CPU Usage Research Articles

Exploring the Impact of Resource Management Strategies on Simulated Edge Cloud Performance: An Experimental Study

Edge computing has emerged as a critical technology for meeting the needs of latency-sensitive applications and reducing network congestion. This goal is achieved mainly by distributing computational resources closer to end users and away from traditional data centers. Optimizing the utilization of limited edge cloud resources and improving the performance of edge computing systems requires efficient resource-management techniques. In this paper, we primarily discuss the use of simulation tools—EdgeSimPy in particular—to assess edge cloud resource management methods. We give a summary of the main difficulties in managing a limited pool of resources in edge cloud computing, and we go over how simulation programs like EdgeSimPy work and evaluate resource management algorithms. The scenarios we consider for this evaluation involve edge computing while taking into account variables like user location, resource availability, and network structure. We evaluate four resource management algorithms in a fixed, simulated edge computing environment to determine their performance regarding their CPU usage, memory usage, disk usage, power consumption, and latency performance metrics to determine which method performs better in a fixed scenario. This allows us to determine the optimal algorithm for tasks that prioritize minimal resource use, low latency, or a combination of the two. Furthermore, we outline areas of unfilled research needs and potential paths forward for improving the reliability and realism of edge cloud simulation tools.

Comparative analysis of performance, efficiency, and resource usage between COMSOL Multiphysics and the MPh library of Python in the simulation of physical phenomena

MPh is a library of Python that makes possible to link the Python computer language with COMSOL Multiphysics. The use of the MPh library opens the possibility to save the computer resources employed when simulating physical phenomena and solving mathematical models and equations. In the Python command interpreter is possible to change or adjust some settings and parameters from the models created in COMSOL, and to execute the COMSOL kernel to solve those models. In this study, we compare the performance of COMSOL Multiphysics and the MPh library of Python when computing the magnetic field generated by a distribution of currents and ferromagnetic material. The metrics employed to do the comparison and the methodology to measure them are described, as well as the computer equipment where the programs ran. The results show that the execution time of the computations are similar in both software, but in the rest of the metrics, the execution in Python surpassed the execution in COMSOL. We showed that the MPh library of Python demands less CPU and RAM usage when solving the mathematical models and the files containing the solutions use less storage memory. As a conclusion, we propose the use of the MPh library of Python to improve the performance of the computer in charge of carrying out the calculations.

BCAS: Blockchain-based secure access and sharing scheme for EHR data

Sharing Electronic Health Record (EHR) data is critical for improving medical decision-making and emergency care. However, EHRs contain sensitive patient information, making data breaches and misuse a significant concern during sharing. To tackle this challenge, we propose the Blockchain-Based Secure Access and Sharing (BCAS) scheme, a patient-centered, secure, and efficient solution for large-scale EHR access and sharing that intelligently combines blockchain with proxy re-encryption. Four smart contracts are designed for the BCAS to achieve effective user identity registration, precise virtual identity verification, strict access control, and secure data management. The adoption of hybrid on-chain and off-chain storage structures, coupled with the four smart contracts, effectively optimizes the efficiency of the blockchain in BCAS. Proxy re-encryption further enhances data confidentiality and streamlines EHR sharing among authorized users. The security analysis indicates that BCAS can resist DDoS, spoofing, and man-in-the-middle attacks, ensuring the privacy and security of patient EHR data. The performance evaluation demonstrates that the CPU usage of smart contracts does not exceed 15%. The on-chain and off-chain storage structures require less than 30s for uploading and downloading an 800MB file. Additionally, the proxy re-encryption scheme can process a 10MB file in less than 43ms. These results indicate that BCAS offers efficient performance without compromising data security and privacy. Compared with existing blockchain-based EHR sharing systems, BCAS offers superior performance, a more comprehensive solution, and increased resistance to attacks.

A Comparative Study of CPU and GPU Power Consumption while using Open-Source and Proprietary Media Players

This study presents a comparative analysis of power consumption between open-source and proprietary media players when playing open-media format videos (.webm). As media consumption grows, energy-efficient software is critical for both environmental sustainability and device performance. Using tools like HWiNFO, key metrics such as GPU and CPU power consumption, memory usage, and efficiency were evaluated for popular open-source (e.g., VLC, Kodi) and proprietary (e.g., GOM Player, KMPlayer) players. The results reveal that open-source players generally consume less GPU power but more CPU resources, while proprietary players balance CPU and GPU usage with higher memory demands. The findings suggest that careful selection of media players can lead to significant energy savings over time, offering insights for developers and users focused on energy-efficient computing.

