Scalable Resource Research Articles

Learning Decentralized and Scalable Resource Management for Wireless Powered Communication Networks

Learning Decentralized and Scalable Resource Management for Wireless Powered Communication Networks

HPC resources for CMS offline computing: An integration and scalability challenge for the Submission Infrastructure

HPC resources for CMS offline computing: An integration and scalability challenge for the Submission Infrastructure

Cutting-Edge Inference: Dynamic DNN Model Partitioning and Resource Scaling for Mobile AI

Cutting-Edge Inference: Dynamic DNN Model Partitioning and Resource Scaling for Mobile AI

Cloud-Based Solutions for Video Streaming and Big Data Testing

In the era of digital transformation, cloud-based solutions have revolutionized the video streaming industry and big data testing methodologies. As consumer demand for high-quality, on-demand video content surges, traditional on-premises infrastructure often falls short in meeting scalability, performance, and cost-efficiency requirements. Cloud-based platforms, with their scalable resources and flexibility, have become essential in addressing these challenges. This paper explores the benefits and implementation strategies of cloud-based solutions for video streaming and big data testing, focusing on how these technologies enhance performance, scalability, and cost-effectiveness Video streaming services require a robust infrastructure capable of handling large volumes of data and delivering seamless user experiences across diverse devices and network conditions. Cloud-based video streaming solutions offer several advantages, including the ability to dynamically scale resources based on user demand, optimize content delivery through content delivery networks (CDNs), and provide a global reach without the need for extensive physical infrastructure. By leveraging cloud services, streaming platforms can reduce latency, enhance video quality, and improve overall user satisfaction. Traditional testing methods often struggle to cope with the complexity and volume of big data. Cloud-based testing platforms provide scalable environments where testers can simulate various data scenarios, execute performance tests, and analyze results with greater efficiency. The cloud's elasticity allows for on-demand provisioning of testing resources, making it easier to handle the dynamic nature of big data and conduct comprehensive testing without investing in expensive hardware.

Розробка методу забезпечення високої доступності для програмного забезпечення управління конфігураціями

The article proposes its own method of providing high availability for configuration management software. The current state of the electronic resources management sphere was examined, the reasons for the use of automation tools were provided. The advantages of using configuration management software were analyzed, examples of using Infrastructure as Code and GitOps approaches to automate the deployment and scaling of electronic resources were given. The existing methods of ensuring high availability were analyzed. The development of our own method of ensuring high availability was carried out. The resulting method of providing high availability is based on the Raft consensus algorithm and the software system clustering approach and extends them with its own solutions. The algorithm of the proposed method was developed, the resulting flowchart of the algorithm and individual steps of its implementation were described in detail. The efficiency of the developed method was evaluated. An a priori ranking of a number of factors that evaluate the effectiveness of automatic recovery strategies and methods was conducted. The analysis of the results has shown that the proposed method implements the most important factors for experts, and in terms of RTO and RPO, the method can work on a par with existing popular disaster recovery strategies.

VNF and CNF Placement in 5G: Recent Advances and Future Trends

With the growing demand for openness, scalability, and granularity, mobile network function virtualization (NFV) has emerged as a key enabler for the most of mobile network operators. NFV decouples network functions from hardware devices. This decoupling allows network services, called Virtualized Network Functions (VNFs), to be hosted on commodity hardware which simplifies and enhances service deployment and management for providers, improves flexibility, and leads to efficient and scalable resource usage, and lower costs. The proper placement of VNFs in the hosting infrastructures is one of the main technical challenges. This placement significantly influences the network’s performance, reliability, and operating costs. The VNF placement is NP-Hard. Therefore, there is a need for placement methods that can cope with the complexity of the problem and find appropriate solutions in a reasonable duration. The primary purpose of this study is to provide a taxonomy of optimization techniques used to tackle the VNF placement problems. We classify the studied papers based on performance metrics, methods, algorithms, and environment. Virtualization is not limited to simply replacing physical machines with virtual machines or VNFs, but may also include micro-services, containers, and cloud-native systems. In this context, the second part of our article focuses on the placement of Containers Network Functions (CNFs) in edge/fog computing. Many issues have been considered as traffic congestion, resource utilization, energy consumption, performance degradation, etc. For each matter, various solutions are proposed through different surveys and research papers in which each one addresses the placement problem in a specific manner by suggesting single objective or multi-objective methods based on different types of algorithms such as heuristic, meta-heuristic, and machine learning algorithms.

