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检索条件"任意字段=23th IEEE International Conference on Distributed Computing Systems"

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distributed k-Hop Query Powered by an Asynchronous Framework 25th

Distributed k-Hop Query Powered by an Asynchronous Framewo...

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25th international conference on Web Information systems Engineering, WISE 2024

作者： Zheng, Jinlong Lu, Yujie Zheng, Weiguo School of Data Science Fudan University Shanghai China

ISBN: (纸本)9789819605781

the k-hop query represents a fundamental challenge in various graph applications, often supported by numerous distributed systems. the conventional approach to this query paradigm typically involves iterative layer-by-layer expansion, which inevitably increases redundant vertices as the hop count grows. Moreover, issues such as master node idle waiting and workload imbalances across computing nodes contribute to the wastage of computational resources and time. To tackle these challenges, this paper introduces an asynchronous framework for k-hop queries, namely ADFQ, designed for seamless integration into any distributed system. Diverging from the traditional vertex-centric techniques, this framework employs distributed machines to store graph blocks and process queries without waiting during deduplication between hops. By ensuring comprehensive deduplication at each hop’s conclusion and efficiently expanding frontiers, the framework significantly improves query performance. Experimental results demonstrate superior performance compared to the classic Bulk Synchronous Parallel paradigm, particularly as graph imbalances intensify. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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Optimizing Latency and Communication in Federated Edge computing with LAFEO and Gradient Compression for Real-Time Edge Analytics 6

Optimizing Latency and Communication in Federated Edge Compu...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Namratha, P. Chaganti, Koushik Reddy Elicherla, Sivananda Lahari Reddy Guddati, Surekha Swarna, Akarapu thiramal Reddy, P. Department of CSE GATES Institute of Technology and Science Anantapur India Dept. of Computer and Information Systems University of the Cumberlands KY United States Dept of CSE Dayananda Sagar University Bangalore India Dept of CSE Vidya Jyothi Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

the increasing need for real-time data analytics in edge computing is furthered by the latency pressures from computational and communication bottlenecks, thus demanding novel solutions. Fundamental approaches to federated learning and distributed machine learning do not accommodate changes in dynamic latency conditions very well, thereby being inefficient in terms of synchronization, communication overhead, and delay in task execution. this paper presents a holistic framework consisting of three innovative approaches to solve these problems: Latency-Aware Federated Edge-Aware Optimization (LAFEO), Latency-Driven Gradient Compression with Adaptive Sparsity (LAGCAS), and Predictive Latency Compensation with Dynamic Replication (PLCDR). the LAFEO framework consists of a double-layer optimization framework, combining latency-weighted scheduling with reinforcement learning to dynamically adapt the synchronization frequencies. this results in 25-30% end-to-end latency reduction with an accuracy deviation that is capped at 2% centralized models. LAGCAS reduces communication overhead by applying adaptive gradient scarification methods, bringing compressions of gradients under real-time accuracy thresholds: it reduces 40% communication volume and accelerates convergence by 20%. PLCDR uses predictive modelling to predict latency bottlenecks and dynamically replicates critical computations across the edge nodes. It hence offers 15-25% decrease in the completion time of tasks and up to 30% enhancement in the resilience of system sets. the existing methods will be addressed with the key limitations due to the introduction of latency-aware mechanisms that adapt to real-time edge conditions along with high accuracy and efficiency. Simulation results have shown marked scalability improvements, latency reductions, and resilience;in its positioning, this work's effort is a transformative advancement in the latency-sensitive edge computing process. © 2025 ieee.

关键词： Federated learning

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computing Performance Analysis of IEC 61499 distributed Automation systems

Computing Performance Analysis of IEC 61499 Distributed Auto...

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26th international conference on Industrial Technology, ICIT 2025

作者： Lyu, Tuojian Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798331521950

this work analyzes the computing performance of distributed control systems in IEC 61499 on two hardware devices, Dell XPS workstation and Raspi 4B. Based on the test IEC 61499 application, three different system configurations are tested to simulate the distribution options of singleprocess and single-thread, multi-threading, and multi-process on both hardware platforms. Experimental results show that multiprocess configurations yield more consistent execution time due to dedicated resource allocation, while multi-threading, though resource-efficient, can lead to latency variability. these results aim at help in choosing optimal configurations and selecting hardware for performance in distributed automation systems. © 2025 ieee.

关键词： Parallel processing systems

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Spindle: Efficient distributed Training of Multi-Task Large Models via Wavefront Scheduling 25

Spindle: Efficient Distributed Training of Multi-Task Large ...

