouds and clumps. We find that magnetic fields overall suppress star formation by ${\sim}$10 per cent with a weak field (5 $\mu$G) and ${\sim} 50$ per cent with a stronger field (50 $\mu$G). Cluster masses are reduced by about 40 per cent with a strong field but show little change with a weak field. We find that clouds tend to be aligned parallel to the field with a weak field and become perpendicularly aligned with a stronger field, whereas on clump scales the alignment is more random. The magnetic fields and densities of clouds and clumps in our models agree with the Zeeman measurements of the Crutcher relation $B\!-\!\rho$ in the weaker field models, while the strongest field models show a relation that is too flat compared to the observations. In all our models, we find that both subcritical and supercritical clouds and clumps are present. We also find that if using a line-of-sight (1D) measure of the magnetic field to determine the critical parameter, the magnetic field, and thereby also criticality, can vary by a factor of 3–4 depending on whether the direction the field is measured along corresponds to the direction of the ordered component of the magnetic field.

Monitoring of a rockfill embankment dam using TLS and sUAS point clouds

Abstract Terrestrial laser scanning (TLS) and camera-equipped small unmanned aircraft systems (sUAS) are two methods that are often used to produce dense point clouds for several monitoring applications. This paper compares the two methods in their ability to provide accurate monitoring information for rockfill embankment dams. We compare the two methods in terms of their uncertainty, data completeness, and field data acquisition/processing challenges. For both datasets, we derive an error budget that considers registration and measurement uncertainty. We also proceed to merge the TLS and sUAS data and leverage the advantages of each method. Furthermore, we conduct an analysis of the multiscale model-to-model cloud comparison (M3C2) input parameters, namely projection scale, normal scale, and sub-sampling of the reference point cloud, to show their effect on the M3C2 distance estimation. The theoretical methodologies and practical considerations of this paper can assist surveyors, who conduct monitoring of rockfill embankment dams using point clouds, in establishing reliable change/deformation estimations.

Improved optimization algorithm for resource management in cloud applications with performance monitor of VM provisioning, placement and recycling

The machine learning technique has been used to increase cloud management’s intelligence. Effective resource provisioning also preserves the environment. Manual cloud management has some difficult problems, such as complexity in cloud systems and scale issues. Hence, this paper introduces a new task for managing the resources in the cloud using deep learning. The aim is to predict the overall workload and server status prediction to the cloud resource management. Initially, performance monitoring is performed to keep aware of the performance of the application and guarantee the cloud application’s performance. In the suggested work, the required data is collected for the resource utilization on multiple Virtual Machine (VM) metrics. The VM provisioning is performed next to rectify the issues of resource provisioning. After that, the workload and server status prediction is conducted, where the Weighted Recurrent Neural Network (W-RNN) is adopted. After attaining the predicted workload, the VM placement module is carried out. Here, the virtual resource’s quantity is attained. Moreover, the multi-objective functions like resource utilization; cost, energy, time, and Quality of Service (QoS) are derived in this phase with the help of the Improved Rain Optimization Algorithm (IROA). Subsequently, the VM recycling is performed in the suggested work. Here, a resource collector is given for the virtual resources recycling task. It scans the applications of the cloud in the data centre and processes the VM recycling for every application. While considering the statistical analysis of the IROA-W-RNN-based resource management system achieved a mean of 56.27% than JAYA-W-RNN, 21.09% than SCO-W-RNN, 60.2% than MFOA-W-RNN, and 16.74% than DA-W-RNN for configuration 4. Finally, the numerical analysis is conducted to validate the presented resource management task with the aid of various conventional tasks.

Application of machine learning optimization in cloud computing resource scheduling and management

In recent years, cloud computing has been widely used. Cloud computing refers to the centralized computing resources, users through the access to the centralized resources to complete the calculation, the cloud computing center will return the results of the program processing to the user. Cloud computing is not only for individual users, but also for enterprise users. By purchasing a cloud server, users do not have to buy a large number of computers, saving computing costs. According to a report by China Economic News Network, the scale of cloud computing in China has reached 209.1 billion yuan.Rational allocation of resources plays a crucial role in cloud computing. In the resource allocation of cloud computing, the cloud computing center has limited cloud resources, and users arrive in sequence. Each user requests the cloud computing center to use a certain number of cloud resources at a specific time.