Effect of resourcefulness practice combined with mindfulness training on improving the symptoms of depression in patients after percutaneous coronary intervention

ObjectiveThis study aimed to explore the effect of resourcefulness and mindfulness training on depressive patients after percutaneous coronary intervention (PCI). MethodsThis single-center, randomized clinical trial was conducted. From October 2020 to August 2021, 132 depressive patients after PCI were selected from 4 wards of the cardiovascular department of a Class A tertiary hospital in Tangshan, China and divided into control group (CNG), resourcefulness practice group (RPG), mindfulness training group (MTG) and resourcefulness practice combined mindfulness training group (CMG). On the basis of routine nursing, the three intervention groups received resourecfulness practice, mindfulness training, and resourcefulness practice combined with mindfulness training for 12 weeks (1–6 weeks, once a week, 30 min each time; 7–12 weeks, practice three times a week and live on WeChat once a week, 30 min each time). The control group received routine nursing. Before the intervention, at 12 weeks and 24 weeks after the intervention, the Chinese version of the Cardiac Depression Scale, the Chinese Resourcefulness Scale, and the Five Facet Mindfulness Questionnaire were used to evaluate the depressive symptoms, the level of resourcefulness and the level of mindfulness among patients, respectively. ResultsA total of 132 patients completed the study (33 participants in each groups). After 12 weeks of intervention, the mixed effects model revealed that the level of resourcefulness and mindfulness were improved, and the depressive symptoms of patients were reduced in CMG as compared to CNG (the least square mean difference was −12.99, −37.19, 19.45, respectively, P < 0.01). Compared with the RPG, the level of mindfulness was improved, and the depressive symptoms were reduced in CMG (the least squares mean difference was −13.54, 10.99, respectively, P < 0.01). The level of resourcefulness of patients in CMG was higher than MTG (least squares mean difference was −8.37, P < 0.05). After 24 weeks of intervention, the level of mindfulness was improved, and the depressive symptoms were reduced in CMG, as compared to CNG (the least squares mean difference of the depressive symptoms and the level of mindfulness was −12.45, 13.03, respectively, P < 0.01) and RPG (the least squares mean difference was −7.79, 9.09, respectively, P < 0.05). Compared with the MTG, the depressive symptoms among patients were reduced in CMG (least squares mean difference was 7.37, P < 0.05). ConclusionResourcefulness practice combined with mindfulness training can be effective in reducing the symptoms of depression in patients after PCI, and the effects could last longer. Mindfulness training can supplement spiritual resourcefulness in resourcefulness practice.

Selection of summer feeding sites and food resources by female migratory caribou (Rangifer tarandus) determined using camera collars.

Migratory caribou (Rangifer tarandus) is a socioeconomically and culturally key species for northern communities in the Arctic, and most of its populations are experiencing a sharp decline. Female migratory caribou depend on the availability of summer habitat resources to meet the needs associated with lactation and the accumulation of fat reserves to survive when resources are less abundant. Because of the large scales at which habitat and resource data are usually available, information on how female migratory caribou select habitat and resources at fine scales in the wild is lacking. To document selection of summer feeding sites, we equipped 60 female caribou with camera collars from 2016 to 2018. We collected a total of 65,150 10-sec videos between June 1st and September 1st for three years with contrasted spring phenology. We determined the selection at the feeding site scale (3rd scale of Johnson) and food item scale (4th scale of Johnson) using resource selection probability functions. Wetlands were highly selected as feeding sites in June and July while they were avoided in August. Shrublands were mostly selected in July and August. At the resources scale, lichen, birch, willow, and mushrooms were the most strongly selected resources. Our results provide precise and novel information on habitat selection at feeding sites and food resources selected by female caribou in the wild. This information will help understand foraging patterns and habitat selection behavior of female migratory caribou and will contribute to the management and conservation of its declining populations.

