Management Server Research Articles

Interpretable Mixture of Experts for Decomposition Network on Server Performance Metrics Forecasting

The accurate forecasting of server performance metrics, such as CPU utilization, memory usage, and network bandwidth, is critical for optimizing resource allocation and ensuring system reliability in large-scale computing environments. In this paper, we introduce the Mixture of Experts for Decomposition Kolmogorov–Arnold Network (MOE-KAN), a novel approach designed to improve both the accuracy and interpretability of server performance prediction. The MOE-KAN framework employs a decomposition strategy that breaks down complex, nonlinear server performance patterns into simpler, more interpretable components, facilitating a clearer understanding of how predictions are made. By leveraging a Mixture of Experts (MOE) model, trend and residual components are learned by specialized experts, whose outputs are transparently combined to form the final prediction. The Kolmogorov–Arnold Network further enhances the model’s ability to capture intricate input–output relationships while maintaining transparency in its decision-making process. Experimental results on real-world server performance datasets demonstrate that MOE-KAN not only outperforms traditional models in terms of accuracy but also provides a more trustworthy and interpretable forecasting framework. This makes it particularly suitable for real-time server management and capacity planning, offering both reliability and interpretability in predictive models.

INNOVATION IN LEARNING: DESIGNING KELADI APPLICATIONS USING C#

Technological developments such as the development of digital-based learning media have a major impact on the world of education. This study aims to design and develop the KelaDi (Digital Class) application using Visual Studio and the C# programming language as an interactive learning media for students. The KelaDi application has various features that support the teaching and learning process, such as e-books and e-book readers, learning videos, uploading assignments, and quizzes. The methodology used in this study is a qualitative methodology using the Waterfall model software development approach. The development process includes the stages of requirements analysis, system design, coding and testing program. Supporting software includes Visual Studio 2019 and SQL Server Management Studio 20 software. This program is built using NET 4.7.2 framework technology with the C# programming language. The author hopes that the birth of this program can help support digital-based learning

Strengthening vaccination assessment at a southern oncology center: A quality improvement endeavor.

322 Background: The 2013 revision of the Standards for Adult Immunization Practice by the National Vaccine Advisory Committee (NVAC) mandated healthcare professionals to evaluate and ensure adult patients' immunization. Additionally, the 2024 recommendations from the American Society of Clinical Oncology (ASCO) stressed the importance of determining vaccination status of adults newly diagnosed with cancer, to safeguard against vaccine-preventable diseases. Our 2022-2023 cumulative assessment rate for flu vaccination was 64%, and COVID-19 vaccination status was not being assessed in the electronic medical record (EMR). This prevented us from identifying patients who would benefit from vaccination. Methods: A quality improvement initiative was initiated at Norton Cancer Institute – Downtown Office, focusing on enhancing vaccination assessment. Launching in October 2024, the initiative targeted flu and COVID-19 vaccination assessment, with updates to flu assessment questions and the implementation of a COVID-19 assessment flowsheet in the EMR. Best practice advisories (BPAs) were employed to prompt nurse clinicians and providers to order vaccines based on patient responses. Staff compliance was reinforced through tailored training sessions for medical assistants responsible for initial patient assessments, with an emphasis on reconciling external records to improve assessment accuracy. A de-identified data set was created to analyze workflow compliance, the data was abstracted from the relational databases of our institution's EMR system. Abstraction queries were written in SQL using the Microsoft SQL Server Management Studio platform. Results: Following the implementation of the initiative, there was an increase in vaccination assessment completion rates for both Flu and Covid-19. Flu assessment rates improved from 64% at the end of the 2022-2023 flu season to 76% during the 2023-2024 flu season. The implementation of Covid-19 Vaccination assessment workflow also resulted in increased assessment rates each month of the initiative, starting in October with initial rate of 36% and ending in March with 67% cumulative assessment rate. Conclusions: The targeted intervention increased vaccination assessment practices within the oncology setting; by improving clinical workflow to better align with NVAC guidelines and ASCO recommendations for assessment of vaccination status. By Improving vaccination assessment processes our hope is to improve rates of vaccination for oncology patients.

