Concurrency and Computation-Practice & Experience最新文献_第2页

An Effective Load-Balancing Approach for Multicast IPTV Traffic in Multihoming Networks 一种有效的多归属网络组播IPTV业务负载均衡方法

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-04-29 DOI: 10.1002/cpe.70103

Afaf Benaouda Chaht, Mohammed Bencheikh, Chakib Zouaoui, Abdennacer Bounoua, Nasreddine Taleb, Mohamed Naimi

{"title":"An Effective Load-Balancing Approach for Multicast IPTV Traffic in Multihoming Networks","authors":"Afaf Benaouda Chaht, Mohammed Bencheikh, Chakib Zouaoui, Abdennacer Bounoua, Nasreddine Taleb, Mohamed Naimi","doi":"10.1002/cpe.70103","DOIUrl":"https://doi.org/10.1002/cpe.70103","url":null,"abstract":"<div>\u0000 \u0000 <p>Traditional multicast streams are typically sent using unicast routing plans, which follow a single path or, at best, multiple paths of the same cost. However, this method is not efficient for modern networks with various access points. To improve this, we propose a new approach that combines multipath and multicast streams with a load-balancing model. This method aims to enhance multicast traffic transmission in a multipath environment. Our approach separates the Control Plan (CP) from the User Plan (UP). The CP uses Multipath TCP functions, while the UP handles IPTV (Internet Protocol Television) packets, which can be either TCP or UDP. This separation allows for better load distribution, bandwidth conservation, and balanced traffic across available paths. Real-world tests show that all multicast substreams can be smoothly transferred across the network evenly divided and routed through suitable paths whether symmetrical or asymmetrical scénario. Thus, the results of tests on the main performance and reliability parameters such as bandwidth utilization, cross traffic and delay, prove the algorithm's proactivity and responsiveness to network conditions and validate the effectiveness of our approach. In summary, using multicast in multipath TCP networks improves the streaming experience for end users.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888999","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

Weak–Strong Synergy Learning With Random Grayscale Substitution for Cross-Modality Person Re-Identification 基于随机灰度替代的强弱协同学习跨模态人物再识别

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-04-29 DOI: 10.1002/cpe.70101

Zexin Zhang

{"title":"Weak–Strong Synergy Learning With Random Grayscale Substitution for Cross-Modality Person Re-Identification","authors":"Zexin Zhang","doi":"10.1002/cpe.70101","DOIUrl":"https://doi.org/10.1002/cpe.70101","url":null,"abstract":"<div>\u0000 \u0000 <p>Visible-infrared person re-identification (VI-ReID) is a rapidly emerging cross-modality matching problem that aims to identify the same individual across daytime visible modality and nighttime thermal modality. Existing state-of-the-art methods predominantly focus on leveraging image generation techniques to create cross-modality images or on designing diverse feature-level constraints to align feature distributions between heterogeneous data. However, challenges arising from color variations caused by differences in the imaging processes of spectrum cameras remain unresolved, leading to suboptimal feature representations. In this paper, we propose a simple yet highly effective data augmentation technique called Random Grayscale Region Substitution (RGRS) for the cross-modality matching task. RGRS operates by randomly selecting a rectangular region within a training sample and converting it to grayscale. This process generates training images that integrate varying levels of visible and channel-independent information, thereby mitigating overfitting and enhancing the model's robustness to color variations. In addition, we design a weighted regularized triplet loss function for cross-modality metric learning and a weak–strong synergy learning strategy to improve the performance of cross-modal matching. We validate the effectiveness of our approach through extensive experiments conducted on publicly available cross-modality Re-ID datasets, including SYSU-MM01 and RegDB. The experimental results demonstrate that our proposed method significantly improves accuracy, making it a valuable training trick for advancing VT-ReID research.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888850","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

Breast Cancer Detection Using a New Parallel Hybrid Logistic Regression Model Trained by Particle Swarm Optimization and Clonal Selection Algorithms 基于粒子群优化和克隆选择算法的新型并行混合逻辑回归模型的乳腺癌检测