The Relationships between Family Resources, Parental Stress, and Child Well-Being in Pediatric Sickle Cell Disease

Introduction: Parents and caregivers play an important role in the management of pediatric chronic illness. In sickle cell disease (SCD), families often manage multiple components simultaneously, such as attending clinic appointments, following a medication regimen, and treating pain symptoms. The availability of resources is known to affect disease management. However, it is unknown how children and parents view their resource availability, and how this can impact both parent stress and child quality of life (QoL). The current study examined the ways in which family resources, parental stress, and child well-being interact with and potentially influence each other. We hypothesize the following: 1) There is a relationship between family resources and child well-being such that more family resources leads to higher quality of life reporting; 2) There is a relationship between family resources and parental stress such that more family resources leads to lower parental stress; and 3) Parental stress moderates the relationship between family resources and child well-being. Methods : Parents of youth with SCD reported on the adequacy of their family's resources by completing the 30-item Family Resource Scale (FRS), rating items on a 5-point Likert scale (1 = not adequate at all to 5 = almost always adequate). Parents also reported on their parental stress by completing the 42-item Pediatric Inventory for Parents (PIP), rating items on a 5-point Likert scale (1 = never/not at all to 5 = very often/extremely). Children and parents reported on the child's overall well-being by completing the 43-item PedsQL SCD Module self-report form and 43-item parent proxy-report, rating items on a 5-point Likert scale (0 = never to 5 = almost always). Pearson's correlation coefficients were calculated to measure relationships between family resources (FRS) and child well-being (PedsQL), family resources (FRS) and parental stress (PIP), and parental stress (PIP) and child well-being (PedsQL). Multiple regressions were used to determine if parental stress moderated, or changed the effect of, family resources on child well-being. Results: Seventy-eight primary caregivers of youth aged 7-16 with SCD (Mean age = 10.37, SD = 2.86; 58% female; 68% HbSS; 87% African American or Black; 90% Not Hispanic) completed measures on their family resources and parental stress. Preliminary correlation analyses show the relationship between family resources and child self-reports on QoL was weak, positive, and not significant (r = 0.18, p = .167). The relationship between family resources and parent proxy-reports on QoL, however, was moderate, positive, and significant (r = .36, p = .001). The correlation between family resources and parental stress was moderate, negative, and significant (r = -.42, p &amp;lt; .001). The correlation between parental stress and child QoL self-reports was weak, negative, and significant (r = -.225, p = .05). The correlation between parental stress and parent proxy-reports on child QoL was moderate, negative, and significant (r= -.50, p = &amp;lt;.001). Multiple regression results for parent proxy-reports on child QoL show parental stress significantly and negatively impacts parent proxy-reports on child QoL (b = -5.52, p = .034). The relationship between family resources and parent proxy-reports on child QoL was not moderated by parental stress (b = 2.44, p = .207). Conclusion: There is a relationship between family resources and child well-being such that more resources reflect higher quality of life, but only when the parents are reporting on their child's well-being. Current results also show that as family resources decrease, parental stress moderately increases. Parental stress significantly impacted both self-reports and parent proxy-reports on child quality of life, though there was a weaker relationship shown in child self-reports. It's likely that parents who are more stressed view their child as being more stressed. Future interventions should consider including parent components to 1) further understand the role of parental stress as it relates to pediatric SCD management and 2) identify ways to best support families experiencing resource inadequacy or high levels of parental stress. Developing screeners to give to parents of youth with SCD during clinic appointments could offer a destigmatized way of connecting families to resources and providing tools for parental stress management.