Development of a Hint Suggestion System Based on Student's Learning Status in Server Management Exercises

Development of a Hint Suggestion System Based on Student's Learning Status in Server Management Exercises

Analyzing AWS Edge Computing Solutions to Enhance IoT Deployments

This paper explores integrating Internet of Things (IoT) deployments with edge computing, focusing on Amazon Web Services (AWS) as a key facilitator. It provides an analysis of AWS IoT services and their integration with edge computing technologies, addressing challenges, and practical applications across industries, and outlining future research directions. IoT and edge computing revolutionize data processing by enabling real-time analytics, reduced latency, and enhanced operational efficiency. IoT involves interconnected devices autonomously gathering and exchanging data, while edge computing processes data near its source, decentralizing data processing and minimizing data transmission to centralized servers. AWS facilitates scalable and secure infrastructures for IoT and edge computing. AWS IoT Core manages IoT device connectivity and data ingestion, AWS Greengrass extends AWS capabilities to edge devices, and AWS Lambda enables serverless computing, empowering efficient deployment and scaling of IoT applications. Centralized cloud architectures often struggle with vast IoT data. Edge computing decentralizes data processing, reducing latency, enhancing real-time capabilities, and minimizing bandwidth. AWS ensures secure device connectivity through AWS IoT Core, supporting various protocols for seamless integration with IoT devices. AWS Greengrass allows local data processing and machine learning at the edge, vital for environments with limited connectivity or stringent latency requirements. AWS Lambda supports serverless computing, enabling scalable, event-driven architectures without server management, crucial for fluctuating IoT workloads. In conclusion, AWS advances IoT capabilities at the edge, with practical implementations across industries. As IoT evolves, AWS remains pivotal, innovating to meet dynamic IoT deployment demands.

FoPraNet-BW: An Infrastructure for Clinical Studies in Practice-Based Research Networks in the German Health System.

General practice-based research networks have become an integral tool to gain medical knowledge from primary care in many countries. For this purpose, a scalable IT-infrastructure is presented considering the limiting peculiarities in the German health system and enabling GPs to participate in clinical studies based on their patient population. The infrastructure consists of a central study management server and local clients for each practice. It adopts to the currently limited digital connectivity of GP practices, data protection regulations for clinical data and the needs of the medical staff to manage a clinical study. The infrastructure is in production at the four university hospitals in the state of Baden-Württemberg. Until now three clinical studies with over 70 GPs and 350 Participants are successfully conducted or have been finished. Further clinical studies are in the planning stages.

Managing Linux servers with LLM-based AI agents: An empirical evaluation with GPT4

This paper presents an empirical study on the application of Large Language Model (LLM)-based AI agents for automating server management tasks in Linux environments. We aim to evaluate the effectiveness, efficiency, and adaptability of LLM-based AI agents in handling a wide range of server management tasks, and to identify the potential benefits and challenges of employing such agents in real-world scenarios. We present an empirical study where a GPT-based AI agent autonomously executes 150 unique tasks across 9 categories, ranging from file management to editing to program compilations. The agent operates in a Dockerized Linux sandbox, interpreting task descriptions and generating appropriate commands or scripts. Our findings reveal the agent’s proficiency in executing tasks autonomously and adapting to feedback, demonstrating the potential of LLMs in simplifying complex server management for users with varying technical expertise. This study contributes to the understanding of LLM applications in server management scenarios, and paves the foundation for future research in this domain.

Principles of Cloud Computing Infrastructure IaaS

Infrastructure as a Service (IaaS) represents a cornerstone in contemporary cloud computing, providing essential on-demand computing resources, including servers, storage, and networking. This paper explores the foundational principles of IaaS, highlighting its ability to facilitate dynamic server management through practices like auto-scaling and ephemeral server utilization. The use of custom images in IaaS ensures minimal downtime and scalable deployment, while loose coupling enhances fault tolerance and adaptability. Emphasizing high availability through auto-scaling, IaaS supports continuous operations even in geographically vulnerable areas. The security framework of IaaS, encompassing a shared responsibility model, ensures robust data protection and compliance with industry standards. The paper also addresses cost optimization strategies and the integration of hybrid cloud architectures to maximize resource efficiency and maintain data integrity. Overall, IaaS is integral to modern IT infrastructure, offering flexible, scalable, and cost-effective solutions that align with the evolving demands of the digital landscape.