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-04-29 DOI: 10.1002/cpe.70107

Mustafa Etcil, Bilge Kagan Dedeturk, Burak Kolukisa, Burcu Bakir-Gungor, Vehbi Cagri Gungor

{"title":"Breast Cancer Detection Using a New Parallel Hybrid Logistic Regression Model Trained by Particle Swarm Optimization and Clonal Selection Algorithms","authors":"Mustafa Etcil, Bilge Kagan Dedeturk, Burak Kolukisa, Burcu Bakir-Gungor, Vehbi Cagri Gungor","doi":"10.1002/cpe.70107","DOIUrl":"https://doi.org/10.1002/cpe.70107","url":null,"abstract":"<p>Breast cancer is one of the most widespread kinds of cancer, especially in women, and it has a high mortality rate. With the help of technology, it is possible to develop a computer-aided method for the diagnosis of breast cancer, which is crucial for effective treatment. Recent breast cancer diagnosis studies utilizing numerous machine learning models were efficient and innovative. However, it has been observed that they may have problems such as long training times and low accuracy rates. To this end, in this study, we present a new classifier that utilizes a hybrid of the clonal selection algorithm (CSA) and the particle swarm optimization (PSO) algorithm for the training of the logistic regression (LR) model, which is named CSA-PSO-LR. The proposed method is evaluated using two publicly accessible breast cancer datasets, that is, the Wisconsin Diagnostic Breast Cancer (WDBC) database and the Wisconsin Breast Cancer Database (WBCD), with 10-fold cross-validation and Bayesian hyperparameter optimization techniques. Additionally, a CPU parallelization method is applied, which substantially shortens the training time of the model. The efficacy of the CSA-PSO-LR classifier is compared with state-of-the-art machine learning algorithms and related studies in the literature. Performance analysis indicates that the proposed method achieves 98.75% accuracy and 98.27% F1-score on the WDBC dataset, and 97.94% accuracy and 97.35% F1-score on the WBCD dataset. These results demonstrate the potential of the proposed method as an effective approach for improving breast cancer diagnosis.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved and Untraceable Lightweight Authentication and Key Agreement Scheme for Wireless Body Area Networks 一种改进的、不可追踪的无线体域网络轻量级认证与密钥协议方案

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-04-24 DOI: 10.1002/cpe.70104

Zhongqing Wu, Ying Wang, Bo Gong, Lei Cheng, Jianbo Xu, Yulong Wang

{"title":"An Improved and Untraceable Lightweight Authentication and Key Agreement Scheme for Wireless Body Area Networks","authors":"Zhongqing Wu, Ying Wang, Bo Gong, Lei Cheng, Jianbo Xu, Yulong Wang","doi":"10.1002/cpe.70104","DOIUrl":"https://doi.org/10.1002/cpe.70104","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless body area networks (WBANs) are an important component of Medical 4.0, as they can use sensors to collect real-time data on a patient's vital signs and transmit this information over the internet to healthcare providers, greatly improving the quality and efficiency of medical care. However, Since WBANs typically collect human physiological information, which involves personal privacy, and the collected data are usually used by medical professionals for medical diagnosis. If the transmitted data are tampered with by attackers, it may lead to errors in medical diagnosis. Therefore, we must ensure the privacy, integrity, and reliability of the data during the transmission process. As a result, identity authentication and session key negotiation become crucial for secure communication in this context. Moreover, due to the constraints of sensors in terms of memory, computation, and battery life, a lightweight and efficient authentication scheme is required. Previously, Narwal et al. proposed a scheme called SAMAKA, which allows for anonymous authentication and session key establishment between sensor nodes and a control node. However, in-depth analysis has revealed that their scheme is vulnerable to sensor node capture attacks and does not provide session unlinkability or forward secrecy. To address the security flaws in Narwal et al.'s protocol, we have proposed an improved and untraceable mutual authentication and key negotiation scheme. We have also formally verified the security of our scheme using BAN logic and the AVISPA tool. Performance analysis shows that our scheme has significant advantages over other related schemes in terms of computational and communication costs, making it more suitable for the resource-constrained WBAN environment.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871685","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

