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

{"title":"Elevating Datacenter Resilience with ThermADNet: A Thermal Anomaly Detection System","authors":"Mohsen Seyedkazemi Ardebili ,&nbsp;Andrea Acquaviva ,&nbsp;Luca Benini ,&nbsp;Andrea Bartolini","doi":"10.1016/j.future.2025.108311","DOIUrl":"10.1016/j.future.2025.108311","url":null,"abstract":"<div><div>In the era of digital transformation, datacenters and High Performance Computing (HPC) Systems have emerged as the backbone of global technology infrastructure, powering essential services across various industries, including finance and healthcare. Therefore, ensuring the uninterrupted service of these datacenters has become a critical challenge. Thermal anomalies pose a significant risk to datacenter operation, potentially leading to hardware deterioration, system downtime, and catastrophic failures. This threat is exacerbated by the growing number of datacenters, increased power density, and heat waves fostered by global warming. Detecting thermal anomalies in datacenters involves several challenges. Large-scale data collection is difficult, requiring diverse monitoring signals from thousands of nodes over long periods. The absence of labeled data complicates the identification of normal and abnormal states. Establishing accurate classification thresholds to minimize false positives and negatives is another significant hurdle. Traditional statistical methods often fail to capture temporal dependencies and complex correlations in monitoring signals. Additionally, finding anomalies at both the system and subsystem levels adds to the complexity. Deploying machine learning models in production environments presents technical and operational challenges, making real-time anomaly detection a demanding task. This paper introduces ThermADNet, a Thermal Anomaly Detection framework that combines statistical rules-based methods with Deep Neural Network (DNN) techniques for thermal anomaly detection in datacenters. ThermADNet utilizes a semi-supervised learning approach by training on a “semi-normal” dataset, addressing the challenges of large-scale data collection, semi-normal dataset identification, and classification threshold establishment. This framework’s efficacy is validated by its success in identifying real physical thermal failure events within a Tier-0 datacenter, pinpointing anomalies at both the system and subsystem levels, including compute nodes and datacenter infrastructure. In the critical evaluation window covering the July 28 failure, ThermADNet achieves precision and recall up to 0.97, with F1-scores as high as 0.97. By providing detailed information about anomalies, the framework clarifies the characteristics and reasoning behind the DNN outputs, thereby building trust in the AI model and ensuring that users can understand and rely on the system’s decisions. By offering a sophisticated method for thermal anomaly detection, ThermADNet significantly contributes to enhancing datacenter reliability and efficiency. This advancement supports the uninterrupted operation of critical HPC systems, averting considerable economic and societal losses.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108311"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785032","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":"DGWOSC: A depth-based grey wolf optimizer for reliability aware soft real-time service scheduling and multiserver configuration","authors":"Tian Wang ,&nbsp;Jianfei Chen ,&nbsp;Liying Li ,&nbsp;Wei Shen ,&nbsp;Lei Zhou ,&nbsp;Linli Xu ,&nbsp;Junlong Zhou","doi":"10.1016/j.future.2025.108326","DOIUrl":"10.1016/j.future.2025.108326","url":null,"abstract":"<div><div>With the popularization of cloud computing, more and more cloud providers charge for cloud services based on the performance of computing resource provisioning. For cloud service providers, maximizing profit by focusing on multicore-based multiserver systems is a perennial goal. However, existing research on multiserver systems that maximize the service profit either limits itself to optimizing the multiserver configuration while neglecting the schedulability of cloud service requests or focuses on cloud service scheduling while ignoring the dynamic scalability of the multiserver. Furthermore, the potential impact of transient faults on service processing presents a significant opportunity for improving cloud profitability, an area that has received less attention in profit-oriented research. Therefore, it is necessary to design a collaborative optimization method for cloud service scheduling and multiserver configuration, specifically targeting soft real-time cloud service requests, to fill the gap in existing works. In this work, we first model cloud service scheduling and multiserver configuration as a profit maximization problem that is a mixed integer nonlinear optimization. Then, we propose a depth-based grey wolf optimizer to solve our formulated problem. Finally, extensive experiments are conducted to validate the effectiveness of our proposed method. The empirical results demonstrate that our method achieves an average increase of 7.04 % in service profits compared to six benchmark methods.