Implementation of concurrency control mechanisms to enhance the performance of multi-threaded applications

Concurrency control is a crucial aspect of multi-threaded application development, directly impacting performance and resource utilization.This research project investigates the impact of various concurrency control mechanisms on the performance of multi-threaded applications, specifically focusing on mutexes, semaphores, and lock-free data structures implemented using queues. Conducted in a Python environment within Google Colab, the study aims to assess performance metrics such as execution time, cpu usage, and memory usage. Each mechanism’s performance was evaluated through repeated trials, and the results were aggregated and averaged to ensure reliability. By systematically evaluating these concurrency control mechanisms, the project seeks to understand how each approach affects the efficiency of applications in a multi-threaded context. The findings reveal that the queue mechanism provides the fastest execution time, the semaphore mechanism, on the other hand, exhibited the lowest CPU usage, and the mutex mechanism offered a balanced performance in both speed and CPU efficiency. Memory usage was similar in mutex and semaphore, but queue differed significantly from the other two. These results show that the choice of concurrency control mechanism should be guided by the specific requirements of the application, considering factors such as workload nature and system architecture.

Enhancing Edge Environment Scalability: Leveraging Kubernetes for Container Orchestration and Optimization

ABSTRACTKubernetes is an open‐source container orchestration platform, offers a comprehensive suite of features for managing containerized applications effectively. These features encompass horizontal scaling, per‐node‐pool cluster scaling and automated resource request adjustments. This research endeavors to harness these capabilities to address the limitations experienced by fog servers in edge environments, particularly those arising from restricted network connectivity and scalability challenges. In this research paper, the primary focus is on Kubernetes role of enhancing scalability, providing a robust framework for managing containerized applications. The proposed approach involves creating a predefined number of pods and containers within a Kubernetes cluster, specifically designed to efficiently handle incoming requests while optimizing CPU and memory usage. This method implements a microservice architecture for the web tier, with separate pods for the front end, back end and database, ensuring modular and scalable design. All pods communicate and integrate through REST APIs, facilitating seamless interaction and data exchange between the services. When handling web requests, the approach enables and controls both internal and external networks, ensuring secure and efficient communication. The analysis then examines the CPU and memory utilization of the pods, as well as node bandwidth, to provide a comprehensive evaluation of container scalability and performance within the Kubernetes cluster. These findings effectively demonstrate Kubernetes' capability in managing container scalability and optimizing resource utilization, highlighting its efficiency and robustness in a microservice environment.

RT-APT: A real-time APT anomaly detection method for large-scale provenance graph

Advanced Persistent Threats (APTs) are prevalent in the field of cyber attacks, where attackers employ advanced techniques to control targets and exfiltrate data without being detected by the system. Existing APT detection methods heavily rely on expert rules or specific training scenarios, resulting in the lack of both generality and reliability. Therefore, this paper proposes a novel real-time APT attack anomaly detection system for large-scale provenance graphs, named RT-APT. Firstly, a provenance graph is constructed with kernel logs, and the WL subtree kernel algorithm is utilized to aggregate contextual information of nodes in the provenance graph. In this way we obtain vector representations. Secondly, the FlexSketch algorithm transforms the streaming provenance graph into a sequence of feature vectors. Finally, the K-means clustering algorithm is performed on benign feature vector sequences, where each cluster represents a different system state. Thus, we can identify abnormal behaviors during system execution. Therefore RT-APT enables to detect unknown attacks and extract long-term system behaviors. Experiments have been carried out to explore the optimal parameter settings under which RT-APT can perform best. In addition, we compare RT-APT and the state-of-the-art approaches on three datasets, Laboratory, StreamSpot and Unicorn. Results demonstrate that our proposed method outperforms the state-of-the-art approaches from the perspective of runtime performance, memory overhead and CPU usage.