Service Placement in Fog Computing Using a Combination of Reinforcement Learning and Improved Gray Wolf Optimization Method 基于强化学习和改进灰狼优化方法的雾计算服务布局

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-04-24 DOI: 10.1002/cpe.70097

Pouria Ashkani, Seyyed Hamid Ghafouri, Maliheh Hashemipour

{"title":"Service Placement in Fog Computing Using a Combination of Reinforcement Learning and Improved Gray Wolf Optimization Method","authors":"Pouria Ashkani, Seyyed Hamid Ghafouri, Maliheh Hashemipour","doi":"10.1002/cpe.70097","DOIUrl":"https://doi.org/10.1002/cpe.70097","url":null,"abstract":"<div>\u0000 \u0000 <p>Fog computing extends cloud computing to the edge of the network, bringing processing and storage capabilities closer to end users and Internet of Things (IoT) devices. This paradigm helps to reduce latency, improve response time, and optimize bandwidth usage. In the cloud computing environment, service availability is a criterion for determining user satisfaction, which is strongly influenced by response time and optimal allocation of network resources (communication bandwidth). Service placement in fog computing refers to the process of determining optimal locations for placing services in the network. In this paper, the service placement is done by being aware of the volume of user requests from fog nodes by using neural networks, reinforcement learning, and the improved gray wolf optimization (IGWO) method. Based on the results obtained from simulation, the proposed approach has less response time (between 5% and 21%), more favorable load balance, more utility value (12%) and lower Energy consumption by a minimum of 10% and a maximum of 25%.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865629","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 Blockchain-Based Secure Model to Share Medical Data Using Mobile Edge Computing 基于节能区块链的移动边缘计算医疗数据共享安全模型

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-04-23 DOI: 10.1002/cpe.70087

Sagnik Datta, Suyel Namasudra

{"title":"Energy-Efficient Blockchain-Based Secure Model to Share Medical Data Using Mobile Edge Computing","authors":"Sagnik Datta, Suyel Namasudra","doi":"10.1002/cpe.70087","DOIUrl":"https://doi.org/10.1002/cpe.70087","url":null,"abstract":"<div>\u0000 \u0000 <p>Blockchain technology is gaining importance in different sectors like healthcare, finance, agriculture, and many more. The important capabilities of blockchain like decentralization, immutability, consensus mechanism, etc. provide security, privacy, transparency, accountability, and many other benefits. On the other hand, Mobile Edge Computing (MEC) is a distributed framework that provides cloud computing capabilities to mobile devices. The existing studies combining blockchain technology and MEC often do not consider the delay and energy consumption for data offloading. In this paper, a blockchain-based scheme has been proposed for sharing Internet of Medical Things (IoMT) data between a patient and a doctor, which offloads tasks to the MEC server to achieve energy efficiency. In the proposed scheme, the Non-Orthogonal Multiple Access (NOMA) protocol is used to share a channel among several users. Here, NOMA offers some advantages in the system like low cost, latency, and power consumption. In the proposed scheme, the energy consumption is optimized based on the task delegation decision and resource distribution in the MEC server. Additionally, operations of the blockchain network are automated using various smart contracts. The efficiency of the proposed scheme is analyzed in terms of energy consumption, average transmission rate, and offloading delay in processing healthcare data. The experimental results demonstrate that the proposed model enhances energy efficiency and optimizes performance compared to the state-of-the-art offloading schemes.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861833","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 Mining Pool Performance and Security Through Optimized Cluster-Trust Consensus Mechanism in Blockchain Networks 通过优化的区块链网络簇信任共识机制提高矿池性能和安全性

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-04-22 DOI: 10.1002/cpe.70077

