Future Generation Computer Systems-The International Journal of Escience最新文献

{"title":"Re-evaluating compute performance in SBC clusters: HPL benchmarking across generations","authors":"Z. Krpić,&nbsp;I. Lukić,&nbsp;M. Habijan,&nbsp;L. Loina","doi":"10.1016/j.future.2025.108137","DOIUrl":"10.1016/j.future.2025.108137","url":null,"abstract":"<div><div>Single Board Computer Clusters (SBCCs) are increasingly used as accessible, low-power platforms for parallel and distributed computing, particularly in edge and fog environments. Yet their performance remains underexplored through reproducible, tuned evaluations. This paper presents a benchmarking methodology based on the High Performance Linpack (HPL) benchmark, selected for its use of dense linear algebra kernels common in scientific and machine learning workloads. The evaluation includes HPL parameter tuning, compiler configuration, and comparison of ATLAS vs. OpenBLAS.</div><div>We apply the methodology SBCs spanning a decade of development: Raspberry Pi 1B, 3B, 4B, and 5, Cubieboard 2, Odroid U3, and Odroid-MC1. Results show that software-level tuning without overclocking or hardware modification can yield performance improvements of up to 2.3<span><math><mo>×</mo></math></span> over prior reports. A 146<span><math><mo>×</mo></math></span> increase in HPL performance between the Pi 1B and Pi 5 illustrates the evolution in computational capability within a stable form factor. OpenBLAS outperforms ATLAS on newer platforms, while ATLAS retains marginal advantages on older boards.</div><div>The findings provide a reproducible baseline for SBCC performance evaluation and support their relevance for benchmarking, education, and energy-efficient high-performance workloads in scenarios where conventional clusters are impractical due to cost, size, or power.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"176 ","pages":"Article 108137"},"PeriodicalIF":6.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145182918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On effectiveness of AI-based misbehavior detection in medical IoT","authors":"Hamid Al-Hamadi ,&nbsp;Ing-Ray Chen ,&nbsp;Ding-Chau Wang ,&nbsp;Abdullah Almutairi","doi":"10.1016/j.future.2025.108162","DOIUrl":"10.1016/j.future.2025.108162","url":null,"abstract":"<div><div>Artificial Intelligence (AI) classification techniques are pivotal for misbehavior detection in the Internet of Things (IoT), but their potential for severe failure poses a risk in safety-critical applications. This work introduces a novel statistical methodology to evaluate the operational readiness of these AI systems by quantitatively forecasting their effectiveness throughout the learning process. The significance of our methodology lies in its ability to provide predictive insights into an AI detector’s performance, enabling a system architect to make data-driven decisions about deployment. We use two lightweight statistical analysis methods: one to model device compliance and forecast the detector’s false negative probability (<span><math><msub><mi>p</mi><mrow><mi>f</mi><mi>n</mi></mrow></msub></math></span>) of missing a malicious device and its false positive probability (<span><math><msub><mi>p</mi><mrow><mi>f</mi><mi>p</mi></mrow></msub></math></span>) of misidentifying a benign one, and another to model the learning curve and predict the future misclassification rate. This framework allows a designer to determine precisely when a system has been trained sufficiently to meet predefined safety and reliability targets. We demonstrate the feasibility of our approach on an artificial pancreas system with a smart Continuous Subcutaneous Insulin Infusion (CSII) device, confirming the effective and predictable detection of sophisticated attacks.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"176 ","pages":"Article 108162"},"PeriodicalIF":6.2,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145182919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforced model selection for resource efficient anomaly detection in edge clouds","authors":"Javad Forough,&nbsp;Monowar Bhuyan,&nbsp;Erik Elmroth","doi":"10.1016/j.future.2025.108161","DOIUrl":"10.1016/j.future.2025.108161","url":null,"abstract":"<div><div>Web application services and networks encounter a broad range of security and performance anomalies, necessitating sophisticated detection strategies. However, performing anomaly detection in edge cloud environments, often constrained by limited resources, presents significant computational challenges and demands minimized detection time for real-time response. In this paper, we propose a model selection approach for resource efficient anomaly detection in edge clouds by leveraging an adapted Deep Q-Network (DQN) reinforcement learning technique. The primary objective is to minimize the computational resources required for accurate anomaly detection while achieving low latency and high detection accuracy. Through extensive experimental evaluation in our testbed setup over different representative scenarios, we demonstrate that our adapted DQN approach can reduce resource usage by up to 45 % and detection time by up to 85 % while incurring less than an 8 % drop in F1 score. These results highlight the potential of the adapted DQN model selection strategy to enable efficient, low-latency anomaly detection in resource-constrained edge cloud environments.