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30th ACM international conference on Architectural Support for Programming Languages and Operating systems, ASPLOS 2025

作者： Wang, Yujie Zhu, Shenhan Fu, Fangcheng Miao, Xupeng Zhang, Jie Zhu, Juan Hong, Fan Li, Yong Cui, Bin Peking University Beijing China Purdue University West LafayetteIN United States Alibaba Group Beijing China Peking University China China

ISBN: (纸本)9798400710797

Recent foundation models are capable of handling multiple tasks and multiple data modalities with the unified base model structure and several specialized model components. However, efficient training of such multi-task (MT) multi-modal (MM) models poses significant system challenges due to the sophisticated model architecture and the heterogeneous workloads of different tasks and modalities. In this paper, we propose Spindle, a brand new training system tailored for resource-efficient and high-performance training of MT MM models via wavefront scheduling. the key idea of Spindle is to decompose the model execution into waves and address the joint optimization problem sequentially, including both heterogeneity-aware workload parallelization and dependency-driven execution scheduling. We build our system and evaluate it on various MT MM models. Experiments demonstrate the superior performance and efficiency of Spindle, with speedup ratio up to 71% compared to state-of-the-art training systems. © 2025 ACM.

关键词： Wavefronts

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Fault Diagnosis Research of distributed Photovoltaic System Based on Support Vector Machine

Fault Diagnosis Research of Distributed Photovoltaic System ...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 ieee international conference on

作者： Xinran Wang Di Bai Shenyang Institute of Engineering Shenyang China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

In order to improve the precision and efficiency of fault diagnosis of distributed photovoltaic (PV) systems, an efficient fault diagnosis model is constructed by combining the classification ability of support vector machine and the advantages of distributed computing. the analysis results show that the model has a classification accuracy of 97.6%, a recall of 96.5% and an F1 value of 96.8% on the experimental dataset, which can adapt to a variety of operating states and fault scenarios, and maintains a high diagnostic performance under the condition of multiple fault superposition, with a classification accuracy of 91.8%-94.2%, demonstrating a strong applicability and robustness, and providing a reliable technological support for the intelligent operation and maintenance of PV systems. It shows strong applicability and robustness, and provides reliable technical support for intelligent operation and maintenance of PV systems.

关键词： Fault diagnosis Support vector machines Photovoltaic systems Industries Adaptation models Accuracy Computational modeling Robustness Maintenance distributed computing

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A 2-Dimensional mm-Scale Network-on-Textiles (kNOTs) for Wearable computing with Direct Die-to-Yarn Integration of 0.6×2.15mm2SoC and bySPI Chiplets 72

A 2-Dimensional mm-Scale Network-on-Textiles (kNOTs) for Wea...

引用

72nd ieee international Solid-State Circuits conference, ISSCC 2025

作者： Agrawal, Anjali Chen, Zhenghong Desman, Braden E. Wang, Jinhua Tanaka, Akiyoshi Foysal, Fahim Hess, Charlie D. Farrell, Will Owens, Jim Truesdell, Daniel S. Calhoun, Benton H. University of Virginia CharlottesvilleVA United States Nautilus Defense LLC PawtucketRI United States

ISBN: (纸本)9798331541019

this paper proposes a scalable, 2-dimensional network-on-textiles (kNOT) comprised of systems-on-chip (SoCs) and 'bySPI' networking chiplets that are jointly capable of supporting heterogeneous programming, multiple sensing modalities, and a distributed memory system. Emerging e-textiles must retain the flexibility and comfort of their host garment to be viable in wearable applications for healthcare, virtual reality, and sports [1]-[4]. Prior integration efforts have demonstrated textile computing by weaving flexible filament circuits [5]-[7], embroidering conductive yarns [8], and fabricating electronic fibers [9]-[10]. Still, these works suffer from a combination of bulky, rigid components, high cost, or 1-dimensionality (Fig. 37.2.1). To preserve the textile's look and feel, SoCs for fabric integration must also be highly miniaturized, feature minimal area-hungry IO interfaces, be easily programmable, and be fully integrable. the e-textile system in [11] integrated a health sensing chip onto a planar fashionable circuit board, yet the board (25×25mm2) is 40× larger than the chip itself. the System-in-Fiber from [12] is fully autonomous but has many IO pads, is limited to 1D networks, and requires an interposer (4.7×3.7mm2) that is 3.8× larger than the die. Most recently, a battery-less e-textile system in [13] integrates cm-scale harvesting tiles and an inductor onto a shirt, but it lacks an integrated SoC. Many wearable applications also demand substantial on-garment storage, yet large memories are unsuited for comfortable textile integration due to their sizable footprint. Our kNOT solution addresses this by replacing monolithic memory units with a distributed set of smaller memories. However, networking these chiplets via existing bus standards incurs significant area and power penalties [14]. Existing Body Area Networks highlight the potential of multi-chip solutions, but they lack seamless textile integration [15]-[17]. Hence, to truly realize a fabric comp

关键词： Network-on-chip

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DS-TFP: A distributed and Secure Traffic Flow Prediction Framework Based on Federated Graph Learning 18th

DS-TFP: A Distributed and Secure Traffic Flow Prediction Fr...