MEDICAL AND SOCIAL REASONING FOR IMPROVING THE ORGANISATION OF PREVENTING AND FIGHTING SKIN CANCER IN THE CONDITIONS OF THE PUBLIC HEALTH SYSTEM DEVELOPMENT

Introduction. Scientific reasoning for improving the organisation of cancer prevention and the providing onco-dermatological care to the population in the conditions of the public health system development.&#x0D; Materials and methods. The article includes the method of system approach as well as bibliosemantic, epidemiological, medical and statistical, sociological, expert assessment, content analysis, structural and logical analysis, modelling methods. Sources of information are the data from scientific literature, the Center for Medical Statistics of the Ministry of Health Care of Ukraine, the National Cancer Registry of Ukraine, the sociological research among various groups of participants in providing healthcare services and strategic documents of the national and international levels.&#x0D; Results. Unfavourable trends in skin malignancies spread, an increase in their contribution to the burden of the disease and the imperfection of the organisation of skin cancer prevention and the onco-dermatological care to the population were revealed. According to the principles of the state policy and the recommendations of the international organisations the directions of improving the preventive, therapeutic and diagnostic services were reasoned out on the basis of the data from the scientific literature, epidemiological research, spread, locational, gender and age peculiarities of skin cancer among the population, risk factors for their development, estimating the health care resource support, scale and results of the activities, studies of the patients, doctors and health care professionals' opinions. The modern functional-organisational model of the prevention system and control of skin cancer in the conditions of the public health system development with the definition of its principles, structures and functions was reasoned and worked out.&#x0D; Conclusions. The practical implementation of the measures for improving the prevention organisation and therapeutic and diagnostic work to fight skin cancer and the developed functional and organisational model of the system of prevention and control of onco-dermatological pathology in conditions of the public health development will effectively prevent onco-dermatological diseases, reduce the burden of skin cancer, improve people’s health and health care performance indicators and contribute to the health care sustainable development and the population’s well-being.

Do interpersonal resources improve your job performance? Scale development of interpersonal resources and the moderated mediation model of group harmony.

How to help workers establish a safe and healthy working environment is the primary purpose of promoting workplace health psychology. The aim of this study was to investigate the relationship between interpersonal resources and job performance (work performance, organizational citizen behavior). Besides, this study tries to clarify the psychological mechanisms of vitality and emotional attachment, and further considers the moderating role of group harmony. Questionnaires were used. Two samples included 267 and 285 questionnaires were collected in study 1. A total of 151 valid and matched surveys were collected in study 2. Study 1 tested the psychometric properties of the interpersonal resources scale, and then, established three constructs of interpersonal resources: "interpersonal caring", "interpersonal inclusion", and "interpersonal connection". Study 2 results showed positive correlations between interpersonal resources and job performance. Vitality has the mediating effect between interpersonal resources and work performance, and emotional attachment has the mediating effect between interpersonal resources and organizational citizen behaviors. In addition, the moderating effects of group harmony were supported. This research covered two studies to clarify the construct of interpersonal resources and develop an interpersonal resources scale with reliability and validity. Furthermore, this research further discussed the theoretical meaning and practical application and explained the future research direction. This research not only responds to experts' calls but also considers the influence of cultural values to examine the model of interpersonal resources.

