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

On-device Artificial Intelligence solutions with applications to Smart Environments 设备上的人工智能解决方案及其在智能环境中的应用

IF 7.5 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2026-01-14 DOI: 10.1016/j.future.2026.108373

Fabrizio De Vita, Dario Bruneo, Sajal K. Das

引用次数: 0

Leveraging Cutting-Edge High Performance Computing for Large-Scale Applications 利用尖端的高性能计算大规模应用程序

IF 7.5 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2026-01-14 DOI: 10.1016/j.future.2026.108374

Claude Tadonki, Gabriele Mencagli, Leonel Sousa

引用次数: 0

IRL-D3QN: An intelligent multi-agent learning framework for dynamic spectrum management in vehicular networks 基于IRL-D3QN的车辆网络动态频谱管理智能多智能体学习框架

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2026-01-10 DOI: 10.1016/j.future.2026.108371

Jing Wang , Wenshi Dan , Ke Yang , Xing Tang , Lingyu Yan

The proliferation of vehicular networks within intelligent transportation systems (ITS) has significantly increased the demand for efficient and adaptive spectrum resource allocation. Spectrum coordination is challenging due to high vehicle traffic, intensive communication environments and diversified service requirements. These are of particular significance in Vehicle-to-Everything (V2X) communications, where adaptive conditions call out powerful solutions. Multi-agent reinforcement learning (MARL) techniques are promising and have been applied to the management of dynamic spectrum access, but with limitations including overestimated value functions, unsteady policy convergence, and dependence on manual choices of rewards, these techniques have limitations as far as their application in practice. This paper presents a new framework of spectrum management IRL-D3QN, which combines Inverse Reinforcement Learning (IRL) and a Dueling Double Deep Q-Network (D3QN). This algorithm involves a prediction network of rewards on determining intrinsic motivation according to its interplay with environments, eliminating the necessity of a danger of designing rewards manually. This enhances generalization in various situations. The dueling network design contributes to learning that is more stable because it keeps the values of state and values of the action apart. In the meantime, the bias of overestimation is minimized in the case of double q-learning. It has been demonstrated through simulations that IRL-D3QN can support a higher Vehicle to Infrastructure (V2I) transmission rate by 7.94 percent and demonstrate significantly less performance degradation under heavy communication loads than state of the art RL algorithms. Therefore, it will provide a solution to the distribution of dynamic spectrum, which will be scalable and self-sufficient in the next generation of vehicular communication systems.

智能交通系统（ITS）中车辆网络的激增显著增加了对高效和自适应频谱资源分配的需求。由于高车流量、密集通信环境和多样化的业务需求，频谱协调具有挑战性。这在车联网（V2X）通信中尤为重要，因为自适应条件需要强大的解决方案。多智能体强化学习（MARL）技术很有前途，已经应用于动态频谱接入的管理，但由于存在价值函数高估、策略收敛不稳定以及依赖人工选择奖励等局限性，这些技术在实际应用中存在局限性。本文提出了一种新的频谱管理框架IRL-D3QN，该框架将逆强化学习（IRL）和Dueling双深度Q-Network （D3QN）相结合。该算法包含一个奖励预测网络，根据其与环境的相互作用来确定内在动机，从而消除了手动设计奖励的必要性。这增强了在各种情况下的泛化。决斗网络设计有助于学习更稳定，因为它使状态值和动作值分开。同时，在双q学习的情况下，高估的偏差被最小化。通过仿真已经证明，IRL-D3QN可以支持7.94%的更高的车辆到基础设施（V2I）传输速率，并且在高通信负载下的性能下降明显小于最先进的RL算法。因此，它将为下一代车载通信系统提供可扩展和自给自足的动态频谱分配解决方案。

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引用次数: 0

Striking the balance between speed and compression ratio: A fast bit-grouping algorithm and adaptive compressor selection for scientific data 在速度和压缩比之间取得平衡：科学数据的快速位分组算法和自适应压缩器选择

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2026-01-10 DOI: 10.1016/j.future.2026.108370

Michael Middlezong

High-performance computing (HPC) systems have enabled unprecedented advancements in scientific simulation, producing larger and larger quantities of data to be analyzed. The resulting storage and I/O overheads present a significant bottleneck to scientific workflows. While many compression algorithms have been developed to address the issue, achieving the optimal balance between compression ratio and throughput remains a challenge. Furthermore, strict error bound requirements are inadequately addressed by current solutions. This paper introduces GRASP, a fast bit-grouping compressor that leverages the local smoothness of data to achieve high throughput while maintaining competitive compression ratios under tight error constraints. For the purposes of compressor selection, we also propose a novel efficiency metric that considers both compression and I/O performance, allowing the user to make an informed decision about which compressor to use. We also develop an adaptive compression selection framework based on this metric, using sampling to determine at runtime the optimal compressor for specific use cases. Experimental results across six diverse datasets demonstrate that GRASP outperforms traditional error-bounded compressors such as SZ3 and ZFP in speed while achieving similar compression ratios under tight error bounds. Additionally, we assess scenarios in which a naive compressor selection fails to select the optimal compressor, demonstrating the importance of an adaptive compressor selection framework. These contributions provide a practical approach to balancing speed and compression ratio in modern scientific data management.