Naga Sravanthi Puppala, R. Manoharan

{"title":"Enhancing Mining Pool Performance and Security Through Optimized Cluster-Trust Consensus Mechanism in Blockchain Networks","authors":"Naga Sravanthi Puppala, R. Manoharan","doi":"10.1002/cpe.70077","DOIUrl":"https://doi.org/10.1002/cpe.70077","url":null,"abstract":"<div>\u0000 \u0000 <p>This research presents an innovative method for enhancing security in pool mining consensus within Blockchain systems. Block mining is a demanding endeavor in decentralized systems, leading many miners to congregate within mining pools. The inherent traits of Blockchain technology, defined by decentralization and transparency, create a conducive environment for extensive mining pool participation. However, the inclusion of all the miners into mining raises concerns about miners' consensus reliability, malicious attacks, fraud detection rates, and variance in miners' incentives. These consequences necessitate the encouragement of trusted miners to pool consensus to ensure security. A clustering-based trust model in blockchain for pool mining consensus (CBTMB-PM) has been proposed to address the miner's trust dynamics and improve pool consensus efficiency. Miner's trust dynamics are assessed through clusters based on miners' behavioral attributes, which encompass historical performance, readiness, and indirect feedback recommendations of miners to improve overall pool reliability. Furthermore, a Cluster-Trust Proof of Work (CTPoW) consensus protocol has been proposed to enhance pool efficiency, ensuring that only reliable miners contribute to the consensus process by filtering out untrusted miners in the consensus process. Experimental and theoretical evaluations demonstrate the effectiveness of the CTPoW protocol. The effectiveness of the model is tested using a partially decentralized open-source Hyperledger Fabric framework for parameters such as block authorization time, validation time, block creation time, processing time, and confirmation time to analyze the practical changes observed by a group of miners through off-chain mode. It has excellent performance by comparison with some other state-of-the-art.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861639","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 Efficient Scaling Scheme for Polynomial Acceleration in ILU-Preconditioned Systems ilu预条件系统中多项式加速的一种有效标度方案

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-04-22 DOI: 10.1002/cpe.70098

Feng Zhang, Xuebin Chi, Jinrong Jiang, Junlin Wei, Lian Zhao, Yuzhu Wang

{"title":"An Efficient Scaling Scheme for Polynomial Acceleration in ILU-Preconditioned Systems","authors":"Feng Zhang, Xuebin Chi, Jinrong Jiang, Junlin Wei, Lian Zhao, Yuzhu Wang","doi":"10.1002/cpe.70098","DOIUrl":"https://doi.org/10.1002/cpe.70098","url":null,"abstract":"<div>\u0000 \u0000 <p>Polynomial preconditioning accelerates iterative methods for large-scale sparse linear systems by optimizing the spectral distribution and decreasing reduction communication overhead. The Neumann polynomial is notable for its simple construction and stable performance, making it easy to combine with other preconditioners and widely used in high-performance computing. The choice of scaling parameter within the Neumann series is critical for polynomial acceleration, requiring an accurate estimate of the eigenvalue bounds of the preconditioned system. In preconditioned systems, the clustering of the largest eigenvalues often slows the convergence of iterative methods used to estimate the maximum eigenvalue, leading to an underestimated scaling parameter. We address this issue by using a Least-Squares model with linear inequality constraints to learn effective combination weights of Ritz values from training samples. While the Rayleigh-Ritz process (the current best eigen-estimation approach) requires 20-30 iterations and systematically underestimates extremal eigenvalues due to Ritz values' interior spectral distribution, our constrained optimization approach achieves comparable accuracy in 10 iterations by learning optimal combination weights from Ritz value distributions and corrects the systematic underestimation while preserving positive definitenessï£¡a critical stability requirement that ensures robust preconditioning performance across diverse problem configurations. Our implementation of the Neumann polynomial with the proposed scaling scheme achieved acceleration ratios of 2.61 and 3.52 for ILU (Incomplete LU factorization) and block-ILU preconditioned systems, respectively. It achieves comparable acceleration with the recent state-of-the-art minimum residual polynomial in the ILU-preconditioned systems frequently providing better convergence acceleration in numerous practical scenarios.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856743","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