A Study Resource Optimization Techniques Based Job Scheduling in Cloud Computing

Cloud computing has revolutionized the way businesses and individuals utilize computing resources. It offers on-demand access to a vast pool of virtualized resources, such as processing power, storage, and networking, through the Internet. One of the key challenges in cloud computing is efficiently scheduling jobs to maximize resource utilization and minimize costs. Job scheduling in cloud computing involves allocating tasks or jobs to available resources in an optimal manner. The objective is to minimize job completion time, maximize resource utilization, and meet various performance metrics such as response time, throughput, and energy consumption. Resource optimization techniques play a crucial role in achieving these objectives. Resource optimization techniques aim to efficiently allocate resources to jobs, taking into account factors like resource availability, job priorities, and constraints. These techniques utilize various algorithms and optimization approaches to make intelligent decisions about resource allocation. Research on resource optimization techniques for job scheduling in cloud computing is of significant importance due to the following reasons: Efficient Resource Utilization: Cloud computing environments consist of a large number of resources that need to be utilized effectively to maximize cost savings and overall system performance. By optimizing job scheduling, researchers can develop algorithms and techniques that ensure efficient utilization of resources, leading to improved productivity and reduced costs. Performance Improvement: Job scheduling plays a crucial role in meeting performance metrics such as response time, throughput, and reliability. By designing intelligent scheduling algorithms, researchers can improve the overall system performance, leading to better user experience and customer satisfaction. Scalability: Cloud computing environments are highly scalable, allowing users to dynamically scale resources based on their needs. Effective job scheduling techniques enable efficient resource allocation and scaling, ensuring that the system can handle varying workloads without compromising performance. Energy Efficiency: Cloud data centres consume significant amounts of energy, and optimizing resource allocation can contribute to energy conservation. By scheduling jobs intelligently, researchers can reduce energy consumption, leading to environmental benefits and cost savings for cloud service providers. Quality of Service (QoS): Cloud computing service providers often have service-level agreements (SLAs) that define the QoS requirements expected by users. Resource optimization techniques for job scheduling can help meet these SLAs by ensuring that jobs are allocated resources in a timely manner, meeting performance guarantees, and maintaining high service availability. Here in this research, we have used the method of the weighted product model (WPM). For the topic of Resource Optimization Techniques Based Job Scheduling in Cloud Computing For calculating the values of alternative and evaluation parameters. A variation of the WSM called the weighted product method (WPM) has been proposed to address some of the weaknesses of The WSM that came before it. The main distinction is that the multiplication is being used in place of additional. The terms "scoring methods" are frequently used to describe WSM and WPM Execution time on Virtual machine, Transmission time (delay)on Virtual machine, Processing cost of a task on virtual machine resource optimization techniques based on job scheduling play a crucial role in maximizing the efficiency and performance of cloud computing systems. By effectively managing and allocating resources, these techniques help minimize costs, reduce energy consumption, and improve overall system throughput. One of the key findings is that intelligent job scheduling algorithms, such as genetic algorithms, ant colony optimization,

Development of a pressure coring system for the investigation of deep underground exploration

To provide a more accurate evaluation of the scale of deep underground resources, a new pressure coring system was carefully developed and its strength and safety of the engineering were verified by theoretical analyses and numerical simulation. The designed pressure coring system can obtain cores with length of 3 m and diameter of 50 mm at 70 MPa. The results of the ball-drop operation experiment demonstrate that differential motion assembly can effectively cut a safety pin by applying a tensile force of 4852 N, and it can lift the core tube through the center pole to complete a series of mechanical actions to seal the pressure. Additionally, by maintaining pressures at 70 MPa for 30 min, the pressure sealing capacity of the system was proven. Furthermore, a core sample with a diameter of 50 mm was obtained through a core drilling experiment and the coring performance of the pressure coring system was verified. This study can not only enrich the existing onshore coring technology but also provide a theoretical guide and design criteria for the development of similar pressure coring systems to meet the demand for deeper underground exploration.

Gaussian Adapted Markov Model with Overhauled Fluctuation Analysis-Based Big Data Streaming Model in Cloud.

An accurate resource usage prediction in the big data streaming applications still remains as one of the complex processes. In the existing works, various resource scaling techniques are developed for forecasting the resource usage in the big data streaming systems. However, the baseline streaming mechanisms limit with the issues of inefficient resource scaling, inaccurate forecasting, high latency, and running time. Therefore, the proposed work motivates to develop a new framework, named as Gaussian adapted Markov model (GAMM)-overhauled fluctuation analysis (OFA), for an efficient big data streaming in the cloud systems. The purpose of this work is to efficiently manage the time-bounded big data streaming applications with reduced error rate. In this study, the gating strategy is also used to extract the set of features for obtaining nonlinear distribution of data and fat convergence solution, used to perform the fluctuation analysis. Moreover, the layered architecture is developed for simplifying the process of resource forecasting in the streaming applications. During experimentation, the results of the proposed stream model GAMM-OFA are validated and compared by using different measures.