高性能计算（HPC）系统使科学模拟取得了前所未有的进步，产生了越来越多的需要分析的数据。由此产生的存储和I/O开销是科学工作流程的一个重要瓶颈。虽然已经开发了许多压缩算法来解决这个问题，但在压缩比和吞吐量之间实现最佳平衡仍然是一个挑战。此外，当前的解决方案没有充分解决严格的错误边界要求。本文介绍了一种快速的位分组压缩器GRASP，它利用数据的局部平滑性来实现高吞吐量，同时在严格的错误约束下保持有竞争力的压缩比。为了选择压缩机，我们还提出了一种考虑压缩和I/O性能的新型效率指标，允许用户对使用哪种压缩机做出明智的决定。我们还基于该指标开发了一个自适应压缩选择框架，使用采样在运行时确定特定用例的最佳压缩器。在六个不同数据集上的实验结果表明，GRASP在速度上优于传统的错误有界压缩器（如SZ3和ZFP），同时在严格的错误界限下获得相似的压缩比。此外，我们还评估了原始压缩机选择无法选择最佳压缩机的情况，证明了自适应压缩机选择框架的重要性。这些贡献为现代科学数据管理中平衡速度和压缩比提供了一种实用的方法。

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引用次数: 0

BiD-Accel: Accelerated bidimensional input-aware SDC vulnerability assessment for GPU static instructions BiD-Accel: GPU静态指令加速二维输入感知SDC漏洞评估

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2026-01-08 DOI: 10.1016/j.future.2026.108372

Zhenyu Qian , Lianguo Wang , Pengfei Zhang , Jianing Rao

Graphics Processing Units (GPUs) are increasingly used in safety-critical systems where Silent Data Corruptions (SDCs) pose severe risks. Selective Instruction Duplication (SID) can mitigate these risks but relies on accurate static-instruction vulnerability assessment, which is complicated by variations in input values and sizes. This paper presents a comprehensive study of how input characteristics shape instruction-level SDC vulnerability, which we quantify using the Static Instruction Error Probability (SIEP) and the SDC Occurrence rate (SDCO). We extend gpuFI-4 to enable fault injection mapping at the static-instruction level. Across 14 benchmarks and more than ten million single-, double-, and triple-bit injections, we find that SIEP is largely value-insensitive, whereas SDCO is highly value-sensitive. For register instructions, SDCO remains stable for random and structured-sparse inputs but differs markedly for all-zero, NaN, or denormal inputs. Moreover, when SIEP is size-sensitive, SDCO also tends to exhibit size sensitivity. We further observe that invalid-injection rates decrease with input size and that shared-memory instructions, though few, can contribute disproportionately to SDCs. Leveraging these insights, we propose BiD-Accel, a bi-dimensional, input-aware framework for accelerated static-instruction SDC vulnerability assessment. Its SIEP-driven Descending Order Sort (DOS) method achieves stable SDCO rankings with injections on only 70.4% of instructions on average, compared with 86.2% for the Random Ordering (RO) method, thereby meaningfully reducing assessment cost while preserving ranking fidelity and providing actionable guidance for robust SID under input-varying GPU workloads.

图形处理单元（gpu）越来越多地用于安全关键系统，在这些系统中，静默数据损坏（sdc）会带来严重的风险。选择性指令复制（SID）可以减轻这些风险，但它依赖于准确的静态指令漏洞评估，而输入值和大小的变化使其变得复杂。本文全面研究了输入特征如何影响指令级SDC漏洞，并使用静态指令错误概率（SIEP）和SDC发生率（SDCO）对其进行量化。我们扩展了gpuFI-4，以在静态指令级别启用故障注入映射。通过14次基准测试和数千万次单、双、三比特注入，我们发现SIEP在很大程度上对值不敏感，但对大小敏感，而SDCO对值高度敏感。对于寄存器指令，SDCO对于随机和结构化稀疏输入保持稳定，但对于全零、NaN或非正常输入则明显不同。此外，当SIEP对规模敏感时，SDCO也表现出规模敏感性（Pearson r=0.609, p=1.85×10−5）。我们进一步观察到，无效注入率随输入大小而降低，共享内存指令虽然很少，但对sdc的贡献不成比例。利用这些见解，我们提出了BiD-Accel，这是一个双向的、输入感知的框架，用于加速静态指令SDC漏洞评估。其siep驱动的递减顺序排序（DOS）方法平均仅对70.4%的指令进行注入，实现了稳定的SDC漏洞排名，而随机排序（RO）方法的平均注入率为86.2%，从而大大降低了评估成本，同时保持了排名保真度，并为不同输入GPU工作负载下的鲁棒SID提供了可操作的指导。