SBAC-SDN: A Scalable Blockchain-based Access Control in Northbound Interface for Multi-Controller SDN with Load Balancing Mechanism

Abstract The rapid adoption of Software-Defined Networking (SDN) has not only revolutionized network management but also presented challenges in scalability and security, particularly in multi-controller environments. This paper introduces a load balancing technique aimed at enhancing the scalability of the Northbound interface, a critical component often susceptible to performance bottlenecks. The experimental setup utilized Mininet and Ryu to create a simulated multi-controller SDN environment, where SBAC-SDN was thoroughly evaluated based on CPU and memory usage, response times, and error rates. The results showed substantial improvements in critical performance indicators, confirming the effectiveness of the load balancing technique in addressing scalability challenges. The findings indicate that the system can be scaled without excessive resource consumption, as CPU usage peaked at 60.3% and memory usage remained below 8 GB even with eight controllers. The stable average response times were consistently below 1 s, and the high throughput, ranging from 4.34 to 4.67 requests per second, further demonstrates the efficiency of the system. The error rate remained 0.0% across all configurations, highlighting the robustness of the implemented security measures. This study contributes to ongoing efforts to improve the scalability and overall robustness of SDNs, aligning with the broader goals of reliability and efficiency in network management.

AI for Cloud Cost Management: Predictive and Prescriptive Analytics

With reference to the previous information and literature review, the purpose of this research paper is to evaluate the impact of multithreading concerning computer resource utilization and performance by undertaking and comparing various crucial multithreading techniques, including work stealing, fork join, and the use of the thread pool control. In an experimental context, benchmark applications that can be characteristic of several domains were evaluated in a controlled multicore computing platform. Time, CPU occupation, memory usage and throughputs were determined systematically and compared according to the various workloads. The results highlight the superiority of the work-steing algorithm in keeping the execution time and CPU usage low compared to the rest as a sign of its efficiency in managing work loads. Further, the memory usage and the throughput statistics showed the degree of inefficiency of each algorithm and performance penalty. Interviews with developers elaborated on more practical issues of multithreading as the generally rigid nature of the problems suggested that optimal solutions called for adaptive measures in practice. Based on the results achieved in this research, developers and researchers can increase their knowledge about available multithreading algorithms and make suggestions to choose and apply them with high efficiency and low resource consumption.

Environmental sound classification on an embedded hardware platform

Convolutional neural networks (CNNs) have exhibited state-of-the-art performance in various audio classification tasks. However, their real-time deployment remains a challenge on resource constrained devices such as embedded systems. In this paper, we analyze how the performance of large-scale pre-trained audio neural networks designed for audio pattern recognition changes when deployed on a hardware such as a Raspberry Pi. We empirically study the role of CPU temperature, microphone quality and audio signal volume on performance. Our experiments reveal that the continuous CPU usage results in an increased temperature that can trigger an automated slowdown mechanism in the Raspberry Pi, impacting inference latency. The quality of a microphone, specifically with affordable devices such as the Google AIY Voice Kit, and audio signal volume, all affect the system performance. In the course of our investigation, we encounter substantial complications linked to library compatibility and the unique processor architecture requirements of the Raspberry Pi, making the process less straightforward compared to conventional computers (PCs). Our observations, while presenting challenges, pave the way for future researchers to develop more compact machine learning models, design heat-dissipative hardware, and select appropriate microphones when AI models are deployed for real-time applications on edge devices.

A Comparative Analysis of Power Consumption While Using Open-Source and Proprietary Media Players

This study investigates the power consumption of various media player software applications, comparing open-source and proprietary options. The experiment measured the average power consumption of CPU, GPU, and memory usage of media players such as Kodi, MPC, MPV, SMP, VLC, Windows Media Player, ACG, ALLPlayer, GOM, KMPlayer, LAPlayer, POTPlayer, and RealPlayer while playing 4K video. The results revealed that proprietary media players generally consume less power compared to their open-source counterparts. Statistical analysis, including descriptive statistics and independent samples t-tests, confirmed these findings. Long-term power consumption projections indicated substantial energy savings with more efficient media players. These findings underscore the importance of considering energy efficiency in software selection for sustainable computing.

ECDSA-secured blockchain architecture for interoperable healthcare in the Internet of Robotic Things (IoRT)

ABSTRACT The ever-growing Internet of Robotic Things (IoRT) in healthcare settings introduces new challenges in securing sensitive patient data. Existing communication networks within IoRT environments are vulnerable to security breaches. This paper proposes an ECDSA-secured blockchain architecture specifically designed to address these challenges. A key innovation is the implementation of a private blockchain network on ZedBoard development boards for access control and data integrity verification. IoT devices collect patient health data, which is then securely transmitted to a Mobius server for storage, minimizing resource impact on ZedBoards. To further strengthen the security framework, the architecture integrates the Elliptic Curve Digital Signature Algorithm (ECDSA) with Radix- 2 w optimizations. These optimizations result in a 28% reduction in CPU usage and an 8.96% reduction in memory usage, significantly enhancing cryptographic performance. Practical implementation validates the effectiveness of the proposed approach, providing a robust solution for securing IoRT networks. This research offers valuable insights for industries reliant on the seamless integration of IoT and robotics and concludes with key findings and future research directions, including exploring energy-efficient cryptographic protocols, enhancing scalability through advanced consensus mechanisms, and developing user-friendly interfaces for managing IoRT networks.