The effectiveness of a workplace violence prevention strategy based on situational prevention theory for nurses in managing violent situations: a quasi-experimental study

BackgroundWorkplace violence (WPV) poses a significant occupational hazard for nurses. The efficacy of current education and training programs in mitigating WPV incidence among nurses remains uncertain, possibly due to insufficient consideration of clinical contexts and nurses’ specific needs. Therefore, this study developed a WPV prevention strategy based on the actual requirements of clinical nurses and situational prevention theory and aimed to explore its application effects.MethodsUnder the guidance of situational prevention theory, a WPV prevention strategy for nurses was constructed through literature review, semi-structured interviews and focus group discussion. This study adopted a self-controlled research design, and trained 130 nurses selected from a comprehensive tertiary grade A hospital in Suzhou in this WPV prevention strategy. Data were collected through structured questionnaires, including the revised WPV questionnaire, WPV severity grading scale, and hospital WPV coping resources scale. The WPV incidence, severity, and WPV coping resource scores of nurses were collected before the intervention, as well as at 3 months, 6 months, and 9 months after training.ResultsThe WPV prevention strategy comprised 11 prevention plans based on 11 high-risk situational elements of WPV. Each prevention plan included the WPV prevention flowchart, treatment principle, and communication strategy. The strategy demonstrated excellent feasibility and practicality. Following the intervention, the overall incidence of WPV among nurses significantly decreased from 63.85% (baseline) to 46.15% (9 months after training) (P < 0.05). After the training, the severity of psychological violence (Wald χ² = 20.066, P < 0.001) and physical violence (Wald χ² = 9.100, P = 0.028) reported by nurses decreased compared to the baseline (P < 0.05). Moreover, the overall WPV coping resource score significantly increased from [66.50 (57.00, 77.25) points] (baseline) to [80.00 (68.00, 97.25) points] (9 months after training) (P < 0.05).ConclusionsThe described WPV prevention strategy, grounded in situational prevention theory and tailored to the needs of clinical nurses, effectively reduced WPV incidence, mitigated its severity, and enhanced nurses’ WPV coping resources. This approach offered new avenues for nurses in the prevention of WPV.

The Association Between Family Resources and Language Among Young Children Who are Deaf and Hard of Hearing.

Our study (1) examined demographic factors in families with children with bilateral hearing loss and how they relate to Family Resource Scale (FRS) questionnaire data and (2) examined correlations between FRS data and measures of language. Children aged 6 months to 10 years with bilateral hearing loss were enrolled. Parents completed the FRS questionnaire to assess their access to socioeconomic resources at the first language measurement visit. Assessments measured receptive and expressive language, nonverbal intelligence quotient, and adaptive functioning. Among the 85 children included in the analysis, approximately 40% had hearing loss classified as mild to moderate and 25% had a cochlear implant. Participants' mean FRS score was 130 (SD 16.6) (with the highest possible score of 150 and indicating better access to resources). Significant positive correlations ( p -value ≤ 0.05) were found between maternal education, paternal education, and family income and several FRS subscales (Growth and Support, Necessities in Health, Childcare, Personal Resources). Significant positive correlations were found between the Necessities in Health subscale and all the language measurements. Children whose parents reported better access to socioeconomic resources related to health care had higher language performance scores. Although early access to intervention services has improved for deaf or hard-of-hearing children, there are other variables contributing to language development, including access to socioeconomic resources. This study highlights the need for further research addressing more specific and modifiable resources to improve language performance for deaf or hard-of-hearing children.

Efficient Auto‐scaling for Host Load Prediction through VM migration in Cloud

SummaryThe expeditious deployment of Cloud applications and services on wide‐ranging Cloud Data Centres (CDC) gives rise to the utilization of many resources. Moreover, by the increase in resource utilization, virtualization also greatly impacts achieving desired performance. The major challenges in virtualization are detecting over‐utilized or under‐utilized hosts at the right time and the proper scaling of Virtual Machines (VM) on the accurate host. Auto‐scaling in Cloud Computing allows the service providers to scale up or down the resources automatically and provides on‐demand computing power and storage capacities. Effective utilization and autonomous scaling of resources eventually reduce the load, energy consumption, and operating costs. In this paper, an efficient auto‐scaling approach for predicting host load through VM migration has been proposed. The ensemble method using different time‐series forecasting models has been proposed to forecast the approaching workload on the host. Based on this predicted load, different algorithms have been devised to detect over‐utilized and under‐utilized hosts and VMs can be migrated. The designed approach has been validated by experimentation on a real‐time Google cluster dataset. The proposed technique significantly improves average CPU utilization and reduces over‐utilization and under‐utilization. It also minimizes response time, service level agreement violations, and the slighter number of migrations and scaling overhead.

