Concurrency and Computation-Practice & Experience最新文献_第3页

3D LVCN: A Lightweight Volumetric ConvNet

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-20 DOI: 10.1002/cpe.8312

Xiaoyun Lu, Chunjie Zhou, Shengjie Liu, Jialong Li

{"title":"3D LVCN: A Lightweight Volumetric ConvNet","authors":"Xiaoyun Lu, Chunjie Zhou, Shengjie Liu, Jialong Li","doi":"10.1002/cpe.8312","DOIUrl":"https://doi.org/10.1002/cpe.8312","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent years, with the significant increase in the volume of three-dimensional medical image data, three-dimensional medical models have emerged. However, existing methods often require a large number of model parameters to deal with complex medical datasets, leading to high model complexity and significant consumption of computational resources. In order to address these issues, this paper proposes a 3D Lightweight Volume Convolutional Neural Network (3D LVCN), aiming to achieve efficient and accurate volume segmentation. This network architecture combines the design principles of convolutional neural network modules and hierarchical transformers, using large convolutional kernels as the basic framework for feature extraction, while introducing 1 × 1 × 1 convolutional kernels for deep convolution. This improvement not only enhances the computational efficiency of the model but also improves its generalization ability. The pro-posed model is tested on three challenging public datasets, namely spleen, liver, and lung, from the medical segmentation decathlon. Experimental results show that the proposed model performance has in-creased from 0.8315 to 0.8673, with a reduction in parameters of approximately 5%. This indicates that compared to currently advanced model structures, our proposed model architecture exhibits significant advantages in segmentation performance.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142868741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multi-Constrained Green Routing Protocol for IoT-Based Software-Defined WSN 基于物联网的软件定义 WSN 的多约束绿色路由协议

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-17 DOI: 10.1002/cpe.8306

Nitesh Kumar, Rohit Beniwal

{"title":"A Multi-Constrained Green Routing Protocol for IoT-Based Software-Defined WSN","authors":"Nitesh Kumar, Rohit Beniwal","doi":"10.1002/cpe.8306","DOIUrl":"https://doi.org/10.1002/cpe.8306","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent times, there has been a notable surge in the utilization of Internet of Things (IoT) network devices due to their vast applications. However, this rapid growth has undoubtedly led to raised energy consumption, which, in turn, has raised significant concerns about the environment. Consequently, there is a growing demand for green computing techniques that can mitigate IoT device's energy usage and carbon footprint. Clustering IoT networks is a useful strategy for extending their lifespan. However, clustering presents a complex optimization problem that falls under the category of NP-hard; hence making it a challenging issue. Nevertheless, using meta-heuristics algorithms has greatly improved our ability to tackle such challenges. Therefore, this study introduces a clustering scheme called EQ-AHA, which combines Equilibrium optimization and artificial hummingbird optimization techniques to enhance the efficiency of IoT-based Software-Defined Wireless Sensor Networks (IoT-SDWSN). The primary goal of EQ-AHA is to select the Cluster Heads (CHs) and determine the optimal path between CHs and the Base Station (BS). EQ-AHA employs a fitness function that considers three important factors: the distance between CHs, the distance between nodes and the CHs, and the energy levels of the nodes. Overall, this strategy improves the network's performance by 31.6% compared to other State-of-the-Art (SoA) algorithms.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 28","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Camellia oleifera trunks detection and identification based on improved YOLOv7 基于改进型 YOLOv7 的油茶树干检测和识别技术

