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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

共 34218 条记录，以下是41-50 订阅

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Enabling grid Stability: Harnessing μPMU Data for Data-Driven Analysis of grid Frequency Events 12th

Enabling Grid Stability: Harnessing μPMU Data for Data-Driv...

引用

12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

作者： Singh, Uttam Dey, Maitreyee Patel, Preeti Department of Computer Science and Applied Computing London Metropolitan University 166-220 Holloway Rd LondonN7 8DB United Kingdom

ISBN: (纸本)9789819601462

The surge in distributed energy resources connected to the power grid has significantly heightened challenges for grid operators. While the integration of micro-synchrophasor unit (μPMU) into the power grid infrastructure has facilitated the acquisition of high-resolution data, analyzing such vast datasets presents its own set of challenges. This paper offers insights into fifty days of μPMU data through data analysis techniques focusing on frequency events within the operational and hazardous ranges. Drawing from fifty days of real-world data collected from grid-connected solar farm in England, this paper examines and analyzes multiple frequency events while enhancing comprehension of their diverse patterns. Additionally, this study has explored the isolation forest model for anomalous event detection, with results validated against power quality data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Power grids

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Research Support for Key Technologies for distributed computing grid Investment Analysis

Research Support for Key Technologies for Distributed Comput...

引用

2024 international conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Zhang, Geli Li, Tong Wang, Xiaohui Cheng, Wei Zhang, Heng Wang, Yujie Dept. Technical and Economic Center State Power Economic Research Institute Beijing China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

With the high proportion of new source loads such as distributed power sources, electric vehicles, and energy storage, as well as the rapid development of new models and formats such as microgrids, integrated energy, and virtual power plants, the distribution network is gradually transforming from a power network that simply receives and distributes electricity to users to a power network that integrates source grid load storage interaction and flexibly couples with the higher-level power grid. In recent years, State grid Economic and Technological Research Institute has focused on original technology research and development in the field of distribution networks, fully supporting the construction of original technology source areas. It has developed a distribution network simulation calculation and analysis platform with completely independent intellectual property rights, supporting the construction of new distribution systems in multiple regions. The development of new source loads may lead to local concentration and overall imbalance, resulting in significant differences in load rates between different regions and lines of the distribution network. This often leads to issues of insufficient or unusable equipment capacity, which is not conducive to improving the overall quality and efficiency of the distribution network. The State grid Economic and Technological Research Institute, in collaboration with the United Nations grid Tianjin Electric Power Company, has established a flexible interconnection technology system for distribution networks, developed the world's first set of corresponding flexible interconnection power electronic equipment, and implemented it in the Beichen National Industry City Integration Demonstration Zone in Tianjin. After the project is put into operation, the load balancing degree of the demonstration zone's power grid has reached over 99%, the power supply capacity of the power grid has increased by nearly 40%, and important industria

关键词： Power distribution networks

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O(1)-Round MPC Algorithms for Multi-Dimensional grid Graph Connectivity, Euclidean MST and DBSCAN 28

O(1)-Round MPC Algorithms for Multi-Dimensional Grid Graph C...