PxBLAT: an efficient python binding library for BLAT

BackgroundWith the surge in genomic data driven by advancements in sequencing technologies, the demand for efficient bioinformatics tools for sequence analysis has become paramount. BLAST-like alignment tool (BLAT), a sequence alignment tool, faces limitations in performance efficiency and integration with modern programming environments, particularly Python. This study introduces PxBLAT, a Python-based framework designed to enhance the capabilities of BLAT, focusing on usability, computational efficiency, and seamless integration within the Python ecosystem.ResultsPxBLAT demonstrates significant improvements over BLAT in execution speed and data handling, as evidenced by comprehensive benchmarks conducted across various sample groups ranging from 50 to 600 samples. These experiments highlight a notable speedup, reducing execution time compared to BLAT. The framework also introduces user-friendly features such as improved server management, data conversion utilities, and shell completion, enhancing the overall user experience. Additionally, the provision of extensive documentation and comprehensive testing supports community engagement and facilitates the adoption of PxBLAT.ConclusionsPxBLAT stands out as a robust alternative to BLAT, offering performance and user interaction enhancements. Its development underscores the potential for modern programming languages to improve bioinformatics tools, aligning with the needs of contemporary genomic research. By providing a more efficient, user-friendly tool, PxBLAT has the potential to impact genomic data analysis workflows, supporting faster and more accurate sequence analysis in a Python environment.

Off-Grid Electric Vehicle Charging Station with Integrated Local Server OCPP Protocol as a Management System

Abstract Electric vehicles are widely regarded as pivotal in driving the sustainability of transportation networks forward, thanks to their capacity to diminish carbon emissions, enhance air quality, and bolster the robustness of electricity grids. The accessibility of charging infrastructure and the subjective norms that endorse electric mobility actively shape the electric vehicles acceptance. In this study, Our main goal is to provide off-grid electric vehicle charging infrastructures and the data communication protocols that connect to servers. We analyze the specifications of the OCPP (Open Charge Point Protocol) with an emphasis on its applicabillity for electric charging stations for vehicles. Our research concludes that off-grid electric vehicle charging systems can be effectively applied to small electric vehicles such as electric motorcycles, scooters, and bicycles. The OCPP data communication protocol can also support interactions between small electric vehicle charging stations and central server management systems (CSMS). Furthermore, we tested the electric vehicle charging process for a duration of two hours, and the charging station consistently produced stable voltage, current, and power output, matching the inverter outputs and fulfilling the specifications required by electric vehicle charging adapters. Analysis of throughput data indicates a positive correlation between the number of operational ports at a charging station and the volume of data processed by the server. However, beyond a certain threshold a decline in data transactions was observed, attributable to data loss.

ВИКОРИСТАННЯ ВІДДАЛЕНОГО МОНІТОРИНГУ ДЛЯ ПІДВИЩЕННЯ ЯКОСТІ ТА ЕФЕКТИВНОСТІ У ВИРОБНИЧОМУ СЕРЕДОВИЩІ

The article thoroughly examines the challenges associated with monitoring computer networks and servers in the context of rapid technological advancements and increasing complexity. The effective management and monitoring of networks and servers have become critically important due to the growing exchange of data and the need to ensure their continuous operation. The article proposes solutions based on innovative software tools such as Prometheus and Grafana, which provide real-time monitoring and data visualization capabilities to enhance understanding of the state of networks and servers. Traditional monitoring methods, their limitations, and the advantages of the new approach based on Prometheus and Grafana are discussed. Through these tools, network administrators can gain full control over the status and resource utilization of networks and servers, enabling them to promptly address any issues or deviations in their operation. The successful implementation of a monitoring system using these tools is demonstrated, along with an analysis of the prospects for further development of this approach. The methodology of the research is described in detail, including the main tasks of server and network monitoring, as well as the principles of operation of the Prometheus system. A comparison with traditional monitoring methods is conducted to highlight the advantages of the new approach and its importance in the modern information environment. The article also focuses on the possibilities of extending monitoring to other services and devices such as SSH, email, Jenkins, and Kubernetes. The architectural foundation of Prometheus and Grafana is presented, along with methods of their utilization for data visualization and system status notification. This article will serve as a valuable source of information for network administrators, system engineers, and anyone interested in the monitoring and management of information systems in industrial environments.