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引用次数: 0

Dynamic task transmission control and improved greedy strategy for vehicular edge computing 车辆边缘计算的动态任务传输控制与改进贪婪策略

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2026-01-07 DOI: 10.1016/j.future.2026.108369

Sheng Cai , Jianmao Xiao , Yuanlong Cao , Qinghang Gao , Zhiyong Feng , Shuiguang Deng

With the rapid development of on-board applications, edge computing is now widely used in Internet of vehicles, enabling vehicles with limited resources to offload tasks to the edge for execution via computation offloading. However, current research methods are often hard to adapt to dynamic scenarios due to model training costs and vehicle mobility and also lack consideration for load balancing in high-load situations. To improve the quality of experience of users and balance the load of edge servers simultaneously, this paper proposes an improved greedy strategy method for computation offloading. First, to mitigate potential communication overload during peak hours, this study analyzes the relationship between transmission scheduling and execution queues, and investigates a dynamic task transmission control method. Second, explicit modeling of round-trip communication reliability in mobile environments is provided to extend the vehicle interconnection model. Subsequently, by analyzing the structure of the optimal solution for total latency optimization, the priority of offloaded tasks is classified. A multi-perspective analysis of task offloading is then conducted, and a greedy strategy is adopted to ensure both the quality of user experience and load balancing at the edge. Finally, comparative experiments on real-world datasets validate the efficiency of the proposed method and model under high-mobility and high-load experimental scenarios.

随着车载应用的快速发展，边缘计算已广泛应用于车联网，使资源有限的车辆可以通过计算卸载将任务卸载到边缘执行。然而，目前的研究方法由于模型训练成本和车辆移动性等因素，往往难以适应动态场景，也缺乏对高负载情况下负载平衡的考虑。为了提高用户的体验质量，同时平衡边缘服务器的负载，本文提出了一种改进的贪心策略计算卸载方法。首先，为了缓解高峰时段潜在的通信过载，本研究分析了传输调度与执行队列之间的关系，并研究了一种动态任务传输控制方法。其次，对移动环境下的往返通信可靠性进行了显式建模，扩展了车辆互联模型。然后，通过分析总时延优化最优解的结构，对卸载任务的优先级进行分类。然后对任务卸载进行多角度分析，采用贪心策略保证用户体验质量和边缘负载均衡。最后，通过对真实数据集的对比实验，验证了该方法和模型在高迁移率和高负载实验场景下的有效性。

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引用次数: 0

Large-scale HPC Approaches and Applications on Highly Distributed Platforms 大规模高性能计算方法及其在高度分布式平台上的应用

IF 7.5 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2026-01-07 DOI: 10.1016/j.future.2025.108365

Alessia Antelmi, Emanuele Carlini

The ever-increasing complexity of scientific and industrial challenges due to the enormous amount of data available nowadays requires advanced high-performance computing (HPC) solutions capable of processing and analyzing data efficiently on highly distributed platforms. Traditional centralized HPC systems frequently fall short of the demands of contemporary large-scale applications (e.g., large language models), prompting a move towards more flexible and scalable distributed computing environments. Furthermore, the growing emphasis on the environmental impact of large-scale computing has highlighted the need for sustainable computing practices that minimize energy consumption and carbon footprint. This special issue targets contributions that investigate both the challenges and the opportunities arising from this evolution. The accepted articles highlight enhancements in five key areas: (i) HPC in the cloud continuum, (ii) heterogeneous HPC architectures, performance tools, and programming models, (iii) parallel and distributed algorithms and applications, (iv) data management and storage systems, and (v) sustainable and energy-efficient HPC systems. In total, 29 submissions were received, and 20 papers were selected after a rigorous peer-review process. Collectively, these contributions provide a representative snapshot of current research efforts towards resilient, efficient, and sustainable HPC approaches and applications on highly distributed platforms.

