Server Utilization Research Articles

Precision Offloading in Edge Computing: Leveraging Predictive Model

The intended effect of the investigation is to provide sophisticated prediction and decision-making models in order to optimize service delivery and improve the Quality of Experience (QoE) for users. This research tackles the problems that are associated with job offloading in edge computing settings. In order to reduce service latency and improve overall performance, the Bi-Directional Long Short-Term Memory (B-LSTM) model is used. This model provides the ability to forecast task creation and server load. In order to accommodate the particular qualities of different devices, the Selective Objective Offloading Decision (SOOD) approach is presented. This method makes use of the TOPSIS methodology to turn server assessment into a decision-making issue that involves several criteria. A considerable increase of 98.4% in user quality of experience is achieved by the SOOD paradigm. In addition, the Rapid Offloading Decision (ROD) model is presented in order to manage unexpected work patterns. This is accomplished by using the log information of surrounding devices, which results in instantaneous and dependable offloading choices. Through the usage of prediction algorithms and selective decision-making, this research gives a complete strategy to improving the efficiency of edge computing. The goal of this technique is to maximize the utilization of servers and the user experience.

A joint vehicular device scheduling and uncertain resource management scheme for Federated Learning in Internet of Vehicles

Federated learning (FL) offers an effective framework for the efficient process in vehicular edge computing. However, FL encompasses the process of distributing and uploading model parameters, which are inevitably transmitted in a wireless network environment. Some challenges in FL-assisted Internet of Vehicles (IoV) sceneries gradually emerging, such as data heterogeneity, concerned device resources, and unstable communication environment, which necessitate intelligent vehicle selection schemes that accelerate training efficiency. Based on these, we consider a new scenario, specifically an FL-assisted IoV system under uncertain communication conditions, and develop an interval many-objective vehicle selection and bandwidth allocation (IMoVSBA) joint optimization scheme. This scheme takes into account computation latency, energy consumption, server utilization, and data quality, while meeting multi-criteria resource optimization requirements. Among these, server utilization is a new objective designed specifically for this joint optimization problem. For the proposed problem, a novel interval many-objective evolutionary algorithm with individual comprehensive indicator to control the evolution direction (IMaOEACI) is designed. Simulation results demonstrate that this method outperforms other schemes in terms of accuracy, training cost, and server utilization, effectively improving training efficiency in wireless channel environments and reasonably utilizing bandwidth resources. It provides significant scientific value and application potential in the field of the IoVs.

A Comprehensive examination of utilizing neutrosophic parameters in manufacturing industry through queueing approach

Queueing theory plays a pivotal role in analyzing the dynamics of manufacturing systems subject to random arrivals and service times. The M/G/1 queueing model, which represents a single-server queue with general distributions for inter-arrival and service times, is fundamental in this regard. However, traditional queueing models often fail to account for the inherent uncertainties encountered in real-world manufacturing scenarios. Neutrosophic theory provides a robust framework for modeling indeterminacy, ambiguity, and inconsistency in queueing parameters, thereby offering a more adaptable and precise depiction of system behavior. This research investigates the utilization of Neutrosophic sets in defining arrival rates, service times, and queue length distributions within the M/G/1 framework tailored to manufacturing settings. By employing numerical examples and comparative analyses, the study explores the effects of Neutrosophic parameters on key performance indicators such as mean waiting time, queue length distribution, and server utilization, considering distributions like Exponential, Erlang, and Deterministic. Additionally, it delves into the implications of integrating Neutrosophic parameters into queueing theory, providing valuable insights into improved decision-making and system optimization across various manufacturing operational contexts.

Efficient microservices offloading for cost optimization in diverse MEC cloud networks

In recent years, mobile applications have proliferated across domains such as E-banking, Augmented Reality, E-Transportation, and E-Healthcare. These applications are often built using microservices, an architectural style where the application is composed of independently deployable services focusing on specific functionalities. Mobile devices cannot process these microservices locally, so traditionally, cloud-based frameworks using cost-efficient Virtual Machines (VMs) and edge servers have been used to offload these tasks. However, cloud frameworks suffer from extended boot times and high transmission overhead, while edge servers have limited computational resources. To overcome these challenges, this study introduces a Microservices Container-Based Mobile Edge Cloud Computing (MCBMEC) environment and proposes an innovative framework, Optimization Task Scheduling and Computational Offloading with Cost Awareness (OTSCOCA). This framework addresses Resource Matching, Task Sequencing, and Task Scheduling to enhance server utilization, reduce service latency, and improve service bootup times. Empirical results validate the efficacy of MCBMEC and OTSCOCA, demonstrating significant improvements in server efficiency, reduced service latency, faster service bootup times, and notable cost savings. These outcomes underscore the pivotal role of these methodologies in advancing mobile edge computing applications amidst the challenges of edge server limitations and traditional cloud-based approaches.