Artificial Intelligence Based Multi-Layer Approach for Finding Unknown Attacks in Cloud Network by Using Hybrid Intrusion Detection

Objectives : The objective of this study is to explore Intrusion Detection Systems and their various types by gathering research from previously published articles in refereed journals. The focus is on developing a proposed model capable of identifying unknown attacks in cloud networks using Signature and Anomaly-based Intrusion Detection Systems. Subsequently, the efficiency of the proposed model will be assessed, and a comparison with existing models will be conducted. The paper's main objective is to identify unknown attacks in a cloud network using a combination of signature and anomaly-based intrusion detection systems in an artificial intelligence-based multi-layered approach. Methods: Leveraging insights from existing literature, the proposed model combines signature-based IDS for known threat detection and anomaly-based IDS for detecting unusual behavioral patterns indicative of new or unseen attacks. Experimental evaluations using NSL-KDD and ADFA datasets demonstrate competitive accuracy and detection rates, with the proposed artificial intelligence-based Hybrid IDS achieving high performance in detecting both normal and malicious activities. Findings: This model produces above 90%, 96%, and 98% efficiency in the wired, Wireless, and Cloud networks respectively, and this model finds known attacks effectively while using parameters like event logs, file transferring time, TCP and UDP addresses, CPU Usage, Weak and synthetic data, IP and MAC address. Existing literature said that the existing model using the Hybrid Intrusion detection model can identify unknown attacks with a maximum of 80%, 92%, and 96% accuracy respectively. The findings suggest that the artificial intelligence-based multi-layered approach offers a promising solution for enhancing cloud network security, with the potential for further optimization and integration of advanced technologies in future research endeavors. Novelty: This study presents an artificial intelligence-based multi-layered approach for detecting unknown attacks in cloud networks by integrating signature-based and anomaly-based intrusion detection systems (IDS). The authors developed the model to detect the intrusion by using the Behaviour Profiling algorithm and dynamically prevent the data from intrusion by using the Statistical approach model. The authors trying to find unknown attacks, therefore the authors defined the objective of this paper as to find the unknown attacks in cloud networks by using the combination of signature and anomaly-based intrusion detection systems. The objective is to develop a model capable of effectively identifying cyber threats in cloud environments. The existing models do not concentrate on identifying unknown attacks by using Signature-based Intrusion Detection. Very few of the literature said that known attacks can be identified easily by using Signature-based Intrusion Detection but the unknown attacks identifying process is hard. Some of the Literature said that using the Hybrid Intrusion detection model can identify unknown attacks with a maximum of 80%, 92%, and 96% accuracy respectively. The current paper identifies unknown attacks in the cloud network using a combination of signature and anomaly-based intrusion detection systems in an artificial intelligence-based multi-layered approach and it produced above 97% accuracy. The unique feature of the model is artificial intelligence-based multi-layered approach and dynamic key have been utilized to avoid malicious activities in the network. Keywords: Anomaly based IDS, Cloud Network Security, Hybrid IDS, Multi-Layer Approach, Signature based IDS

Application of Proximal Policy Optimization for Resource Orchestration in Serverless Edge Computing

Serverless computing is a new cloud computing model suitable for providing services in both large cloud and edge clusters. In edge clusters, the autoscaling functions play a key role on serverless platforms as the dynamic scaling of function instances can lead to reduced latency and efficient resource usage, both typical requirements of edge-hosted services. However, a badly configured scaling function can introduce unexpected latency due to so-called “cold start” events or service request losses. In this work, we focus on the optimization of resource-based autoscaling on OpenFaaS, the most-adopted open-source Kubernetes-based serverless platform, leveraging real-world serverless traffic traces. We resort to the reinforcement learning algorithm named Proximal Policy Optimization to dynamically configure the value of the Kubernetes Horizontal Pod Autoscaler, trained on real traffic. This was accomplished via a state space model able to take into account resource consumption, performance values, and time of day. In addition, the reward function definition promotes Service-Level Agreement (SLA) compliance. We evaluate the proposed agent, comparing its performance in terms of average latency, CPU usage, memory usage, and loss percentage with respect to the baseline system. The experimental results show the benefits provided by the proposed agent, obtaining a service time within the SLA while limiting resource consumption and service loss.