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108326"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785035","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 and scalable branch-and-bound algorithm for exact qubit allocation","authors":"Jean-Philippe Valois,&nbsp;Guillaume Helbecque,&nbsp;Nouredine Melab","doi":"10.1016/j.future.2025.108342","DOIUrl":"10.1016/j.future.2025.108342","url":null,"abstract":"<div><div>Qubit allocation is a central step in adapting abstract quantum circuits to noisy intermediate-scale quantum devices, yet exact approaches for solving it face severe scalability limitations. In this work, we revisit the formulation of qubit allocation as a permutation-based quadratic assignment problem and develop a branch-and-bound algorithm for its exact resolution. We first establish a refined sequential implementation that achieves significantly faster runtimes than previous exact approaches on most problem instances, thereby setting a new state-of-the-art for this formulation. Building on this foundation, we extend the approach to a performance-aware parallel implementation that exploits both intra-node and inter-node parallelism on High-Performance Computing (HPC) infrastructures. Our experimental evaluation demonstrates near-linear strong scaling at the intra-node level and substantial scalability in distributed settings across nodes. Leveraging these capabilities, we provide reference optimal solutions for challenging benchmark circuits of up to 26 qubits—significantly larger than previously reported instances. These results show that large-scale parallelization can effectively extend the reach of exact methods for qubit allocation, thereby advancing the integration of combinatorial optimization and HPC techniques in quantum computing.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108342"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823160","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":"Model context protocol-based agentic react large language model for adaptive traffic signals: Luxembourg case study","authors":"Tarek Othmani ,&nbsp;Sadok Ben Yahia ,&nbsp;Antonio Lalaguna","doi":"10.1016/j.future.2025.108339","DOIUrl":"10.1016/j.future.2025.108339","url":null,"abstract":"<div><div>Due to the growing issues of urban population, including mobility requirements, this paper addresses the phenomenon of traffic congestion in urban environments by employing a <strong>M</strong>odel Context Protocol-Based <strong>A</strong>gentic <strong>R</strong>eAct <strong>L</strong>arge Language Model for <strong>A</strong>daptive <strong>T</strong>raffic <strong>S</strong>ignals (<strong>MARLATS</strong>) framework based on adaptive Traffic Management, Reinforcement Learning (RL), and Large Language Models (LLMs). The framework assessed energy consumption, emissions measures, traffic performance, and economic performance. The incorporation of various types of vehicles and practical trip scenarios within the <strong>MARLATS</strong> framework of Luxembourg City was carried out to support traffic control in urban areas. The study findings revealed a 89% cut in average travel time, a 96% drop in average waiting time, 74% gain in average speed, a remarkable 50% reduction in fuel consumption and emission abatement (CO, CO₂, NO_<em>x</em>, PM, NMVOC), while increasing noise pollution by 6.9% but <strong>MARLATS</strong> was capable of halving the operating costs by 50% from 14.14€ /h to 7.05€ /h. Compared with leading RL/DRL/LLM studies, <strong>MARLATS</strong> outperforms by 34% to 73%. These results position <strong>MARLATS</strong> as a turnkey, rapid-payback pathway to net-zero, congestion-free cities. Despite the good results, <strong>MARLATS</strong> suffer from some limitations that need to be considered in future projects, such as reducing noise emissions, mixing vehicle fleets like battery electric and plug-in hybrid vehicles, quantifying V2X infrastructure costs, and providing cybersecurity analysis for efficient and safer data transfer.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108339"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823161","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":"A privacy protection mechanism in distributed reinforcement learning using zero-knowledge proof","authors":"Changjin Zhao,&nbsp;Xiang Feng,&nbsp;Huiqun Yu","doi":"10.1016/j.future.2025.108320","DOIUrl":"10.1016/j.future.2025.108320","url":null,"abstract":"<div><div>In the field of distributed agent communication, privacy protection has always been a core concern. With ongoing advances in privacy-preserving technologies, integrating these techniques into distributed reinforcement learning has become a prevailing trend. However, the key challenge lies in safeguarding privacy while ensuring that model learning efficiency remains unaffected. To tackle this concern, a privacy-preserving framework named Zero-Knowledge proof for Distributed