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.

Integrated analysis of reliability, power, and performance for IoT devices and servers

The Internet of Things (IoT) is currently widely used in various sectors and spaces. IoT devices are becoming small yet powerful servers and perform server-like functions. Reliability is a critical aspect in both IoT devices and servers, as they work together to create a robust and dependable IoT ecosystem. Power and performance are two other major considerations of an IoT system. Modeling, analysis, evaluation, and optimization of reliability, power, and performance for IoT devices and servers are major components in IoT systems development and deployment. In this paper, we conduct an integrated study of reliability, power, and performance for IoT devices and servers by mathematically rigorous modeling and analysis. The contributions of the paper can be summarized as follows. We establish a continuous-time Markov chain (CTMC) model that incorporates server failure rate, server repair rate, task arrival rate, and task processing rate. Using such an analytical model, we can calculate the server availability, the average task response time, and the average power consumption. We point out that there is an optimal server speed that minimizes the power-time product and a combined cost-performance metric of power, performance, and reliability. We show the impact of server reliability on response time, power consumption, server utilization, and the power-performance tradeoff. To the best of the author’s knowledge, this is the first paper that takes a combined approach to modeling and analysis of reliability, power, and performance for IoT devices and servers. It has been noticed that there has been little such theoretically solid investigation in the existing literature. Therefore, this paper has made tangible contributions and significant advances in the joint understanding of reliability, power, performance, and their interplay in IoT devices and servers quantitatively and mathematically.

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.

Data Secure De-Duplication in Cloud Environment

In the current area of information explosion, users’ demand for data storage is increasing, and data on the cloud has become the first choice of users and enterprises. Cloud storage facilitates users to backup and share data, effectively reducing users’ storage expenses. As the duplicate data of different users are stored multiple times, leading to a sudden decrease in storage utilization of cloud servers. Data stored in plaintext form can directly remove duplicate data, while cloud servers are semi-trusted and usually need to store data after encryption to protect user privacy. In this paper, we focus on how to achieve secure re-duplication and recover data in ciphertext for different users, and determine whether the indexes of public key searchable encryption and the matching relationship of trapdoor are equal in cipher text to achieve secure de-duplication. For the duplicate file, the data user’s re-encryption key about the file is appended to the ciphertext chain table of the stored copy. The cloud server uses the re-encryption key to generate the specified transformed ciphertext, and the data user decrypts the transformed ciphertext by its private key to recover the file. The proposed scheme is secure and efficient through security analysis and experimental simulation analysis.

Ensuring Privacy and Recovery: Public Key Encryption for Secure Data De-Duplication with Keyword Search

In the current era of information explosion, user’s demand for data storage is increasing, and data on the cloud has become the first choice of users and enterprises. Clous storage facilitates the backup and sharing of data, efficiently reducing user’s storage expenses. The duplicate data of different users is stored multiple times, leading to a sudden decrease in the storage utilization of cloud servers. Data stored in plaintext form can directly remove duplicate data, while cloud servers are semi-trusted and usually need to achieve secure de-duplication and recover data in cipher text for different users, and relationship of trapdoor are equal in cipher text to achieve secure de-duplication. The proposed scheme is secure and efficient through security analysis and experimental simulation analysis. Keywords: Data De-duplication, Mobile Edge Computing, Authentication, Security, Cryptographic Techniques, Data Integrity.

Use of pure recombinant human enzymes to assess the disease-causing potential of missense mutations in urea cycle disorders, applied to N-acetylglutamate synthase deficiency.