由于目前可用的大量数据，科学和工业挑战的复杂性不断增加，需要能够在高度分布式平台上有效处理和分析数据的先进高性能计算（HPC）解决方案。传统的集中式HPC系统经常无法满足当代大规模应用程序（例如，大型语言模型）的需求，这促使人们转向更灵活和可扩展的分布式计算环境。此外，大规模计算对环境的影响日益受到重视，这突出了对可持续计算实践的需求，这些实践可以最大限度地减少能源消耗和碳足迹。本期特刊针对的是调查这一演变所带来的挑战和机遇的文章。被接受的文章强调了五个关键领域的增强：(i)云连续体中的HPC，（ii）异构HPC架构，性能工具和编程模型，（iii）并行和分布式算法和应用程序，（iv）数据管理和存储系统，以及(v)可持续和节能的HPC系统。总共收到了29份意见书，经过严格的同行评议过程，20篇论文被选中。总的来说，这些贡献提供了当前对高度分布式平台上弹性、高效和可持续的高性能计算方法和应用的研究工作的代表性快照。

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引用次数: 0

LE OS: A lightweight edge operating system for industrial internet of things under resource constraints LE OS：资源受限下的工业物联网轻量级边缘操作系统

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2026-01-07 DOI: 10.1016/j.future.2025.108360

Xianhui Liu , Yangyang Yang , Chenlin Zhu , Yihan Hu , Weidong Zhao

With the rise of Industry 4.0 and edge computing, intelligent manufacturing has undergone rapid development. However, existing research on operating systems for resource-constrained edge devices still exhibits significant limitations: mainstream operating systems require large hardware resources and lack adaptability for edge deployment; the industrial Internet lacks a unified and efficient scheduling and management framework for large-scale devices; and traditional monolithic systems suffer from tight component coupling, where a single component failure can cause system-wide crashes, threatening production stability. To address these challenges, this paper proposes LE OS, a lightweight edge operating system tailored for resource-constrained industrial Internet environments. LE OS leverages container technology to encapsulate system-level components into functional system containers and integrates them with the seL4 microkernel, forming a lightweight, containerized microkernel operating system.Experimental evaluation shows that LE OS improves CPU and I/O performance by 10%-40% and reduces system-level memory usage by over 70% compared with mainstream operating systems, while maintaining high resource efficiency and strong isolation. These results demonstrate that LE OS effectively overcomes the limitations of existing systems and provides a practical and scalable foundation for next-generation industrial Internet edge operating systems.

随着工业4.0和边缘计算的兴起，智能制造得到了快速发展。然而，现有的针对资源受限边缘设备的操作系统研究仍然存在明显的局限性：主流操作系统需要大量硬件资源，缺乏对边缘部署的适应性；工业互联网缺乏统一高效的大规模设备调度管理框架；传统的单片系统受到组件紧密耦合的影响，其中单个组件的故障可能导致系统范围的崩溃，从而威胁到生产的稳定性。为了应对这些挑战，本文提出了LE OS，这是一种为资源受限的工业互联网环境量身定制的轻量级边缘操作系统。LE OS利用容器技术将系统级组件封装到功能性系统容器中，并将它们与seL4微内核集成，形成轻量级的容器化微内核操作系统。实验评估表明，与主流操作系统相比，LE OS的CPU和I/O性能提高了10%-40%，系统级内存使用率降低了70%以上，同时保持了较高的资源效率和强隔离性。这些结果表明，LE OS有效地克服了现有系统的局限性，为下一代工业互联网边缘操作系统提供了实用和可扩展的基础。

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引用次数: 0

Distributed multi-objective consumer-centric routing for LoRa-based IoT-enabled FANET 基于lora的物联网FANET分布式多目标以消费者为中心的路由

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2026-01-06 DOI: 10.1016/j.future.2026.108368

Omer Chughtai , Muhammad Waqas Rehan , Muhammad Naeem , Ali Hamdan Alenezi , Sajjad Ali Haider

LoRa-enabled Flying Ad Hoc Networks (FANETs) offer long-range and energy-efficient connectivity for next-generation IoT and post-disaster communication infrastructures, yet their performance is fundamentally constrained by limited bandwidth, dynamic topologies, and uneven energy depletion across aerial nodes. This work develops a distributed consumer-centric multi-objective routing framework (Proposed-CCR) that jointly optimizes residual energy, link quality, and flow-level priority through a lightweight utility-driven forwarding mechanism. The design integrates composite cost modeling, two-hop neighbor awareness, adaptive path monitoring, and local repair to ensure scalable, resilient, and delay-aware multi-hop communication. Extensive simulations demonstrate that Proposed-CCR reduces per-packet energy consumption by 28–35%, extends network lifetime by over 35%, and decreases high-priority flow delay by nearly 40% relative to state-of-the-art schemes including MinHop, ACOR, GCCR, and BBCCR. These results confirm the effectiveness of a consumer-centric, LoRa-aware multi-objective heuristic for UAV-IoT integration and emergency communication scenarios, while highlighting practical opportunities for sustainable and resource-efficient airborne networking architectures.