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18th international conference on Wireless Artificial Intelligent computing systems and Applications, WASA 2024

作者： Fu, Yu Sun, Lijun Ma, Zhihui Gao, Haoxiang Yan, Chaoying Qingdao University of Science and Technology Qingdao266061 China

ISBN: (纸本)9783031714665

Traffic flow prediction (TFP) plays a crucial role in intelligent transportation systems. Due to the need to collect data during the prediction process, TFP can face the risk of data leakage. Federated Graph Learning (FGL) is a novel method for protecting graph data privacy, but there are lower performance and Byzantine attacks challenges when applying FGL to TFP. In order to address these challenges, this paper proposes a distributed and secure traffic flow prediction (DS-TFP) framework based on federated graph learning, which includes three methods. Firstly, a New Spatial-Temporal Graph (NSTG) model is applied for the dynamic changes of temporal and spatial and simplifies the encoding procedures. Secondly, graph models are combined with federated method to ensure the protection of local data privacy. thirdly, the resist Byzantine attacks method which utilizes clients dynamic cross-validation and a voting-based aggregation algorithm, is designed to enhance system security. Finally, extensive experiments are conducted on two datasets. the results show that compared with the baselines, the Fed-NSTG model improved accuracy by an average of 8.15%. the DS-TFP framework can maintain an average prediction accuracy of 87.57% even under the Byzantine attacks. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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distributed MPC Compensation-Based LFC for Heterogeneous Multi-area Interconnected Power systems 8th

Distributed MPC Compensation-Based LFC for Heterogeneous Mul...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Chi, Xiaobo Li, Xinyi Liu, Xiaolin Jia, Xinchun School of Automation and Software Engineering Shanxi University Taiyuan030031 China School of Mathematical Sciences Shanxi University Taiyuan030006 China The Key Laboratory of Complex Systems and Data Science Ministry of Education Taiyuan030006 China

ISBN: (纸本)9789819602247

this paper addresses the distributed load frequency control (LFC) problem for multi-area interconnected power systems (MAIPSs) with thermal and wind power generations, considering both inter-area and intra-area transmission delays and random packet losses induced by communication networks. Firstly, a heterogeneous framework of MAIPSs is constructed, where a networked distributed model predictive control (DMPC) scheme is proposed to compensate for transmission delays and random packet losses. Based on this framework, we then developed a distributed closed-loop LFC system model for heterogeneous MAIPS. Subsequently, a Luenberger observer is constructed to estimate the system state of the local area, and a networked feedback controller is designed to ensure stability with a certain H∞ performance level for the networked MAIPSs. Moreover, stability conditions and controller design criteria are provided by using the Lyapunov-Krasovskii stability theory. Finally, the control performance of the proposed method is verified through an example of a two-area interconnected power system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive control systems

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Role of Edge computing in IoT

Role of Edge Computing in IoT

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international conference on Communication systems and Network Technologies (CSNT)

作者： Pragya Singh Preeti Gautam Namita Arya MITS Gwalior Centre of Internet of Things Madhya Pradesh India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

computing stands at a crossroads between speed and scale. the cloud promises vast computing power and storage, yet shuffling data to and from those distant servers takes time - often too much. Edge computing looks to resolve that dilemma by shifting processing closer to the devices generating all that data. Rather than relying solely on gargantuan centralized cloud servers, edge computing utilizes nearby devices and micro data centers to enable real-time, location-aware services and this distributed approach brings quicker responses and leaner loads by leveraging local gadgets to handle localized computing chores. However fully activating edge computing's potential requires surmounting a few obstacles first. Efficiently coordinating myriad devices for data storage and number-crunching poses logistical challenges. Integrating these decentralized nodes into the centralized cloud framework demands care and forethought. And securing expansive networks of hardware and software is no simple feat. Still, the promised gains in speed and adaptability compel this revolution in computing.

关键词： Cloud computing Technological innovation Standards organizations Real-time systems Software Hardware Security Servers Internet of things Edge computing

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A Novel distributed Economic Dispatch Method for Smart Grid with Time-Varying Feedback Gain 8th

A Novel Distributed Economic Dispatch Method for Smart Grid...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Guo, Hui Li, Xue Yang, Junlin Tian, Runze Shanghai University Shanghai200444 China Shanghai University of Finance and Economics Shanghai200433 China

ISBN: (纸本)9789819602315

the feedback gain in the traditional economic dispatch consensus algorithm is a fixed value, which leads to a slow convergence speed. To solve the problem, a novel distributed economic dispatch method for smart grid with time-varying feedback gain is proposed in this paper. First, by adding the direct cost, overestimation cost and underestimation cost of wind power to the objective function, an economic dispatch model considering wind power uncertainly is established, which can be flexibly scheduled under random wind speeds. Second, an improved consensus algorithm with time-varying feedback gain is proposed to solve the economic dispatch problem, which provides a design method of dynamic feedback gain instead of a fixed value and improves the convergence speed. Finally, several case studies are carried out in the ieee 14-bus system to illustrate the effectiveness and superiority of the proposed method. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Windmill

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