N-acetylglutamate synthase (NAGS) makes acetylglutamate, the essential activator of the first, regulatory enzyme of the urea cycle, carbamoyl phosphate synthetase 1 (CPS1). NAGS deficiency (NAGSD) and CPS1 deficiency (CPS1D) present identical phenotypes. However, they must be distinguished, because NAGSD is cured by substitutive therapy with the N-acetyl-L-glutamate analogue N-carbamyl-L-glutamate, while curative therapy of CPS1D requires liver transplantation. Since their differentiation is done genetically, it is important to ascertain the disease-causing potential of CPS1 and NAGS genetic variants. With this goal, we previously carried out site-directed mutagenesis studies with pure recombinant human CPS1. We could not do the same with human NAGS (HuNAGS) because of enzyme instability, leading to our prior utilization of a bacterial NAGS as an imperfect surrogate of HuNAGS. We now use genuine HuNAGS, stabilized as a chimera of its conserved domain (cHuNAGS) with the maltose binding protein (MBP), and produced in Escherichia coli. MBP-cHuNAGS linker cleavage allowed assessment of the enzymatic properties and thermal stability of cHuNAGS, either wild-type or hosting each one of 23 nonsynonymous single-base changes found in NAGSD patients. For all but one change, disease causation was accounted by the enzymatic alterations identified, including, depending on the variant, loss of arginine activation, increased Km Glutamate, active site inactivation, decreased thermal stability, and protein misfolding. Our present approach outperforms experimental in vitro use of bacterial NAGS or in silico utilization of prediction servers (including AlphaMissense), illustrating with HuNAGS the value for UCDs of using recombinant enzymes for assessing disease-causation and molecular pathogenesis, and for therapeutic guidance.

A novel single-channel edge computing LoRa gateway for real-time confirmed messaging

LoRaWAN has become the technology of choice for increasing Internet of Things applications owing to its long range and low power consumption characteristics. However, in the uplink confirmed messaging cases, the entire retransmission could take several seconds, so it cannot be used in scenarios that require rapid confirmed messaging, such as emergency alerting and real-time controlling applications. Nevertheless, there has been limited work targeting this issue. This study presents a novel LoRaWAN gateway using edge computing to expedite the confirmed messaging process by generating the acknowledgment (ACK) locally, so that the confirmed messaging time can be significantly reduced. Additionally, the resource utilization of the network server can also be decreased due to the use of edge computing. We verified the effectiveness of our solution through extensive simulations and experiments. The confirmed messaging time between the end nodes and the gateway averaged 43 ms for a maximum of 2 retransmissions. With the adoption of edge computing on the gateway, the network server’s central processing unit (CPU), memory, and bandwidth peak utilization decrease from 53.51 to 39.46, 73.88 to 72.11%, and 4422.68 kbps to 3271.27 kbps, respectively. In addition, the network server’s system load decreases from 2.15 to 1.69, while the gateway cost is reduced by almost $\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\$$$\\end{document}38 compared to the benchmark products.

Piracy? What Piracy?

In discussing piracy herein, the reference is not to historical maritime raiders bestowed with titles by the British Empire, such as Sir Francis Drake, who appropriated gold from the Spaniards, themselves having previously stolen it from indigenous peoples ("Indians"). The act of pilfering from thieves is presently regarded with skepticism and presents distinct ethical quandaries. Rather, my focus pertains to instances wherein scientific and medical publications/ideas have been unlawfully appropriated. One might assume that such occurrences are antiquated or, at the very least, obsolete in contemporary times. Regrettably, they persist. Wherever opportunities emerge, certain transgressions ensue, irrespective of their moral permissibility. The adage "opportunity makes a thief" holds true. Within this discourse, I will scrutinize and evaluate specific behaviors exhibited by authors affiliated with publishing entities and those commonly termed "pirates". Various measures exist to comprehensively safeguard intellectual property against misappropriation. These encompass strategies such as confidentiality, distrust, heightened security awareness, precautionary measures, exercise of prudence, and the utilization of pre-print servers, among others.

Model Utilisasi Dan Visualisasi Resource Menggunakan Prometheus Dan Grafana Untuk Pengelolaan Server Di Universitas Udayana

Monitoring utilization server which refers to the use of server resources such as CPU, memory and storage is important in managing Information Technology (IT) infrastructure by network administrators in an effort to help maximize server performance, identify performance constraints and make decisions to increase efficiency servers. Monitoring is done by checking resource usage one by one on the server to find out which server resources are running. However, the more servers there are, the more time it takes to monitor so that it can interfere with the performance of network administrators. To make it easier for network administrators to monitor, researchers created a resource utilization and visualization model using Prometheus and Graphana which can display server resources without having to check directly on the server you want to monitor. The final result of the research produced a model that can visualize server resources in a dashboard without the need to check the servers one by one so that it helps network administrators work to monitor server resource usage.

