Cluster Computing-The Journal of Networks Software Tools and Applications最新文献_第3页

Secure peer-to-peer learning using feature embeddings 使用特征嵌入确保点对点学习

3区计算机科学

Cluster Computing-The Journal of Networks Software Tools and Applications Pub Date : 2023-10-13 DOI: 10.1007/s10586-023-04155-y

Anirudh Kasturi, Akshat Agrawal, Chittaranjan Hota

引用次数: 0

Correction: Improved clustering-based hybrid recommendation system to offer personalized cloud services 修正:改进基于聚类的混合推荐系统，提供个性化的云服务

3区计算机科学

Cluster Computing-The Journal of Networks Software Tools and Applications Pub Date : 2023-10-11 DOI: 10.1007/s10586-023-04167-8

Hajer Nabli, Raoudha Ben Djemaa, Ikram Amous Ben Amor

引用次数: 0

Parallel and streaming wavelet neural networks for classification and regression under apache spark 并行和流小波神经网络分类和回归在apache spark

3区计算机科学

Cluster Computing-The Journal of Networks Software Tools and Applications Pub Date : 2023-10-10 DOI: 10.1007/s10586-023-04150-3

Harindra Venkatesh Eduru, Yelleti Vivek, Vadlamani Ravi, Orsu Shiva Shankar

引用次数: 0

Energy efficient power cap configurations through Pareto front analysis and machine learning categorization 通过帕累托前分析和机器学习分类的节能电源帽配置

3区计算机科学

Cluster Computing-The Journal of Networks Software Tools and Applications Pub Date : 2023-10-10 DOI: 10.1007/s10586-023-04151-2

Alberto Cabrera, Francisco Almeida, Dagoberto Castellanos-Nieves, Ariel Oleksiak, Vicente Blanco

{"title":"Energy efficient power cap configurations through Pareto front analysis and machine learning categorization","authors":"Alberto Cabrera, Francisco Almeida, Dagoberto Castellanos-Nieves, Ariel Oleksiak, Vicente Blanco","doi":"10.1007/s10586-023-04151-2","DOIUrl":"https://doi.org/10.1007/s10586-023-04151-2","url":null,"abstract":"Abstract The growing demand for more computing resources has increased the overall energy consumption of computer systems. To support this increasing demand, power and energy consumption must be considered as a constraint on software execution. Modern architectures provide tools for managing the power constraints of a system directly. The Intel Power Cap is a relatively new tool developed to give users fine-grained control over power usage at the central processing unit (CPU) level. The complexity of these tools, in addition to the high variety of modern heterogeneous architectures, hinders predictions of the energy consumption and the performance of any target software. The application of power capping technologies usually leads to the bi-objective optimization problem for energy efficiency and execution time but optimal power constraints could also produce exceeding performance losses. Thus, methods and tools are needed to calculate the proper parameters for power capping technologies, and to optimize energy efficiency. We propose a methodology to analyze the performance and the energy efficiency trade-offs using this power cap technology for a given application. A Pareto front is extracted for the multi-objective performance and energy problem, which represents multiple feasible configurations for both objectives. An extensive experimentation is carried out to categorize the different applications to determine the overall optimal power cap configurations. We propose the use of machine learning (ML) clustering techniques to categorize each application in the target architecture. The use of ML allows us to automate the process and simplifies the effort required to solve the optimization problem. A practical case is presented where we categorize the applications using ML techniques, with the possibility of adding a new application into an existing categorization.","PeriodicalId":50674,"journal":{"name":"Cluster Computing-The Journal of Networks Software Tools and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136294038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

SETL: a transfer learning based dynamic ensemble classifier for concept drift detection in streaming data SETL:基于迁移学习的动态集成分类器，用于流数据中的概念漂移检测

3区计算机科学

Cluster Computing-The Journal of Networks Software Tools and Applications Pub Date : 2023-10-09 DOI: 10.1007/s10586-023-04149-w

Shruti Arora, Rinkle Rani, Nitin Saxena

引用次数: 0

Federated learning for feature-fusion based requirement classification 基于特征融合的需求分类的联邦学习

3区计算机科学

Cluster Computing-The Journal of Networks Software Tools and Applications Pub Date : 2023-10-09 DOI: 10.1007/s10586-023-04147-y

Ruiwen Wang, Jihong Liu, Qiang Zhang, Chao Fu, Yongzhu hou

引用次数: 0

Monitoring and analyzing as a service (MAaaS) through cloud edge based on intelligent transportation applications 基于智能交通应用的云边缘监控和分析即服务(MAaaS)

3区计算机科学

Cluster Computing-The Journal of Networks Software Tools and Applications Pub Date : 2023-10-06 DOI: 10.1007/s10586-023-04146-z

Olfa Souki, Raoudha Ben Djemaa, Ikram Amous, Florence Sedes

引用次数: 0

Cybersecurity for autonomous vehicles against malware attacks in smart-cities 智能城市中自动驾驶汽车抵御恶意软件攻击的网络安全

3区计算机科学

Cluster Computing-The Journal of Networks Software Tools and Applications Pub Date : 2023-10-03 DOI: 10.1007/s10586-023-04114-7

Sana Aurangzeb, Muhammad Aleem, Muhammad Taimoor Khan, Haris Anwar, Muhammad Shaoor Siddique

{"title":"Cybersecurity for autonomous vehicles against malware attacks in smart-cities","authors":"Sana Aurangzeb, Muhammad Aleem, Muhammad Taimoor Khan, Haris Anwar, Muhammad Shaoor Siddique","doi":"10.1007/s10586-023-04114-7","DOIUrl":"https://doi.org/10.1007/s10586-023-04114-7","url":null,"abstract":"Abstract Smart Autonomous Vehicles (AVSs) are networks of Cyber-Physical Systems (CPSs) in which they wirelessly communicate with other CPSs sub-systems (e.g., smart -vehicles and smart-devices) to efficiently and securely plan safe travel. Due to unreliable wireless communication among them, such vehicles are an easy target of malware attacks that may compromise vehicles’ autonomy, increase inter-vehicle communication latency, and drain vehicles’ power. Such compromises may result in traffic congestion, threaten the safety of passengers, and can result in financial loss. Therefore, real-time detection of such attacks is key to the safe smart transportation and Intelligent Transport Systems (ITSs). Current approaches either employ static analysis or dynamic analysis techniques to detect such attacks. However, these approaches may not detect malware in real-time because of zero-day attacks and huge computational resources. Therefore, we introduce a hybrid approach that combines the strength of both analyses to efficiently detect malware for the privacy of smart-cities.","PeriodicalId":50674,"journal":{"name":"Cluster Computing-The Journal of Networks Software Tools and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135695470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 1

Federated deep reinforcement learning-based online task offloading and resource allocation in harsh mobile edge computing environment 恶劣移动边缘计算环境下基于深度强化学习的联合在线任务卸载与资源分配

3区计算机科学

Cluster Computing-The Journal of Networks Software Tools and Applications Pub Date : 2023-10-03 DOI: 10.1007/s10586-023-04143-2

Hui Xiang, Meiyu Zhang, Chengfeng Jian

引用次数: 0

A comparative study of optimization algorithms for feature selection on ML-based classification of agricultural data 基于机器学习的农业数据分类特征选择优化算法比较研究

3区计算机科学

Cluster Computing-The Journal of Networks Software Tools and Applications Pub Date : 2023-10-03 DOI: 10.1007/s10586-023-04165-w

Zeynep Garip, Ekin Ekinci, Murat Erhan Çimen

引用次数: 0