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"176 ","pages":"Article 108161"},"PeriodicalIF":6.2,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HDP-FedCD: Data-quality-driven hierarchical federated learning for optimizing privacy protection in non-IID data","authors":"Chunxiao Yin ,&nbsp;Kai He ,&nbsp;Jiaoli Shi","doi":"10.1016/j.future.2025.108140","DOIUrl":"10.1016/j.future.2025.108140","url":null,"abstract":"<div><div>With the proliferation of Internet of Things (IoT) devices, Federated Learning (FL) has become a key paradigm for collaborative machine learning on decentralized edge data. However, FL remains vulnerable to inference attacks, posing significant privacy concerns, particularly in scenarios with diverse data quality and distribution. Existing privacy protection methods often neglect such heterogeneity, resulting in suboptimal trade-offs between privacy and performance. We propose a Hierarchical Differential Privacy protection scheme in Federated Learning based on Core-Degree (HDP-FedCD), which achieves an optimal balance between privacy and utility by leveraging core-degree as a measure of data quality to dynamically adjust noise levels. Using an adaptive core-degree threshold, HDP-FedCD layers local datasets into core and non-core layers, tailoring noise intensity to data quality: low-intensity noise preserves utility for core-layer data, while high-intensity noise enhances privacy for non-core-layer data. Furthermore, the adaptive threshold mechanism responds to dynamic data distribution changes, ensuring robustness across diverse FL scenarios. Empirical evaluations on image classification tasks demonstrate that HDP-FedCD outperforms state-of-the-art methods in model accuracy and resistance to inference attacks, offering an innovative solution for privacy-preserving federated learning.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"176 ","pages":"Article 108140"},"PeriodicalIF":6.2,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CICFormer: An efficient unsupervised performance anomaly detection model based on temporal context attention and interactive convolution for fluctuating cloud environments","authors":"Xuelian Xie ,&nbsp;Peng Chen ,&nbsp;Xi Li ,&nbsp;Ang Bian ,&nbsp;Juan Chen ,&nbsp;Linqiao Huang","doi":"10.1016/j.future.2025.108126","DOIUrl":"10.1016/j.future.2025.108126","url":null,"abstract":"<div><div>Anomaly detection in real-time monitoring metrics is crucial for ensuring the stability and service quality of cloud computing systems. However, owing to the dynamic nature of cloud computing resources, interdependencies among high-dimensional monitoring indicator data, and the sparsity of anomalies, anomaly detection tasks face significant challenges. Traditional methods often rely on static features or single time-scale analyses, failing to fully consider the complex contextual information and temporal dependencies between monitoring indicator data, making it difficult to accurately identify potential anomalies in cloud monitoring metrics. To address this issue, this paper proposes an efficient unsupervised performance anomaly detection model based on temporal context attention and interactive convolution transformer (CICFormer) for fluctuating cloud environments. This model adopts an unsupervised temporal anomaly detection method that integrates the temporal context-aware mechanism (TCAM), squeeze-and-excitation (SE), and interactive convolutional block (ICB). TCAM leverages temporal pooling and channel attention to enhance understanding of key time points, SE improves sensitivity to relevant variables, and ICB captures both local and global features using multi-scale convolutions. Extensive experiments on five public datasets show that CICFormer outperforms 18 baseline methods, achieving a 4.26 % improvement in the average F1 score. These results demonstrate that CICFormer is highly effective for real-time anomaly detection in cloud environments.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"176 ","pages":"Article 108126"},"PeriodicalIF":6.2,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quality of experience aware task execution in digital twinning vehicular edge computing: A framework and A3C algorithm","authors":"Mostakim Jihad ,&nbsp;Abdullah Al Fahad ,&nbsp;Palash Roy ,&nbsp;Md Abdur Razzaque ,&nbsp;Abdulhameed Alelaiwi ,&nbsp;Md Rafiul Hassan ,&nbsp;Mohammad Mehedi Hassan","doi":"10.1016/j.future.2025.108144","DOIUrl":"10.1016/j.future.2025.108144","url":null,"abstract":"<div><div>Real-time computationally intensive task scheduling for intelligent transportation system (ITS) applications like road safety and traffic forecasting within the deadline while ensuring user quality of experience (QoE) is