Cyber Security Portal for Effective Management of Servers and Firewalls

The project is an innovative endeavour that tackles the difficulties associated with overseeing heterogeneous servers inside the digital ecosystem. It suggests the creation of a single, centralized interface for the real-time monitoring and management of firewall configurations, security, and server health on diverse platforms. With an all-inclusive platform, administrators may take proactive measures against new threats by integrating firewall settings seamlessly, detecting security risks, streamlining server management, and providing administrators with actionable intelligence.Beyond traditional server management, the proposed system offers a centralized hub for effective, safe, and straightforward administrative server management in multi-server environments. It increases the overall effectiveness of server administration by reducing difficulties with server management and establishing the groundwork for a strong and resilient digital infrastructure that can adjust to changing cyber security threats

Predictive Maintenance of Server using Machine Learning and Deep Learning

Traditional IT maintenance often leads to wasted resources and downtime. This paper explores how predictive maintenance (PdM) with machine learning can revolutionize server management in Industry 4.0. By comparing various machine learning models for PdM, we analyze their effectiveness in predicting server failures. The paper emphasizes the critical role PdM plays in boosting server reliability and driving industry transformation, supported by statistics highlighting its growing importance. Furthermore, we bridge the theory-practice gap by proposing a web application specifically designed for server PdM, allowing for proactive maintenance and reduced downtime. Finally, we explore future research directions and emerging trends in server PdM, providing valuable insights for organizations seeking to optimize maintenance practices and achieve operational excellence in the digital age.

Serverless Architectures and Their Influence on Web Development

In recent years, serverless computing has emerged as a transformative technology in the field of web development, offering a new paradigm that eliminates the need for server management and promotes a more efficient deployment model. This computing model enables developers to focus solely on writing code, while the underlying infrastructure scaling, provisioning, and maintenance is handled by cloud service providers. The key attraction of serverless computing lies in its promise to increase operational efficiency, reduce costs, and simplify scalability challenges inherent in traditional web development practices.

Houston Methodist cardiovascular learning health system (CVD-LHS) registry: Methods for development and implementation of an automated electronic medical record-based registry using an informatics framework approach

ObjectivesTo investigate the potential value and feasibility of creating a listing system-wide registry of patients with at-risk and established Atherosclerotic Cardiovascular Disease (ASCVD) within a large healthcare system using automated data extraction methods to systematically identify burden, determinants, and the spectrum of at-risk patients to inform population health management. Additionally, the Houston Methodist Cardiovascular Disease Learning Health System (HM CVD-LHS) registry intends to create high-quality data-driven analytical insights to assess, track, and promote cardiovascular research and care. MethodsWe conducted a retrospective multi-center, cohort analysis of adult patients who were seen in the outpatient settings of a large healthcare system between June 2016 - December 2022 to create an EMR-based registry. A common framework was developed to automatically extract clinical data from the EMR and then integrate it with the social determinants of health information retrieved from external sources. Microsoft's SQL Server Management Studio was used for creating multiple Extract-Transform-Load scripts and stored procedures for collecting, cleaning, storing, monitoring, reviewing, auto-updating, validating, and reporting the data based on the registry goals. ResultsA real-time, programmatically deidentified, auto-updated EMR-based HM CVD-LHS registry was developed with ∼450 variables stored in multiple tables each containing information related to patient's demographics, encounters, diagnoses, vitals, labs, medication use, and comorbidities. Out of 1,171,768 adult individuals in the registry, 113,022 (9.6%) ASCVD patients were identified between June 2016 and December 2022 (mean age was 69.2 ± 12.2 years, with 55% Men and 15% Black individuals). Further, multi-level groupings of patients with laboratory test results and medication use have been analyzed for evaluating the outcomes of interest. ConclusionsHM CVD-LHS registry database was developed successfully providing the listing registry of patients with established ASCVD and those at risk. This approach empowers knowledge inference and provides support for efforts to move away from manual patient chart abstraction by suggesting that a common registry framework with a concurrent design of data collection tools and reporting rapidly extracting useful structured clinical data from EMRs for creating patient or specialty population registries.

A Review on Detection of Cybersecurity Threats in Banking Sectors Using Ai Based Risk Assessment

The continued rise of cybercrime in the current era has frightened not only off-users but also violating financial institutions. The recent studies have shown that most financial institutions lose the credibility of their clients and their investments as a result of these attacks. The cyber-hackers involved in these types of cybercrime are now using more advanced technology. Its vigour surpasses the firewall system currently in use our network. Hence, the security needs for financial institutions are increased. So, the server management and cyber security systems getting higher end security. The cyber security aspects are is always shifting based on the attacks, but the cyber security threats are increased and create the serious discussion about the financial institutions. In General, the cyberspace describes the combination of all types of communication networks, databases, embedded processors, Internet and controllers for exchanging electronic documents. The global network environment is created by telephone wires, coaxial cables, electromagnetic waves and fiber-optic lines. Although the terms cyberspace and the Internet can be used interchangeably, it is still part of cyberspace. In simple terms, cyberspace is a connected web environment. Now, cyber-security is the process of protecting cyberspace from attack, misuse, damage and economic espionage. Cyberspace can sometimes be blocked by intrinsic vulnerabilities that cannot be eliminated. In this study, different types of cyber cards are explained in detail. And the escape processes from certain subtle attack methods are clearly stated.