Reinforcement Learning (ZKDRL) is proposed. This framework equips each agent with strict differential privacy and integrates a privacy-aware receiver at the Learner end to mitigate the impact of noise on model aggregation. Additionally, zero-knowledge proof techniques are incorporated to ensure communication security and integrity within the distributed system, thereby verifying information authenticity without revealing any additional details. Implementation of ZKDRL on the open-source Surreal framework shows that, compared to baseline methods, the approach enhances data privacy by at least 21.9 % while increasing the model’s average cumulative reward by 9.5 %. Consequently, the model’s performance loss remains confined to an acceptable range, which confirms the framework’s practical applicability in distributed reinforcement learning.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108320"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823165","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":"Adaptive Digital Twin: Protection, deception, and testing","authors":"Cristina Alcaraz,&nbsp;Hector Guzman,&nbsp;Javier Lopez","doi":"10.1016/j.future.2025.108357","DOIUrl":"10.1016/j.future.2025.108357","url":null,"abstract":"<div><div>A Digital Twin (DT) is a cutting-edge technology that has gained relevance in recent years, demonstrating huge potential for the simulation of processes and the provision of valuable insights to improve and optimise systems. Leveraging a high degree of fidelity in replicating real-world processes, DTs are being explored for advanced applications such as deception and proactive protection of critical infrastructures. However, this same advantage also raises concerns with respect to a system’s exposure, as the detailed digital representation may introduce new cybersecurity risks. With the aim of assisting the growth of this technology, this paper presents an adaptive DT solution that facilitates the configuration of particular components of the digital system, tailoring different application scenarios specifically for protection, deception, and testing purposes. Finally, the proposed architecture is tested under a specific IoT-oriented use case to validate, experiment, and extract conclusions of the proposed solution.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108357"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893750","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":"A multi-objective multi-stage genetic algorithm for community detection in biological networks","authors":"Mingyuan Bi ,&nbsp;Junliang Shang ,&nbsp;Yahan Li ,&nbsp;Anqi Xu ,&nbsp;Yaxuan Zhang ,&nbsp;Feng Li ,&nbsp;Jin-Xing Liu","doi":"10.1016/j.future.2025.108322","DOIUrl":"10.1016/j.future.2025.108322","url":null,"abstract":"<div><div>Community detection in biological networks is crucial for capturing genes with similar functions and discovering biomarkers. However, communities discovered by many existing methods are often not closely connected, both topologically and functionally, so studies on community detection are still ongoing. In this paper, a multi-objective multi-stage genetic algorithm, named MOMSGA, is proposed to extract communities in biological networks. Firstly, the pre-reduction and boundary correction strategies are introduced to enhance the scalability of MOMSGA in large-scale networks. Secondly, a genetic algorithm is improved to guide the search process, where two improved objective functions are designed to simultaneously optimize the topological and functional connections to accurately extract information about relevant biological processes. The population initialization strategy and mutation operator are tailored. Thirdly, a multi-stage strategy is proposed that divides the evolutionary process into distinct stages based on the characteristics of the population at each stage, employing different selection and update strategies to obtain better diversity performance. Two notable innovations of MOMSGA lie in its multi-objective and multi-stage strategies. Experiments on 11 synthetic networks and 5 real-world networks demonstrate the superiority of MOMSGA, which outperforms four advanced methods. Furthermore, MOMSGA is applied to four gene expression datasets for biomarker identification. The results consistently show that MOMSGA outperforms other methods in classification performance across six indicators, particularly on the pheochromocytoma dataset, where the AUC reached 0.86, 2.9 % to 10.3 % higher than other methods. Moreover, the identified communities have been shown to be associated with the corresponding diseases through GO and KEGG enrichment analysis.