引用

28th international conference on Database Theory, ICDT 2025

作者： Gan, Junhao Wirth, Anthony Zhang, Zhuo The University of Melbourne Australia

ISBN: (纸本)9783959773645

In this paper, we investigate three fundamental problems in the Massively parallel Computation (MPC) model: (i) grid graph connectivity, (ii) approximate Euclidean Minimum Spanning Tree (EMST), and (iii) approximate DBSCAN. Our first result is a O(1)-round Las Vegas (i.e., succeeding with high probability) MPC algorithm for computing the connected components on a d-dimensional c-penetration grid graph ((d, c)-grid graph), where both d and c are positive integer constants. In such a grid graph, each vertex is a point with integer coordinates in Nd, and an edge can only exist between two distinct vertices with ∞-norm at most c. To our knowledge, the current best existing result for computing the connected components (CC’s) on (d, c)-grid graphs in the MPC model is to run the state-of-the-art MPC CC algorithms that are designed for general graphs: they achieve O(log log n + log D) [10] and O(log log n + logλ1) [8] rounds, respectively, where D is the diameter and λ is the spectral gap of the graph. With our grid graph connectivity technique, our second main result is a O(1)-round Las Vegas MPC algorithm for computing approximate Euclidean MST. The existing state-of-the-art result on this problem is the O(1)-round MPC algorithm proposed by Andoni et al. [5], which only guarantees an approximation on the overall weight in expectation. In contrast, our algorithm not only guarantees a deterministic overall weight approximation, but also achieves a deterministic edge-wise weight approximation. The latter property is crucial to many applications, such as finding the Bichromatic Closest Pair and Single-Linkage Clustering. Last, but not least, our third main result is a O(1)-round Las Vegas MPC algorithm for computing an approximate DBSCAN clustering in O(1)-dimensional Euclidean space. © Junhao Gan, Anthony Wirth, and Zhuo Zhang.

关键词： Trees (mathematics)

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

20th IFIP WG 10.3 International Conference on Network and Pa...

引用

20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

ISBN: (纸本)9789819628636

The proceedings contain 76 papers. The special focus in this conference is on Network and parallel computing. The topics include: AsymFB: Accelerating LLM Training Through Asymmetric Model parallelism;DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Friendly Data Communication and parallelism Strategies;Diagnosability of the Lexicographic Product of Paths and Complete Bipartite Graphs Under PMC Model;DTuner: A Construction-Based Optimization Method for Dynamic Tensor Operators Accelerating;Efficient Implementation of the LOBPCG Algorithm on a CPU-GPU Cluster;HP-CSF: An GPU Optimization Method for CP Decomposition of Incomplete Tensors;JediGAN: A Fully Decentralized Training of GAN with Adaptive Discriminator Averaging and Generator Selection;optimizing Vo-Viso: A Modified Methodology to parallel computing with Isolating Data in Memristor Arrays;parallel Computation of the Combination of Two Point Operations in Conic Curves Cryptosystem over GF(2n) Using Tile Self-assembly;parallel Construction of Independent Spanning Trees on 3-ary n-cube Networks;SpecInF: Exploiting Idle GPU Resources in distributed DL Training via Speculative Inference Filling;swDarknet: A Heterogeneous parallel Deep Learning Framework Suitable for SW26010 Pro Processor;VConv: Autotiling Convolution Algorithm Based on MLIR for Multi-core Vector accelerators;ACH-Code: An Efficient Erasure Code to Reduce Average Repair Cost in Cloud Storage Systems of Multiple Availability Zones;CMS: A Computility Resource Status Management and Storage Framework;fast Memory Disaggregation with SwiftSwap;HASLB: Huge Page Allocation Strategy Optimized for Load-Balance in parallel computing Programs;lightFinder: Finding Persistent Items with Small Memory;miDedup: A Restore-Friendly Deduplication Method on Docker Image Storage Systems;SPLR: A Selective Packet Loss Recovery for Improved RDMA Performance;a Cluster-Based Platoon Formation Scheme for Realistic Automated Vehicle Platooning;AnaNET: Anatomical Network fo

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Scheduling Big Machine Learning Tasks on Clusters of Heterogeneous Edge Devices 17

Scheduling Big Machine Learning Tasks on Clusters of Heterog...

引用

17th international conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Mathkar, Saumya Aiyer, Shreya Bapat, Yashovardhan Pinki, Pinki Paul, Arnab K. Naik, Vinayak BITS Pilani Department of CSIS Goa India

ISBN: (纸本)9798331531195

Edge computing has transformed machine learning by using computing closer to the data sources, thereby reducing latency. The ever-increasing volume of data has necessitated forming clusters of edge devices, possibly with heterogeneous capabilities. Managing heterogeneous resources such as computation and memory remains challenging. Given the capabilities of the edge devices, we need a simple technique suitable for an Edge computing *** introduce a scheduling mechanism that leverages Integer Linear Programming (ILP) to optimize the overall computation time of ML-based tasks. We implement our scheduling mechanism that efficiently allocates resources, ensuring tasks are executed parallel across the cores of edge devices in a cluster by minimizing computation time. For tasks large enough to fit on any core, we leverage distributed learning to train the model in pieces and later combine them. We employ our ILP-based scheduler for efficient task allocation and compare its performance with a Greedy, simple approach based on best-fit technique. We evaluate our approach on three cases of sets of tasks on the spectrum of uniformity and size. Our results demonstrate two times the speed gain for our ILP-based approach over Greedy for the category with the least uniformity and large size. © 2025 IEEE.