Big data-based digital management system for the whole process of financial reporting in SMEs

Abstract Based on big data technology, this paper designs a digital management system for the whole process of financial reimbursement and gives the automatic transformation process of digitalization of financial reimbursement claims and accounting documents. In the K-Means algorithm, the AHC algorithm idea is introduced to construct the DH-K-Means clustering algorithm, which in turn enables the financial reimbursement behavioral portrait of enterprise employees to be constructed. To confirm the effectiveness of the system application in this paper, performance testing and portrait analysis were carried out. The results show that the maximum memory utilization of the database server is 50.42% when the number of concurrent users is 100, and the average response time of the system reaches a maximum of 825.63ms when the number of concurrent users is 200. Based on big data, the whole process of financial reporting digital management system has good stability, which can make enterprises more aware of their financial situation and then develop appropriate financial reimbursement strategies.

Research on the Construction of Multimodal Integration Resource Library for Online Teaching and Learning for Adult Education

Abstract This paper aims to construct a resource base for adult online education using a multimodal fusion basis. The fusion processing methods of knowledge resources incorporate temporal and non-temporal feature learning. This paper proposes a process for constructing a multimodal subject knowledge space that can aid educators in organizing and presenting multimodal teaching content effectively. Also, the operation process of each major functional module of the adult online education resource library, including the online teaching process, online learning process and online resource production process, is studied in detail. Finally, the performance test of optimizing small file storage, the performance test of de-duplication of redundant data of learning resources and the stress test were conducted on the constructed resource library. The study concluded that the resource memory occupancy of the computer class was reduced by 0.35 after the de-duplication process, and in the stress test, the login response time was less than 8 seconds, and the average CPU utilization of the database server was <50%.

Task Offloading of Intelligent Building Based on Dependency-Aware in Edge Computing

Background: With the rapid development of artificial intelligence, the traditional cloud computing model has serious bandwidth and energy consumption problems when storing and processing massive amounts of raw data. To address this problem, recent patents have investigated methods for intelligent task offloading and allocation in mobile edge computing. Objective: A Directed Acyclic Graph (DAG) task unloading model is established to reduce the problem of task delay and energy consumption in edge networks. At the same time, the Modified Tuna Swarm Optimization (MTSO) is used to improve the execution efficiency. Methods: Firstly, this paper integrates (i) inter-task dependencies; (ii) heterogeneity of computing resources in the edge network; and (iii) interference of wireless channels in the edge network. A DAG task offloading model is developed to reduce latency and energy consumption. The end users are guided to offload their tasks/sub-tasks to the most appropriate servers in the edge network, thus minimizing the end-to-end latency of all tasks in the edge network. Secondly, the MTSO algorithm is used to coordinate the dependencies and priorities of subtasks to improve execution efficiency. Results: The experimental results show that when the number of users including subtasks is 10, the final edge server utilization rate is as high as 92%. A more fine-grained segmentation scheme can reduce the average delay of tasks and improve the utilization rate of edge servers. Conclusion: The approach proposed in this paper reduces the end-to-end latency and improves resource utilization in complex applications under the premise of ensuring task dependency. It well relieves the pressure on the cloud and has certain engineering application value

Joint scheduling and offloading of computational tasks with time dependency under edge computing networks

Mobile edge computing is an emerging technology paradigm that improves mobile devices’ computational power and efficiency by offloading computational tasks to edge servers. However, there are still some problems: (1) Ignoring the complex time dependencies of compute-intensive or latency-sensitive tasks, leading to the omission of offload scheduling opportunities for fine-grained sub-tasks within a task, thus reducing the computational power of the mobile device and the utilization of edge resources; (2) Overly biased towards optimizing task execution latency and ignoring the balance between data transmission latency and energy loss, reducing the scheme versatility in natural computing environments. Aiming at the above problems, this paper models the complex dependencies, transmission time, and execution time between computing tasks, constructs a mathematical model of the scheduling and offloading process of computing tasks, and a joint task scheduling and offloading method (N-JSU) based on Nash equilibrium is designed. This offloading strategy is divided into two steps: first, the average latency of each task is analyzed, and the scheduling order of the computational tasks is determined based on the highest response ratio of the tasks; secondly, the optimal solution is determined by iterative search to offload the most suitable IoT tasks or computing tasks to the edge server. The experimental results show that this offloading strategy can effectively reduce the latency of computationally intensive edge network tasks and improve the resource utilization of the edge server.