Artificial Intelligence-Based Electric Vehicle Charging Station Load Forecasting Scheme for Smart Grid System 基于人工智能的智能电网电动汽车充电站负荷预测方案

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-04-21 DOI: 10.1002/cpe.70083

Riya Kakkar, Smita Agrawal, Sudeep Tanwar

{"title":"Artificial Intelligence-Based Electric Vehicle Charging Station Load Forecasting Scheme for Smart Grid System","authors":"Riya Kakkar, Smita Agrawal, Sudeep Tanwar","doi":"10.1002/cpe.70083","DOIUrl":"https://doi.org/10.1002/cpe.70083","url":null,"abstract":"<div>\u0000 \u0000 <p>The electrification and evolvement of intelligent transportation systems (ITS) have proved to be a breakthrough paradigm for adopting the indispensable benefits of electric vehicles (EVs) in the automotive industry. This necessitates intelligent energy management during the communication between the EVs and the charging stations (CS), which is one of the critical concerns due to the huge electricity demand for EVs. Thus, many authors have adopted the smart grid as an intelligent power distribution infrastructure, which requires the EV CS load forecasting to analyze the energy consumption at CS. Therefore, we propose an artificial intelligence (AI)-based EV CS load forecasting scheme adopting the benefits of smart grid environment. Consequently, we foremost consider EV charging data to predict state-of-charge (SoC) using an AI-based sequential model based on that CS issues an energy request to the smart grid. For that, we contemplate considering CS data and predicting the energy usage of different locations based on various parameters using a sequential model. Thus, the proposed EV CS load forecasting facilitates efficient energy transfer from the smart grid to CS for optimal EV charging. The performance evaluation of the proposed scheme is analyzed considering the EV charging dataset with metrics such as EV SoC prediction comparison, error prediction with battery voltage, and mean square error (MSE (0.0007)), mean absolute error (MAE (0.019)), and error prediction with charging time for CS dataset in which Adam optimizer outperform other optimizers (RMSprop and Adadelta) attaining the efficient load forecasting.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852964","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

Large-Scale Network-Oriented Protection Algorithm Through Efficient Tree Granularity Brownian-Like Motion 基于高效树粒度类布朗运动的大规模网络保护算法

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-04-21 DOI: 10.1002/cpe.70099

Longfei Ni, Qian Bai, Yongxin Zhang

{"title":"Large-Scale Network-Oriented Protection Algorithm Through Efficient Tree Granularity Brownian-Like Motion","authors":"Longfei Ni, Qian Bai, Yongxin Zhang","doi":"10.1002/cpe.70099","DOIUrl":"https://doi.org/10.1002/cpe.70099","url":null,"abstract":"<div>\u0000 \u0000 <p>In extensive and decentralized systems with numerous end-to-end communications, inadvertent disconnection can lead to the complete severance of the upper-level applications. Reliable communication can be ensured by designating links as trunks. These trunks maintain connectivity even when a large number of nontrunk links are simultaneously severed. Due to the computational complexity of cut enumeration, prior algorithms are restricted to topologies with a limited number of vertices. To address this challenge, in order to efficiently search in the potential tree space containing all cuts, we search at the granularity of the tree in a tree space that is homomorphic to the cut space and effectively discover a large number of target cuts by combining with Brownian-like motion. The found quasi-trunk links are then filtered to select a minimal-cost subset of trunk links, thereby guaranteeing the desired sustained connectivity. Experimental results demonstrate that the algorithm achieves a three-order of magnitude acceleration compared to the optimal method in small topologies, with limited extra cost. In large topologies, our trunk assignment method exhibits resilience to at least 99.9% stochastic link failures within a reasonable execution time.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852962","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