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-17 DOI: 10.1002/cpe.8265

Haorui Wang, Yang Liu, Hong Luo, Yuanyin Luo, Yuyan Zhang, Fei Long, Lijun Li

{"title":"Camellia oleifera trunks detection and identification based on improved YOLOv7","authors":"Haorui Wang, Yang Liu, Hong Luo, Yuanyin Luo, Yuyan Zhang, Fei Long, Lijun Li","doi":"10.1002/cpe.8265","DOIUrl":"https://doi.org/10.1002/cpe.8265","url":null,"abstract":"<div>\u0000 \u0000 <p><i>Camellia oleifera</i> typically thrives in unstructured environments, making the identification of its trunks crucial for advancing agricultural robots towards modernization and sustainability. Traditional target detection algorithms, however, fall short in accurately identifying <i>Camellia oleifera</i> trunks, especially in scenarios characterized by small targets and poor lighting. This article introduces an enhanced trunk detection algorithm for <i>Camellia oleifera</i> based on an improved YOLOv7 model. This model incorporates dynamic snake convolution instead of standard convolutions to bolster its feature extraction capabilities. It integrates more contextual information, thus enhancing the model's generalization ability across various scenes. Additionally, coordinate attention is introduced to refine the model's spatial feature representation, amplifying the network's focus on essential target region features, which in turn boosts detection accuracy and robustness. This feature selectively strengthens response levels across different channels, prioritizing key attributes for classification and localization. Moreover, the original coordinate loss function of YOLOv7 is replaced with EIoU loss, further enhancing the model's robustness and convergence speed. Experimental results demonstrate a recall rate of 96%, a mean average precision (mAP) of 87.9%, an F1 score of 0.87, and a detection speed of 18 milliseconds per frame. When compared with other models like Faster-RCNN, YOLOv3, ScaledYOLOv4, YOLOv5, and the original YOLOv7, our improved model shows mAP increases of 8.1%, 7.0%, 7.5%, and 6.6% respectively. Occupying only 70.8 MB, our model requires 9.8 MB less memory than the original YOLOv7. This model not only achieves high accuracy and detection efficiency but is also easily deployable on mobile devices, providing a robust foundation for future intelligent harvesting technologies.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 27","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142674364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Software-Defined Networking With Dynamic Load Balancing and Fault Tolerance Using a Q-Learning Approach 利用 Q 学习方法增强软件定义网络的动态负载平衡和容错能力

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-15 DOI: 10.1002/cpe.8298

Ankit Kumar Jain, Pooja Kumari, Rajat Dhull, Krish Jindal, Shahid Raza

{"title":"Enhancing Software-Defined Networking With Dynamic Load Balancing and Fault Tolerance Using a Q-Learning Approach","authors":"Ankit Kumar Jain, Pooja Kumari, Rajat Dhull, Krish Jindal, Shahid Raza","doi":"10.1002/cpe.8298","DOIUrl":"https://doi.org/10.1002/cpe.8298","url":null,"abstract":"<div>\u0000 \u0000 <p>The Software-Defined Networking (SDN) paradigm represents a fundamental shift in networking by decoupling the control plane from the data plane in network devices. This architectural change offers numerous advantages, including network programmability and centralized management capabilities, which improve scalability and efficiency compared to conventional network architectures. However, the dynamic nature of network traffic presents overload challenges, both temporally and spatially, especially in multi-controller SDN settings. To address these challenges, this paper presents an approach leveraging network traffic patterns for dynamic load balancing. The proposed framework optimizes migration strategies to reduce costs and enhance in-packet request-response rates. By exploiting load ratio variance across controllers, the architecture identifies optimal migration triplets, encompassing migration-in and migration-out domains by selecting a subset of switches. The architecture utilizes online Q-learning technology to achieve optimal controller load balancing while minimizing associated expenses. The proposed approach ensures stability and scalability by imposing limits to maintain maximum efficiency and reduce migration conflicts. It iteratively converges to an optimal policy through a comprehensive set of simulations performed on switches under a wide range of load distribution situations. These results highlight the effectiveness and adaptability of the proposed methodology in addressing the intricacies present in dynamic network settings, encouraging further progress in the field of SDN technologies and their real-world applications.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 28","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Job Scheduling in Hybrid Clouds With Privacy Constraints: A Deep Reinforcement Learning Approach