Relative Velocities between 13CO Structures within 12CO Molecular Clouds

Velocity fields of molecular clouds (MCs) can provide crucial information on the merger and split between clouds, as well as their internal kinematics and maintenance, energy injection and redistribution, and even star formation within clouds. Using the CO spectral lines data from the Milky Way Imaging Scroll Painting survey, we measure the relative velocities along the line of sight (ΔV LOS) between 13CO structures within 12CO MCs. Emphasizing MCs with double and triple 13CO structures, we find that approximately 70% of ΔV LOS values are less than ∼1 km s−1, and roughly 10% of values exceed 2 km s−1, with a maximum of ∼5 km s−1. Additionally, we compare ΔV LOS with the internal velocity dispersion of 13CO structures ( σ13CO,in ) and find that about 40% of samples in either double or triple regime display distinct velocity discontinuities, i.e., the relative velocities between 13CO structures are larger than the internal line widths of 13CO structures. Among these 40% samples in the triple regime, 33% exhibit signatures of combinations through the two-body motion, whereas the remaining 7% show features of configurations through the multiple-body motion. The ΔV LOS distributions for MCs with double and triple 13CO structures are similar, as well as their ΔV LOS/ σ13CO,in distributions. This suggests that relative motions of 13CO structures within MCs are random and independent of cloud complexities and scales.

Clumpy structures within the turbulent primordial cloud

ABSTRACT The primordial clouds in the mini-haloes hatch the first generation stars of the Universe, which play a crucial role in cosmic evolution. In this paper, we investigate how turbulence impacts the structure of primordial star-forming clouds. Previous cosmological simulations of the first star formation predicted a typical mass of around $\mathrm{ 100 \, M_\odot }$. This conflicts with recent observations of extremely metal-poor stars, suggesting a lower mass scale of about $\mathrm{25 \, M_\odot }$. The discrepancy may arise from unresolved turbulence in the star-forming cloud, driven by primordial gas accretion during mini-halo formation in the previous simulations. To quantitatively examine the turbulence effect on the primordial cloud formation, we employ the adaptive mesh refinement code Enzo to model the gas cloud with primordial composition, including artificially driven turbulence on the cloud scale and relevant gas physics. This artificially driven turbulence utilizes a stochastic forcing model to mimic the unresolved turbulence inside mini-haloes. Our results show that the turbulence with high Mach number and compressional mode effectively fragments the cloud into several clumps, each with dense cores of $\mathrm{22.7 - 174.9 \, M_\odot }$ that undergo Jeans instability to form stars. Fragmentation caused by intense and compressive turbulence prevents a runaway collapse of the cloud. The self-bound clumps with smaller masses in the turbulent primordial clouds suggest a possible pathway to decrease the theoretical mass scale of the first stars, further reconciling the mass discrepancy between simulations and observations.

Optimization enabled elastic scaling in cloud based on predicted load for resource management

Cloud computing epitomizes an important invention in the field of Information Technology, which presents users with a way of providing on-demand access to a pool of shared computing resources. A major challenge faced by the cloud system is to assign the exact quantity of resources to the users based on the demand, while meeting the Service Level Agreement (SLA). Elasticity is a major aspect that provides the cloud with the capability of adding and removing resources “on the fly” for handling load variations. However, elastic scaling requires suspension of the application tasks forcibly, while performing resource distribution; thereby Quality of Service (QoS) gets affected. In this research, an elastic scaling approach based on optimization is developed which aims at attaining an improved user experience. Here, load prediction is performed based on various factors, like bandwidth, CPU, and memory. Later, horizontal as well as vertical scaling is performed based on the predicted load using the devised leader Harris honey badger algorithm. The devised optimization enabled elastic scaling is evaluated for its effectiveness based on metrics, such as predicted load error, cost, and resource utilization, and is found to have attained values of 0.0193, 153.581, and 0.3217.