Network security enhancement with advanced techniques in network scanning.

This article details the development and evaluation of an advanced network scanning framework that uses Scapy for packet manipulation and Tkinter for the GUI. The framework aims to improve detection accuracy, resource efficiency, and user interface usability, addressing the shortcomings of traditional methods. Key components include a Network Scanner, Packet Handler, Security Measures, and a GUI, all designed for comprehensive network security. Tested in a controlled environment, the framework achieved a 100% detection rate, surpassing traditional methods, and demonstrated resource efficiency with a 20% reduction in CPU usage and a 60 MB decrease in memory usage. The analysis showed network diversity and low utilization, suggesting capacity for future expansion. The framework's GUI effectively displayed scan results, enhancing usability. The novelty of this research lies in its integration of optimized ARP packet handling with a user-friendly interface, ensuring real-time threat detection and scalability. The study contributes to the advancement of network scanning techniques and proposes future enhancements like machine learning integration for better threat detection.

Development and training of LSTM models for control of virtual distributed systems using TensorFlow and Keras

The subject of this paper is the optimization of resource management in virtual distributed systems (VDS) via the application of machine learning algorithms, specifically Long Short-Term Memory (LSTM) networks. The aim is to develop an effective model for managing VDS using contemporary machine-learning techniques. Objectives are as follows: 1) to describe the problem of resource management challenges in VDS and the architecture of LSTM network.; 2) to collect and normalize historical data on resource usage, such as CPU, memory, disk, and network usage; 3) to develop a detailed architecture for the LSTM model, including input layers, multiple LSTM layers with dropout regularization, dense layers, and an output layer; 3) to train the LSTM model using TensorFlow and Keras, ensuring the training process includes at least 50 epochs, early stopping, and cross-validation techniques; 4) to evaluate the performance of the trained LSTM model using a test set, with MSE as the primary metric; 5) to conduct a thorough analysis of the training and validation outcomes, including the visualization of loss values over epochs. Methods involve designing an LSTM model to capture temporal dependencies and sequential patterns in resource usage data, including input layers, multiple LSTM layers with dropout regularization, dense layers, and an output layer. The normalized dataset was split into training and test sets, and the model was compiled using the Adam optimizer with a learning rate of 0.01 and mean squared error (MSE) as the loss function. The model was trained for 50 epochs with early stopping and cross-validation to prevent overfitting, and its performance was evaluated using MSE on a test set. The following results were obtained: 1) the historical data on resource usage, including CPU, memory, disk, and network usage; 2) the LSTM model demonstrated significant potential in managing VDS by efficiently analyzing and predicting optimal resource configurations; 2) visualization of the training process and revelations on how the model's loss values changed over epochs; 3) A comprehensive LSTM model architecture, including input layers, multiple LSTM layers with dropout regularization, dense layers, and an output layer. Conclusions. The primary contribution of this research is the development and training of LSTM models to optimize resource management in VDS using TensorFlow and Keras. This study presents a comprehensive methodology that includes collecting and normalizing historical resource usage data, designing the LSTM model architecture, training the model, and evaluating its performance. The results demonstrate the significant potential of LSTM models in effectively managing VDS by analyzing temporal dependencies and predicting optimal resource configurations. Specifically, the trained model achieved a mean squared error (MSE) below the target threshold, indicating robust predictive performance. The visualization of the training process revealed insights into overfitting and underfitting, with strategies like early stopping and cross-validation enhancing the model's generalizability. This study highlights the practical applicability of LSTM models, offering automated and optimized solutions for complex IT infrastructures, and laying the groundwork for future improvements in handling diverse and unforeseen data patterns in VDS.