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-15 DOI: 10.1002/cpe.8307

Haoyang He, Yan Gu, Qingzhi Liu, Hao Wu, Long Cheng

{"title":"Job Scheduling in Hybrid Clouds With Privacy Constraints: A Deep Reinforcement Learning Approach","authors":"Haoyang He, Yan Gu, Qingzhi Liu, Hao Wu, Long Cheng","doi":"10.1002/cpe.8307","DOIUrl":"https://doi.org/10.1002/cpe.8307","url":null,"abstract":"<div>\u0000 \u0000 <p>With the proliferation of cloud computing and the escalating demand for extensive data processing capabilities, an increasing number of enterprises are embracing hybrid cloud solutions. However, as more businesses move toward hybrid clouds, the need for effective solutions to privacy and security concerns becomes increasingly important. Although current scheduling approaches for cloud computing have addressed privacy protection to some extent, few have adequately considered the unique challenges posed by hybrid clouds. To address this gap, we propose a novel approach for scheduling jobs in hybrid clouds that prioritizes privacy protection. Our approach, called PH-DRL, leverages Deep Reinforcement Learning (DRL) to intelligently allocate jobs to virtual machines, optimizing both privacy and Quality of Service (QoS), while minimizing response time. We present the detailed implementation of our approach and our experimental results demonstrate the superior performance of PH-DRL in terms of privacy protection compared to existing methods.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142868590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TCP Congestion Management Using Deep Reinforcement Trained Agent for RED 使用深度强化训练代理进行 RED TCP 拥塞管理

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-14 DOI: 10.1002/cpe.8300

Majid Hamid Ali, Serkan Öztürk

{"title":"TCP Congestion Management Using Deep Reinforcement Trained Agent for RED","authors":"Majid Hamid Ali, Serkan Öztürk","doi":"10.1002/cpe.8300","DOIUrl":"https://doi.org/10.1002/cpe.8300","url":null,"abstract":"<div>\u0000 \u0000 <p>Increasing data transmission volumes are causing more frequent and more severe network congestion. In order to handle spikes in network traffic, a substantially bigger buffer has been included into the system. Bufferbloat, which happens when a bigger buffer is implemented, exacerbates network congestion. Using the transfer control protocol (TCP) congestion management strategy with active queue management (AQM) can fix this issue. As congestion increases, it becomes increasingly difficult to forecast and fine-tune dynamic AQM/TCP systems in order to achieve acceptable performance. To shed new light on the AQM system, we plan to use deep reinforcement learning (DRL) techniques. It is possible that AQM can learn about the appropriate drop policy the same way people do when using a model-free technique like DRL-AQM. After training in a simple network scenario, DRL-AQM is able to recognize complex patterns in the data traffic model and apply them to improve performance in a wide variety of scenarios. Offline training precedes deployment in our approach. In many cases, the model does not require any further parameter tweaks after training. Even in the most complicated networks, AQM algorithms have proven to be effective, regardless of the network's complexity. Minimizing buffer capacity use is an important goal of DRL-AQM. It automatically and continually adjusts to changes in network connectivity.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 28","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Ultra-Short-Term Wind Power Prediction Method Based on Spatiotemporal Characteristics Fusion

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-13 DOI: 10.1002/cpe.8309

Yuzhen Pi, Quande Yuan, Zhenming Zhang, Jingya Wen, Lei Kou

{"title":"An Ultra-Short-Term Wind Power Prediction Method Based on Spatiotemporal Characteristics Fusion","authors":"Yuzhen Pi, Quande Yuan, Zhenming Zhang, Jingya Wen, Lei Kou","doi":"10.1002/cpe.8309","DOIUrl":"https://doi.org/10.1002/cpe.8309","url":null,"abstract":"<div>\u0000 \u0000 <p>Aiming at the problem that the existing ultra-short-term wind power prediction methods lack consideration of the spatial correlation characteristics of wind farms, resulting in insufficient prediction accuracy, an ultra-short-term wind power prediction method based on spatiotemporal characteristics fusion is proposed in this article. First, the fluctuation difference of the time window of wind power is input into the K-means clustering algorithm to cluster wind farms into several clusters based on power fluctuation similarity. Then, the principal component analysis algorithm is used to reduce the dimensionality of numerical weather prediction data combinations in different regions to reduce the impact of redundant information on modeling accuracy. Finally, a convolutional long-short-term memory neural network is designed to extract spatiotemporal features of wind power data and output prediction results. The experimental verification on 18 wind farms in a province in China shows that the proposed wind power prediction method has an average root mean square error of only 0.1257 and has certain applicability.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142868557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of Positioning Error Prediction Results of Industrial Robots Based on Three Different Types of Neural Networks 基于三种不同类型神经网络的工业机器人定位误差预测结果比较