a complex engineering problem. Meanwhile, adopting Digital Twin (DT) as an emerging technology in vehicular edge computing (VEC) enables efficient capture of real-time state information, thereby addressing the resource scheduling problem in an unpredictable vehicular topology setting. However, exploring strategies to enhance user QoE in timeliness and reliability domains could be a compelling and underexplored research challenge, particularly within the dynamic and trust-sensitive context of vehicular edge computing. In this paper, we have developed an optimization framework using Mixed Integer Linear Programming (MILP), which maximizes user QoE by allocating task execution responsibility to highly reliable and reputed vehicles in a DT-enabled VEC environment. The framework leverages the demand-supply theory of economics to cluster vehicles based on computational resources and applies multi-weighted subjective logic to ensure accurate reputation updates. The NP-hard nature of the formulated optimization problem has driven us to develop an Asynchronous Advantage Actor-Critic (A3C)-based deep reinforcement learning algorithm, namely DARQoE, for offloading tasks in the Internet of Vehicles (IoV). The developed DARQoE framework utilizes effective parallelization across multiple agents with separate environments, accelerating the learning process for IoV task offloading. The experimental results of the developed DARQoE framework demonstrate significant performance improvements in terms of QoE in the timeliness and reliability domains of task execution by up to 15 % and 25 %, respectively, compared to state-of-the-art works.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"176 ","pages":"Article 108144"},"PeriodicalIF":6.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital twins for essential services","authors":"Alessio Bucaioni,&nbsp;Jakob Axelsson,&nbsp;Moris Behnam,&nbsp;Enxhi Ferko","doi":"10.1016/j.future.2025.108147","DOIUrl":"10.1016/j.future.2025.108147","url":null,"abstract":"<div><div>Digital twins, dynamic digital representations of physical systems, are emerging as transformative tools for enhancing crisis preparedness and resilience in critical societal sectors. By enabling real-time monitoring, simulation, and optimization, these technologies offer actionable insights to support proactive risk mitigation, efficient resource allocation, and continuous improvement of crisis response strategies. This study provides a comprehensive knowledge overview of digital twins, focusing on their applicability and impact in key sectors such as energy, healthcare, and transportation. Specifically, it examines the essential services most suited for digital twin adoption, the role of safety-critical data throughout their life-cycle, and their utility in identifying and mitigating risks within critical infrastructure. We employed a mixed-methods research design, combining systematic and gray literature reviews with expert interviews to integrate academic insights with practical perspectives. The findings reveal significant opportunities for digital twins to enhance operational efficiency, strategic planning, and crisis management. However, practical implementation remains in its infancy, with challenges related to cost, complexity, and limited real-world applications. In addition, this study provides actionable recommendations for stakeholders, emphasizing investment in digital twin technologies, robust data governance, and the development of standardized protocols. Future research directions include exploring applications of DTs in emerging sectors, such as crisis preparedness and societal resilience, advancing artificial intelligence integration, and adopting a system-of-systems perspective to address societal challenges comprehensively.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"176 ","pages":"Article 108147"},"PeriodicalIF":6.2,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Formal specification and executable analysis of digital twin systems using Maude rewriting logic","authors":"Turki Alhazmi,&nbsp;Farag Azzedin,&nbsp;Jameleddine Hassine,&nbsp;Mohammad Hammoudeh","doi":"10.1016/j.future.2025.108148","DOIUrl":"10.1016/j.future.2025.108148","url":null,"abstract":"<div><div>Digital Twins (DTs) are revolutionizing industries by enabling real-time simulation, monitoring, and predictive analysis of physical systems. However, the complexity of DTs and the lack of formal specification frameworks hinder their rigorous analysis and verification, limiting their reliability in critical applications. This article presents a novel formal and executable DT system model based on rewriting logic, leveraging Maude as a high-performance specification and analysis tool. Unlike existing models, which are often either informal, semi-formal, or non-executable, our approach ensures precise syntax, well-defined semantics, and full executability. This approach enables automated verification through reachability analysis, model checking, and theorem proving. Our model captures essential DT functional