GoibhniUWE: A Lightweight and Modular Container-Based Cyber Range

Cyberattacks are rapidly evolving both in terms of techniques and frequency, from low-level attacks through to sophisticated Advanced Persistent Threats (APTs). There is a need to consider how testbed environments such as cyber ranges can be readily deployed to improve the examination of attack characteristics, as well as the assessment of defences. Whilst cyber ranges are not new, they can often be computationally expensive, require an extensive setup and configuration, or may not provide full support for areas such as logging or ongoing learning. In this paper, we propose GoibhniUWE, a container-based cyber range that provides a flexible platform for investigating the full lifecycle of a cyberattack. Adopting a modular approach, users can seamlessly switch out existing, containerised vulnerable services and deploying multiple different services at once, allowing for the creation of complex and realistic deployments. The range is fully instrumented with logging capabilities from a variety of sources including Intrusion Detection Systems (IDSs), service logging, and network traffic captures. To demonstrate the effectiveness of our approach, we deploy the GoibhniUWE range under multiple conditions to simulate various vulnerable environments, reporting on and comparing key metrics such as CPU and memory usage. We simulate complex attacks which span multiple services and networks, with logging at multiple levels, modelling an Advanced Persistent Threat (APT) and their associated Tactics, Techniques, and Procedures (TTPs). We find that even under continuous, active, and targeted deployment, GoibhniUWE averaged a CPU usage of less than 50%, in an environment using four single-core processors, and memory usage of less than 4.5 GB.

Implementation of Quick Response Code Tags for Livestock Identification and Farm Management Based on Web and Android for Ruminant Farms

Technological advancements impact various sectors of life, including animal husbandry, which is an important part of the education curriculum. Innovations are needed to optimize livestock management, especially ruminant farming, which is crucial for producing meat and milk. SMKN 1 Bawen, with its study program focusing on ruminant farming (cows, sheep, and goats), still uses conventional recording methods, leading to inefficiencies and limited information for potential buyers. To address this, a QR Code-based livestock management and identification system was developed and integrated into a website and Android application using agile methods. Black box testing confirmed that all features function well. Stress testing on the website with 2 Mbps bandwidth showed 98% successful requests and 2% failed requests, with a data transmission speed of 52,816 bits per second and a reception speed of 268,624 bits per second. At 50 Mbps bandwidth, the system had a 100% success rate, with a data sending speed of 44,304 bits per second and a receiving speed of 343,272 bits per second, demonstrating good and stable performance across different bandwidths. The Android application, tested on a POCO X3 with 6 GB RAM and 128 GB internal storage, showed a highest CPU usage of 39%, memory usage of 326 MB, and network speeds of 10.3 KB/s for data received and 535.9 KB/s for data sent. These results indicate that the application runs optimally and is suitable for use.

Intelligent road extraction from high resolution remote sensing images based on optimized SVM

Road recognition and extraction based on high-resolution remote sensing images is currently a hot issue at the forefront of image processing and other disciplines, and its results not only help to enrich geographic information, but also have important application value in national defence construction and other aspects. For the intelligent extraction of roads in high-resolution remote sensing images, the study firstly preprocesses these images, and then extracts the roads by using variational function and support vector machine. As the extraction results are not satisfactory enough, the 3D wavelet transform technique is used to extract the spectral features of roads in the frequency domain, combined with the texture in the time domain, and optimized the support vector machine in order to extract the road images more accurately. The outcomes denote that in contrast with other methods, the optimized method can have optimal correctness and quality while guaranteeing higher integrity. The satellite road image of Chengdu A industrial park is rich in extracted information, having the highest completeness of 94.23%, correctness of 60.73%, and quality of 59.42%, and the satellite road image of Chengdu B industrial park is similarly rich in extracted information, having the highest completeness of 83.47%, correctness of 85.31%, and quality of 72.85%. The highest value of CPU usage for road extraction was 2000 kb at 37 s, and the average value of CPU usage for the whole 60 s was 500 kb. It shows that the research method has good results in high resolution remote sensing image road extraction.

The Design and Real-Time Optimization of an EtherCAT Master for Multi-Axis Motion Control

To address the issues of low bandwidth, weak real-time performance, and poor synchronization in traditional fieldbuses for multi-axis motion control, a solution for the implementation of an EtherCAT master based on the IgH EtherCAT Master open-source software framework and an embedded hardware platform is proposed. On a hardware platform centered around the AM64x Sitara processor, a Linux real-time operating system based on the Xenomai real-time kernel is constructed, and the IgH master framework is ported to realize a high-performance EtherCAT master. The configuration process of the EtherCAT bus is detailed, a master application program is developed, and methods for the real-time performance optimization of the master—such as exclusive CPU usage by the master process and the optimization of the network card driver—are proposed. Finally, experiments are conducted on a six-axis servo control platform, with the packet analysis of the periodic EtherCAT data frames sent by the master. The experimental results show that the optimized master, under a high-speed communication cycle of 500 microseconds, maintains maximum jitter within 20 microseconds and average jitter within 1 microsecond, meeting the requirements for high-precision multi-axis motion control.