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-13 DOI: 10.1002/cpe.8299

Xin Wang

{"title":"Comparison of Positioning Error Prediction Results of Industrial Robots Based on Three Different Types of Neural Networks","authors":"Xin Wang","doi":"10.1002/cpe.8299","DOIUrl":"https://doi.org/10.1002/cpe.8299","url":null,"abstract":"<div>\u0000 \u0000 <p>With the increasing development of industry, the market demand for manufacturing has shifted to large-scale customized production. This poses new challenges to the production flexibility of industrial robots. The offline programming method can perfectly meet this challenge. But its disadvantage is that it relies heavily on the absolute positioning accuracy of industrial robots. In recent years, there has been an increasing number of studies using neural networks (NN) to predict the positioning errors of industrial robots to improve their absolute positioning accuracy. However, most of these studies only focus on the application of NNs, and do not compare the prediction results and performance of different kinds of NNs. This paper selects three typical network models: backpropagation neural network (BPNN), particle swarm algorithm optimization BPNN (PSO-BPNN), and radial basis function neural network (RBFNN). Through in-depth experiments and analysis of these networks, the purpose is to reveal their respective prediction effects and characteristics and to summarize their advantages and disadvantages. Experimental results show that BPNN performs poorly in predicting positioning errors. As an optimization method, the particle swarm algorithm can effectively improve the prediction performance of BPNN. In contrast, the RBFNN performs well, which makes it very suitable for predicting the positioning error of industrial robots.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 28","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy efficient permanence-based community detection algorithm 基于持久性的高能效社群检测算法

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-13 DOI: 10.1002/cpe.8297

Hardik Saini, Vivek Kumar, Tanmoy Chakraborty

{"title":"Energy efficient permanence-based community detection algorithm","authors":"Hardik Saini, Vivek Kumar, Tanmoy Chakraborty","doi":"10.1002/cpe.8297","DOIUrl":"https://doi.org/10.1002/cpe.8297","url":null,"abstract":"<div>\u0000 \u0000 <p>Detecting an accurate community structure is a crucial task in network analysis. With the increasing popularity of social networking sites, it is essential to have a community detection algorithm that is not only efficien but also cost-effective for running in data centers. There are several metrics for estimating the accuracy of community detection. However, previous research has shown that permanence, a vertex-centric metric, provides the most precise estimate of a community structure compared to other approaches. Despite this, no study has been conducted on parallelizing a permanence-based community detection algorithm and analyzing its energy efficiency. This article introduces Amoeba, a task parallel implementation of a permanence-based community detection algorithm designed for multicore processors. It uses dynamic tasking to schedule the inherent irregular computation, and it can dynamically adapt the total number of parallel threads, which results in improved energy efficiency. We evaluated Amoeba using several real-world and artificial graphs on a multicore server processor. Our experimental results show that Amoeba achieves a geometric mean speedup of 15.3<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>×</mo>\u0000 </mrow>\u0000 <annotation>$$ times $$</annotation>\u0000 </semantics></math> over its sequential implementation, and due to thread adaptability, it achieves energy savings of 12.4% and a speedup of 6% over its nonadaptive implementation.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 28","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Edge Environment Scalability: Leveraging Kubernetes for Container Orchestration and Optimization 增强边缘环境的可扩展性：利用 Kubernetes 进行容器协调和优化

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-10-12 DOI: 10.1002/cpe.8303

K. Aruna, Pradeep Gurunathan

{"title":"Enhancing Edge Environment Scalability: Leveraging Kubernetes for Container Orchestration and Optimization","authors":"K. Aruna, Pradeep Gurunathan","doi":"10.1002/cpe.8303","DOIUrl":"https://doi.org/10.1002/cpe.8303","url":null,"abstract":"<div>\u0000 \u0000 <p>Kubernetes 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.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 28","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0