primitives with abstraction, enabling precise modeling of dynamic behaviors and state transitions. We formally define a structured event-driven DT system architecture, decomposing DT functions into sensing, actuation, processing, and communication layers. The model’s applicability is demonstrated through two case studies: a thermostat system (capturing property-level synchronization) and an incubator system (modeling state-level synchronization). Simulation and verification results reveal critical insights into DT synchronization, showing that initial state discrepancies persist over time, emphasizing the need for formal DT validation techniques. Our rigorous, scalable, and adaptable DT modeling paradigm paves the way for more robust, verifiable, and reliable digital twin applications across industries.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"176 ","pages":"Article 108148"},"PeriodicalIF":6.2,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient disk read and recovery cost reduction approach in heterogeneous liberation-coded storage systems","authors":"Ningjing Liang ,&nbsp;Xiaolong Jiang ,&nbsp;Genqing Bian ,&nbsp;Songchen Huang ,&nbsp;Ying Tang ,&nbsp;Xingjun Zhang","doi":"10.1016/j.future.2025.108142","DOIUrl":"10.1016/j.future.2025.108142","url":null,"abstract":"<div><div>Distributed storage clusters provide large-scale data storage solutions; however they often experience node failures. Erasure codes are widely used to ensure data reliability while maintaining low storage overhead. However, during the recovery process, the high disk reads and network traffic associated with erasure codes can prolong recovery time and increase the risk of data loss. Current solutions that focus exclusively on reducing data reads to expedite recovery are often less effective in real-world network environments. This paper addresses the recovery problem in scenarios where storage nodes exhibit heterogeneity in network bandwidth. We assign recovery cost to each storage node based on its network bandwidth and propose the Recovery Cost Optimization for Heterogeneous Storage (RCOHS), a heterogeneous recovery method for Liberation-coded systems that minimizes data downloads while keeping low recovery cost. RCOHS incorporates the <em>SearchCostOptSeq</em> algorithm, which employs cyclic condition theory to refine the solution space. It determines the lowest-cost solution among all disk-read optimal options, in conjunction with the <em>OptSeqRecov</em> algorithm, which reconstructs failure symbols in the correct order using this solution. We conducted extensive experiments on Amazon EC2, and the results show that RCOHS reduces recovery time by an average of 31.2 % compared to the traditional method of RFPD and 8.4 % over the state-of-the-art technique, DROR.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"176 ","pages":"Article 108142"},"PeriodicalIF":6.2,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reservoir computing for enhanced fidelity in hierarchical digital twin ecosystems","authors":"Matteo Mendula ,&nbsp;Marco Miozzo ,&nbsp;Paolo Bellavista ,&nbsp;Paolo Dini","doi":"10.1016/j.future.2025.108146","DOIUrl":"10.1016/j.future.2025.108146","url":null,"abstract":"<div><div>The growing complexity of Cyber-Physical Systems (CPS) in industrial and manufacturing environments calls for more sophisticated methods to represent heterogeneous assets and processes. In response, hierarchical Digital Twins (DTs)–virtual representations of physical, taxonomy-based processes–offer transparent, layered modeling of diverse data sources. This layered structure fuels renewed interest in intelligent engines capable of extracting meaningful insights and mapping them within the stratified DT ecosystem. While current Intelligent Digital Twin (I-DT) engines based on Deep Learning are computationally demanding, lightweight alternatives like Reservoir Computing (RC) offer efficient solutions with low training costs and fast inference for modeling causal dynamics. This inherent trade-off between performance and practicality underscores the limitations of evaluating I-DTs on accuracy alone. To address this gap, this work introduces a novel metric, <em>Fidelity</em>, designed to provide a comprehensive evaluation. Unlike traditional approaches, <em>Fidelity</em> also accounts for maintainability and deployability, especially in contexts involving time-varying and hierarchical data dynamics. Extensive experiments on two multimodal datasets demonstrate the competitiveness of our RC-based engine and highlight the value of introducing Fidelity for effectively profiling I-DTs. Specifically, our RC-based engine, identified as optimal through a higher Fidelity score, consumes <strong>an order of magnitude less energy</strong> and achieves up to <strong>39 % higher accuracy</strong> (about 10 % increase on average) compared to both canonical and other RC-based alternatives.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"176 ","pages":"Article 108146"},"PeriodicalIF":6.2,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}