Blockchain-Assisted Access Control with Shuffled Shepherd Fire Hawk Optimisation-Key Generation for Privacy Protection in Cloud

Cloud is a model of computing providing on-demand availability of computer system resources, mainly data storage and computing power, without the user’s active management. This provides sharing and reduces user computing and storage costs. With the development of cloud scale and intensification, cloud security is becoming a vital issue in the cloud computing field. Here, a newly optimised hybridisation algorithm, namely, Shuffled Shepherd Fire Hawk Optimization (SSFHO) algorithm is proposed for key generation to initiate privacy protection in the cloud. The different entities involved in this approach are Data Owner (DO), Data User (DU), blockchain, and Cloud Service Provider (CSP). Here, the access control process includes seven categories, such as initialisation, registration, generation of key, data access control, authorisation, authorisation revocation and data protection. Also, registration is done in three phases, like registration of cloud, registration of user and resource publishing. Moreover, SSFHO is the integration among Shuffled Shepherd Optimization Algorithm (SSOA) and Fire Hawk Optimization (FHO) algorithm. The performance of the model is done with various performance metrics like, authorisation time, privacy rate, and memory. These revealed high values of privacy rate of 0.93, low values of authorisation time and memory of 0.559 and 0.843 GHz.

Elasticity unleashed: Fine-grained cloud scaling through distributed three-way decision fusion with multi-head attention

With the proliferation of cloud computing, elastic resource scaling has emerged as a critical challenge. Cloud platforms must efficiently allocate resources to match dynamic workloads. This study addresses the key trade-off between responsiveness and prudence in scaling a novel distributed three-way decision fusion approach. Distributed three-way decisions of immediate, delayed, or no scaling are made at coarse and fine-time granularity levels. Subsequently, a multi-head attention mechanism intelligently fuses these decisions, weighing the relevance of the different granularities to synthesize an integrated scaling strategy. The reactiveness of sudden workload changes are balanced during fusion by considering long-term trends. Comprehensive experiments on real-world data demonstrated that the proposed fusion strategy substantially improves resource efficiency and adherence to service-level agreements compared to existing methods. Multi-head attention imparts autonomy in adapting to diverse operating conditions. The proposed principled methodology and attention-based decision aggregation hold significance for efficient, adaptive, and interpretable cloud scaling mechanisms.

A Strategic Roadmap to Scaling Cloud FinOps Teams: The Vision, Mission, Charter, and Placement

The article provides an in-depth overview of the FinOps methodology, outlining its key components and benefits. It also offers a strategic roadmap for building and scaling a FinOps team, including techniques for gaining executive buy-in. This article focuses on the vision, mission, and charter of FinOps teams, emphasizing the goal of achieving cost efficiency and fostering a culture of cost awareness while maintaining or improving the performance and functionality of cloud-based systems. It discusses the importance of equipping technical and non-technical staff with the techniques, procedures, and tools to monitor, report, and analyze their cloud expenses, ultimately accelerating cloud financial success. Additionally, the article highlights the transformative impact of Cloud FinOps functionality on organizations, empowering them to maximize the value of their cloud investments and achieve optimal cost efficiency.

A Strategic Roadmap to Scaling Cloud FinOps Teams: The Vision, Mission, Charter, and Placement

The article provides an in-depth overview of the FinOps methodology, outlining its key components and benefits. It also offers a strategic roadmap for building and scaling a FinOps team, including techniques for gaining executive buy-in. This article focuses on the vision, mission, and charter of FinOps teams, emphasizing the goal of achieving cost efficiency and fostering a culture of cost awareness while maintaining or improving the performance and functionality of cloud-based systems. It discusses the importance of equipping technical and non-technical staff with the techniques, procedures, and tools to monitor, report, and analyze their cloud expenses, ultimately accelerating cloud financial success. Additionally, the article highlights the transformative impact of Cloud FinOps functionality on organizations, empowering them to maximize the value of their cloud investments and achieve optimal cost efficiency