Power BI and SQL Server Dashboard Data for Monitor Transportation Presence Employees at PT PON Gresik

Employee presence is a crucial aspect in office management. It reflects the way employees work in a company and can impact their productivity. A high level of productivity can indicate achievement of company targets. However, this attendance data is often large and has not been processed. To turn it into valuable information, Business Intelligence (BI) software is needed that is able to visualize employee attendance and transportation data. This research aims to create a dashboard that shows employee attendance data, including entry hours and attendance status, as well as transportation data such as expenditure for each type of vehicle and travel destination code. Microsoft Power BI was used for data visualization, while Microsoft SQL Server Management Studio was used to create the database. Data is collected and processed so that it can become a source of information. The research results include a daily attendance dashboard with peak attendance at 08.00 and 21.00. The trip dashboard shows the highest fare with travel code "7AA0112" exceeding Rp. 12,000,000.00 from PT. PON Gresik. Travel information shows transportation use in Jakarta, Bandung and Surabaya with the largest total fare in Jakarta reaching Rp. 97,761,000.00

Optimizing Big Data Insights with Serverless Architecture

Big data is the huge amount of data, which can be structured, semi-structured, or unstructured, that is required for current commercial processes. Big Data efforts and technologies are used to analyze large amounts of data in order to gain insights critical for strategic decision-making. Data size is constantly rising, reaching petabytes, exabytes, zettabytes, and even yottabytes, offering substantial management and processing issues. In practice, managing massive amounts of data involves several obstacles, such as server management, storage, clustering, and algorithm deployment. Manual intervention hampers the creation of successful Cloud-based data analysis platforms. Serverless computing provides a solution by offering clients pay-per-use backend services, reducing the need for users to manage server operations. This article describes a serverless architecture for large data analytics, including implementation, maintenance, and governance on Amazon Web Services (AWS). Furthermore, it investigates the differences between traditional and big data analytics in a serverless system

Built Custom Alexa Skills using AWS

Abstract:The creation of unique Alexa skills using the Amazon Web Services (AWS) infrastructure is the focus of this project. Alexa skills are voice- activated features that improve Alexa, the virtual assistant from Amazon, in terms of functionality. The project's main goal is to demonstrate the flexibility and adaptability of AWS in voice- activated interactions by developing custom skills that are suited to certain use cases or applications. During the development process, the bespoke Alexa skills are hosted and executed using AWS Lambda, an event-driven computing service. With AWS Lambda, code can be seamlessly integrated without requiring server management, guaranteeing a scalable and effective solution. The stability and dependability of the custom Alexa skills are further enhanced by the extensive suite of tools and services offered by AWS, including the Alexa Skills Kit (ASK), DynamoDB for data storage, and IAM (Identity and Access Management) for security. The complexities of voice-activated technologies, AWS infrastructure, and Alexa skill development are investigated through this research. With the help of the Alexa ecosystem and customized skills, companies, developers, and enthusiasts can use AWS's power to create unique and interesting voice experiences. The project's main ideas are summarized in this abstract, which highlights the use of AWS technology to develop customized and inventive Alexa skills. Keywords:Custom Alexa skills, AWS, Voice- activated technology, Alexa Skills Kit, AWS Lambda, DynamoDB, IAM.

Application solutions of highway freight information systems based on quantum communication.

To improve the security of data transmission in the highway freight information system, this study is an application plan for the highway freight information system based on quantum communication. This solution is based on quantum communication technology to encrypt and transmit key sensitive data[1]; it realizes unified management of quantum keys through the quantum key cloud terminal and provides key services for the highway freight information system; it realizes access to the system through the quantum key cloud service platform. The secure use of mobile terminal quantum keys improves the overall security of the road freight information system. This scheme uses the quantum encryption key generated only once, effectively protecting the entire system's security. The quantum key management server and quantum key cloud platform defined in this plan manage terminals and quantum keys respectively, and jointly produce and distribute quantum keys with the help of other hardware facilities and software to provide secure transmission of key information.