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108322"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785041","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":"A stochastic performance model for evaluating ethereum layer-2 rollups","authors":"Carlos Melo ,&nbsp;José Miqueias ,&nbsp;Johnnatan Messias ,&nbsp;Glauber Gonçalves ,&nbsp;Francisco Airton Silva ,&nbsp;André Soares ,&nbsp;Jean Araujo","doi":"10.1016/j.future.2025.108316","DOIUrl":"10.1016/j.future.2025.108316","url":null,"abstract":"<div><div>Although Ethereum’s transition to Proof-of-Stake and the emergence of sidechains offer partial improvements on scalability, these approaches still present performance-related trade-offs. To address these challenges, ZK-Rollups have emerged as Layer-2 scalability solutions. They combine off-chain computation with on-chain verification, improving scalability while preserving the underlying security guarantees of Ethereum. This paper proposes the use of Stochastic Petri Nets to assess the feasibility of ZK-Rollups, considering the impact of these solutions on throughput, latency, and the cost-benefit relationship, including the average transaction cost and its relationship to performance metrics. The results show that increased adoption of transactions in <em>Layer-2</em> can increase system throughput by up to 20 %, rising from 85 tps in an environment without <em>Layer-2</em> to 105 tps when 90 % of transactions follow this path. On the other hand, latency can increase by more than 100 % when larger <em>batches</em> are used in <em>Layer-2</em>, highlighting a trade-off. While batching improves throughput by reducing per-transaction overhead, it also delays the finalization of individual transactions.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108316"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785034","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":"Automatic tuning based on hardware performance counters and machine learning","authors":"Suren Harutyunyan Gevorgyan ,&nbsp;Eduardo César ,&nbsp;Anna Sikora ,&nbsp;Jiří Filipovič ,&nbsp;Jordi Alcaraz","doi":"10.1016/j.future.2025.108358","DOIUrl":"10.1016/j.future.2025.108358","url":null,"abstract":"<div><div>This paper presents a Machine Learning (ML) methodology for automatically tuning parallel applications in heterogeneous High Performance Computing (HPC) environments using Hardware Performance Counters (HwPCs). The methodology addresses three critical challenges: counter quantity versus accessibility tradeoff, data interpretation complexity, and dynamic optimization needs. The introduced ensemble-based methodology automatically identifies minimal yet informative HwPC sets for code region identification and tuning parameter optimization. Experimental validation demonstrates high accuracy in predicting optimal thread allocation ( &gt; 0.90 K-fold accuracy) and thread affinity ( &gt; 0.95 accuracy) while requiring only 4–6 HwPCs. Compared to search-based methods like OpenTuner, the methodology achieves competitive performance with dramatically reduced optimization time. The architecture-agnostic design enables consistent performance across CPU and GPU platforms. These results establish a foundation for efficient, portable, automatic, and scalable tuning of parallel applications.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108358"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893756","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":"Log-Tree: Building log-enhanced B+-tree for hybrid DRAM/PM main memories","authors":"Zhengzhu Yao,&nbsp;Chaoshu Yang,&nbsp;Runyu Zhang,&nbsp;Hai Yang,&nbsp;Yu Peng","doi":"10.1016/j.future.2025.108332","DOIUrl":"10.1016/j.future.2025.108332","url":null,"abstract":"<div><div>B<span><math><msup><mrow></mrow><mo>+</mo></msup></math></span>-trees are widely used in storage systems, which have been optimized to match the characteristics of the Persistent Memories (PMs) in recent studies. However, existing DRAM/PM hybrid B<span><math><msup><mrow></mrow><mo>+</mo></msup></math></span>-trees still induce write performance penalties, low space utilization, and slow recovery, which are caused by two critical design limitations: (1) massive random writes can lead to severe write performance degradation due to the asymmetric sequential/random write performance of PM; (2) trade-offs among write performance, PM space utilization, and recovery. In this paper, we propose a log-structured B<span><math><msup><mrow></mrow><mo>+</mo></msup></math></span>-tree for hybrid DRAM/PM main memory, called Log-Tree, to solve these problems. First, Log-Tree incorporates a <em>block</em>-grained shadow layer of leaf nodes in PM and designs a lightweight metadata for each block. Then, Log-Tree persists the newly-inserted entry into the corresponding blocks sequentially to reduce cacheline flushes. Finally, Log-Tree employs a dynamic data migration strategy among all blocks to further improve the space utilization of PM. We conducted comprehensive evaluations on the Intel Optane DCPMM platform. Compared with <em>μ</em>Tree/FPTree/CCL-BTree/FAST&amp;FAIR/SSB-Tree, Log-Tree achieves the highest PM space utilization while providing 4.81/1.23 × , 2.99/1.36 × , 1.46/0.99 × , 4.03/1.59 × , and 4.23/1.99 ×  write/read throughput on average, respectively.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108332"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823156","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}