Intelligent Computation Offloading and Resource Allocation in IIoT With End-Edge-Cloud Computing Using NSGA-III

The Industrial Internet of things (IIoT), which consists of massive IoT devices and wireless access points in industrial infrastructures to acquire intelligent services, has been considered as a vital physical information platform for implementing Industry 4.0. Further, mobile edge computing (MEC) has brought an opportunity to accelerate the development of IIoT. However, the existing MEC methods may not be directly used for IIoT scenarios due to the large size of IIoT devices and the characteristics of the applications, as well as the limited and heterogeneous resources of edge servers. In view of this, the computation offloading and resource allocation are formulated as a multi-objective optimization problem, and an end-edge-cloud collaborative intelligent optimization method is devised in this paper. Comprehensive experiments and evaluations are carried out to prove the effectiveness and efficiency of our proposed method with regard to the energy consumption and time consumption of IIoT devices, as well as resource utilization and load balancing of edge servers.

Study on Effects of Operating Parameters on a Water-Cooled Loop Thermosyphon System under Partial Server Utilization

During the operation of a data center, servers are gradually installed in racks, causing most racks to work under a low heating load for a long time and affecting the cooling efficiency of the loop thermosyphon system (LTS). Thus, the effects of operating parameters on the thermal performance should be investigated. In this study, a water-cooled LTS was experimentally investigated under different airflow rates and heating loads. The results show that the additional liquid refrigerant reduced the heat transfer performance and aggravated a drop in cooling capacity when the airflow rate and heating load were decreased. To further reveal the effects of the operating parameters on the thermal performance and cooling efficiency, a steady-state distributed-parameter model was developed and validated based on the experimental data. The results show that the excessive cooling capacity was reduced by decreasing the airflow rate according to the upper limit of the server exhaust air temperature under partial server utilization. The excessive cooling capacity was reduced by 14.5–52.1% under 5–56.5% server utilization. To further reduce the excessive cooling capacity while ensuring thermal security, the water side operating parameters (including the supply chilled water temperature and water flow rate) were adjusted according to the upper limit of the rack’s average outlet air temperature, which reduced the excessive cooling capacity by more than 23.8% under partial server utilization.

POLSTM: Poplar optimization-based long short term memory model for resource allocation in cloud environment

Due to the evolution of Internet of Things (IoT) paradigm, the number of devices is growing day by day which arises the stringent requirements for various communications. The standard cloud computing model is not capable of efficiently hosting IoT tasks due to the high latency associated with it. Hence, this work utilizes the mobile edge computing and fog concept to process the input tasks independent of the cloud layer. Generally, the high computation costs and energy consumption resulted in the applications such as power-hungry and computation-intensive, which become massive challenges for an IoT device. Motivated by the previous research, we mainly plan to investigate the resource allocation issues that arise in the MEC-enabled IoT–Fog–cloud architecture. We propose a Poplar Optimization algorithm (POA) based on Attention 1DCNN-LSTM architecture (POA-A1DCNN-LSTM) for improving the total utility of the MEC servers by optimizing the energy consumption and task delay. Initially, the POA algorithm is implemented for cluster head (optimal fog node) selection using the node degree, node distance, and residual energy. Next, the A1DCNN-LSTM architecture is employed for task offloading by selecting the fog node with minimal task length and processing delay. The performance of the proposed method is validated by average latency, user satisfaction, network lifetime, energy consumption, average time delay, and normalized system utility. The experimentation results revealed that the proposed method attained better effectiveness in different metrics by achieving 1.5 ms, 0.65, 60 s, 0.45 j, 385 ms, and 0.98 compared to state-of-the-art methods.The experimentation outcomes demonstrate the effectiveness of the proposed POA-A1DCNN-LSTM architecture to decrease task completion delay and offer effective task scheduling as well as resource allocation performances in the MEC-enabled IoT–Fog–cloud network.