基于lora的飞行自组织网络（fanet）为下一代物联网和灾后通信基础设施提供了远程和节能的连接，但其性能从根本上受到带宽有限、动态拓扑和空中节点间能量消耗不均匀的限制。本研究开发了一个以消费者为中心的分布式多目标路由框架（Proposed-CCR），通过轻量级实用驱动的转发机制共同优化剩余能量、链路质量和流级优先级。该设计集成了复合成本建模、两跳邻居感知、自适应路径监控和本地修复，以确保可扩展、弹性和延迟感知的多跳通信。大量的仿真表明，与MinHop、ACOR、GCCR和BBCCR等最先进的方案相比，提议的ccr将每包能耗降低了28-35%，将网络寿命延长了35%以上，并将高优先级流延迟降低了近40%。这些结果证实了以消费者为中心、lora感知的多目标启发式方法在无人机-物联网集成和应急通信场景中的有效性，同时强调了可持续和资源高效的机载网络架构的实践机会。

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引用次数: 0

Edge-based proactive and stable two-tier routing for IoV 基于边缘的主动稳定的两层车联网路由

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2026-01-06 DOI: 10.1016/j.future.2026.108367

Asif Mehmood , Muhammad Afaq , Faisal Mehmood , Wang-Cheol Song

The Internet of Vehicles (IoV) is an evolving domain fueled by advancements in vehicular communications and networking. To enhance vehicle coverage, integrating vehicle-to-everything (V2X) networks with cellular networks has become essential, though this integration places increased demand on cellular infrastructure. To address this, we propose a two-tier stable path routing algorithm designed to improve the stability and proactiveness of V2X networks. Our approach divides the coverage area into zones, further segmented into road segments based on road structure. The first tier manages routing within a road segment, while the second tier handles routing between vehicles in adjacent segments. This method improves road awareness, stabilizes topologies, adapts to dynamic changes, and reduces routing overhead. Additionally, the incorporation of Kalman filter-based prediction model further strengthens proactive routing. To validate the proposed approach, we conduct synthetic evaluations across varying vehicular densities with different mobility and traffic scenarios. We compare the traditional centralized routing strategy with the proposed distributed two-tier mechanism to assess execution cost, end-to-end latency, network resource consumption, data rates, packet flow, and packet loss. Quantified results demonstrate that our two-tier approach reduces the average execution cost from 438.61 to 230.48, lowers average latency from 232.34 ms to 129.03 ms, and minimizes average network consumption from 231.26 MB to 129.39 MB. The proposed approach continues to significantly enhance data rates, reduce packet flow processing, decrease packet loss across various routing strategies. Overall, the proposed solution enhances stability, responsiveness, and robustness of V2X communication, making it suitable for future large-scale IoV deployments.

车联网（IoV）是一个不断发展的领域，受到汽车通信和网络技术进步的推动。为了提高车辆覆盖范围，将V2X网络与蜂窝网络集成变得至关重要，尽管这种集成对蜂窝基础设施的需求增加了。为了解决这个问题，我们提出了一种两层稳定路径路由算法，旨在提高V2X网络的稳定性和主动性。我们的方法将覆盖区域划分为区域，并根据道路结构进一步细分为道路段。第一层管理路段内的路线，而第二层处理相邻路段车辆之间的路线。该方法提高了道路感知能力，稳定了拓扑结构，适应动态变化，减少了路由开销。此外，结合基于卡尔曼滤波的预测模型，进一步加强了主动路由。为了验证所提出的方法，我们在不同的车辆密度、不同的机动性和交通场景下进行了综合评估。我们将传统的集中式路由策略与提出的分布式两层机制进行比较，以评估执行成本、端到端延迟、网络资源消耗、数据速率、数据包流和数据包丢失。量化结果表明，我们的两层方法将平均执行成本从438.61降低到230.48，将平均延迟从232.34 ms降低到129.03 ms，并将平均网络消耗从231.26 MB降低到129.39 MB。所提出的方法继续显著提高数据速率，减少数据包流处理，减少各种路由策略之间的数据包丢失。总体而言，该解决方案增强了V2X通信的稳定性、响应性和鲁棒性，适用于未来的大规模车联网部署。

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引用次数: 0