Workload prediction for enhancing power efficiency of cloud data centers using optimized self‐attention‐based progressive generative adversarial network

SummaryNowadays, future workload prediction is an important requirement in cloud data centers to maintain flexibility and scalability of resources. However, due to unexpected peaks, drops in workload, noise, and redundancy in user requests, there is a considerable variance in resource demands, making it difficult to accurately predict workloads. Therefore, a self‐attention‐based progressive generative adversarial network (SAPGAN) optimized with Giza Pyramids Construction Algorithm (GPCA)‐based workload prediction is proposed for Sustainable Cloud Data Centers (CDC). At first, redundant data in historical data obtained through CDC are filtered utilizing Markov chain random field (MCRF) co‐simulation method. These pre‐processed historical data are supplied to the SAPGAN. The SAPGAN weight parameters are optimized by GPCA. The proposed method is analyzed using 2 benchmark datasets: HTTP traces from Saskatchewan and NASA. The simulation is implemented in JAVA. The performance metrics is examined to verify the efficacy of the proposed technique. The performance of the proposed approach provides 28.70%, 11.87%, and 14.79% higher accuracy; 30.15%, 11.72%, and 18.34% lesser energy consume for the dataset of NASA; and 5.32%, 2.45%, and 5.67% higher accuracy; 12.36%, 24.24%, and 34.16% lesser energy consume for the dataset of Saskatchewan HTTP traces compared with existing methods, such as auto‐adaptive learning in a dynamic cloud environment (AADEA‐WLP‐CDC), a neural network model depending on biphase adaptive learning for anticipating cloud data center workload (BALNN‐WLP‐CDC) and multiple scale ensemble of deep learning framework for multistep‐ahead cloud workload prediction (EMD‐LSTM‐GAN‐WLP‐CDC).

Potenential for thermal energy from Tunnels beneath Manchester and Crewe: a case study

potenential for thermal energy from Tunnels beneath Manchester and Crewe: a case study

Advancing Cloud Technology Security: Leveraging High-Level Coding Languages like Python and SQL for Strengthening Security Systems and Automating Top Control Processes

In today's dynamic business environment, staying ahead of competitors requires the integration of cutting-edge technologies into organizational processes. Cloud Computing, a transformative technological advancement, offers a promising avenue for achieving operational efficiency and innovation. This paper explores the integration of Cloud Computing with two powerful coding languages, Python and SQL, to enhance cloud security and automate control processes. Cloud Computing's adoption has revolutionized resource management through virtualization and diverse computing models. However, it also introduces security challenges like data breaches and unauthorized access. Python and SQL emerge as essential tools for addressing these challenges and automating various control processes. Python's versatility empowers organizations to establish sophisticated security protocols and automate tasks such as intrusion detection, anomaly detection, real-time monitoring, and computer vision. On the other hand, SQL's role involves automating control processes like resource provisioning, scaling, backup, recovery, access control, and database management.&#x0D; Integrating Python and SQL offers a holistic approach to cloud security enhancement. However, challenges such as skill set requirements, code quality, integration, maintenance, scalability, monitoring, and data privacy must be addressed. Fortunately, solutions like Snowpark, dbt, Hex, and Dataiku provide platforms that unify various programming languages, fostering collaboration and streamlining tasks. This convergence of Cloud Computing with Python and SQL presents numerous benefits. Automation enhances efficiency, reduces human error, and ensures consistent control process execution. This synergy allows organizations to achieve scalability, cost savings, improved security, and comprehensive monitoring and reporting.&#x0D; As institutions increasingly d on Cloud Computing to drive innovation and competitiveness, the importance of fortifying these systems against evolving threats cannot be overstated. Integrating Python and SQL represents a pivotal juncture in achieving this goal. By harnessing their combined power, organizations can create robust security mechanisms, streamline operations, and promote cross-functional collaboration. As the digital landscape evolves, embracing this approach is crucial for sustaining success in a rapidly changing environment.