Pub Date : 2023-10-11DOI: 10.1007/s10586-023-04167-8
Hajer Nabli, Raoudha Ben Djemaa, Ikram Amous Ben Amor
{"title":"Correction: Improved clustering-based hybrid recommendation system to offer personalized cloud services","authors":"Hajer Nabli, Raoudha Ben Djemaa, Ikram Amous Ben Amor","doi":"10.1007/s10586-023-04167-8","DOIUrl":"https://doi.org/10.1007/s10586-023-04167-8","url":null,"abstract":"","PeriodicalId":50674,"journal":{"name":"Cluster Computing-The Journal of Networks Software Tools and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136062727","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}
Pub Date : 2023-10-10DOI: 10.1007/s10586-023-04151-2
Alberto Cabrera, Francisco Almeida, Dagoberto Castellanos-Nieves, Ariel Oleksiak, Vicente Blanco
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.
随着对计算资源需求的不断增长，计算机系统的整体能耗也随之增加。为了支持这种不断增长的需求，必须将功率和能源消耗视为软件执行的约束。现代体系结构提供了直接管理系统的功率约束的工具。Intel Power Cap是一种相对较新的工具，可以让用户在中央处理单元(CPU)级别对电源使用情况进行细粒度控制。这些工具的复杂性，加上现代异构体系结构的多样性，阻碍了对任何目标软件的能耗和性能的预测。功率封顶技术的应用通常会导致能源效率和执行时间的双目标优化问题，但最优功率约束也可能产生过大的性能损失。因此，需要方法和工具来计算功率封顶技术的适当参数，并优化能源效率。我们提出了一种方法来分析性能和能源效率的权衡使用这种功率上限技术为给定的应用程序。针对多目标性能和能量问题，提取了一个Pareto前，它代表了两个目标的多个可行配置。进行了广泛的实验，对不同的应用进行分类，以确定总体最佳功率帽配置。我们建议使用机器学习(ML)聚类技术对目标架构中的每个应用程序进行分类。机器学习的使用使我们能够自动化这个过程，并简化了解决优化问题所需的努力。给出了一个实际案例，其中我们使用ML技术对应用程序进行分类，并有可能将新应用程序添加到现有分类中。
{"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}
Pub Date : 2023-10-09DOI: 10.1007/s10586-023-04149-w
Shruti Arora, Rinkle Rani, Nitin Saxena
{"title":"SETL: a transfer learning based dynamic ensemble classifier for concept drift detection in streaming data","authors":"Shruti Arora, Rinkle Rani, Nitin Saxena","doi":"10.1007/s10586-023-04149-w","DOIUrl":"https://doi.org/10.1007/s10586-023-04149-w","url":null,"abstract":"","PeriodicalId":50674,"journal":{"name":"Cluster Computing-The Journal of Networks Software Tools and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135092937","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}
Pub Date : 2023-10-06DOI: 10.1007/s10586-023-04146-z
Olfa Souki, Raoudha Ben Djemaa, Ikram Amous, Florence Sedes
{"title":"Monitoring and analyzing as a service (MAaaS) through cloud edge based on intelligent transportation applications","authors":"Olfa Souki, Raoudha Ben Djemaa, Ikram Amous, Florence Sedes","doi":"10.1007/s10586-023-04146-z","DOIUrl":"https://doi.org/10.1007/s10586-023-04146-z","url":null,"abstract":"","PeriodicalId":50674,"journal":{"name":"Cluster Computing-The Journal of Networks Software Tools and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135345721","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}
Pub Date : 2023-10-03DOI: 10.1007/s10586-023-04114-7
Sana Aurangzeb, Muhammad Aleem, Muhammad Taimoor Khan, Haris Anwar, Muhammad Shaoor Siddique
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.
{"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}
Pub Date : 2023-10-03DOI: 10.1007/s10586-023-04143-2
Hui Xiang, Meiyu Zhang, Chengfeng Jian
{"title":"Federated deep reinforcement learning-based online task offloading and resource allocation in harsh mobile edge computing environment","authors":"Hui Xiang, Meiyu Zhang, Chengfeng Jian","doi":"10.1007/s10586-023-04143-2","DOIUrl":"https://doi.org/10.1007/s10586-023-04143-2","url":null,"abstract":"","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":"135695763","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}
Pub Date : 2023-10-03DOI: 10.1007/s10586-023-04165-w
Zeynep Garip, Ekin Ekinci, Murat Erhan Çimen
{"title":"A comparative study of optimization algorithms for feature selection on ML-based classification of agricultural data","authors":"Zeynep Garip, Ekin Ekinci, Murat Erhan Çimen","doi":"10.1007/s10586-023-04165-w","DOIUrl":"https://doi.org/10.1007/s10586-023-04165-w","url":null,"abstract":"","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":"135689820","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}
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