关键词： distributed computing Edge computing Optimization

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

20th IFIP WG 10.3 International Conference on Network and Pa...

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

ISBN: (纸本)9789819628292

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Proceedings of the 15th international conference on Cloud computing and Services Science, CLOSER 2025

Proceedings of the 15th International Conference on Cloud Co...

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15th international conference on Cloud computing and Services Science, CLOSER 2025

ISBN: (纸本)9789897587474

The proceedings contain 28 papers. The topics discussed include: performance and usability implications of multiplatform and WebAssembly containers;operations patterns for hybrid quantum applications;optimization of cloud-native application execution over the edge cloud continuum enabled by DVFS;energy-aware node selection for cloud-based parallel workloads with machine learning and infrastructure as code;security-aware allocation of replicated data in distributed storage systems;performance analysis of mdx ii: a next-generation cloud platform for cross-disciplinary data science research;data orchestration platform for AI workflows execution across computing continuum;framework for decentralized data strategies in virtual banking: navigating scalability, innovation, and regulatory challenges in Thailand;and anomaly detection for partially observable container systems based on architecture profiling.

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A Cloud+HPC hub for astrophysics and cosmology communities 33

A Cloud+HPC hub for astrophysics and cosmology communities

引用

33rd Euromicro international conference on parallel, distributed, and Network-Based Processing, PDP 2025

作者： Pasqui, Matteo INFN Sezione di Roma Tor Vergata ICSC Centro Nazionale di Ricerca in HPC Big Data and Quantum Computing Italy

ISBN: (纸本)9798331524937

The advancement of data-intensive analysis has emphasised the need for extensive, scalable environments that enable easy access to diverse computational resources. This project proposes the development of a Scientific Hub for the astrophysics and cosmology communities, leveraging a global, cloud-based infrastructure that integrates Kubernetes-based offloading to ensure efficient, user-friendly access to High Performance computing (HPC), High Throughput computing (HTC) and cloud resources. This solution introduces 'transparent offloading' of containerized workloads using Kubernetes API primitives, creating a unified cloud-native interface that abstracts complexities and optimally allocates resources based on the specific needs of each task. Through the Scientific Hub, users could access JupyterHub-supported environments with configurable resources, persistent data storage and secure authentication, facilitating the execution and sharing of scientific workflows in a robust, scalable manner. This contribution showcases the aims and challenges of the development of the Scientific Hub on ICSC partners' resources (including the Leonardo supercomputer at CINECA), along with some key use cases. © 2025 IEEE.

关键词： Dark Matter

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Analysis of Time Synchronization Challenges in Digital Twins for Edge-Enabled Data Centers in Smart Cities Scenario 33

Analysis of Time Synchronization Challenges in Digital Twins...

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33rd Euromicro international conference on parallel, distributed, and Network-Based Processing, PDP 2025

作者： Rinaldi, Stefano Iacono, Salvatore Dello Bellagente, Paolo Pasetti, Marco University of Brescia Department of Information Engineering Brescia Italy

ISBN: (纸本)9798331524937

The rapid adoption of Internet of Things (IoT) technologies and digital twins (DTs) is revolutionizing smart cities by facilitating improved real-time monitoring, simulation, and optimization of urban infrastructures. In data centers, digital twins offer virtual representations of physical infrastructure, enabling real-time monitoring, optimization of energy consumption, cooling efficacy, and resource utilization. This integration seeks to improve energy efficiency and sustainability in smart cities. In this context, edge computing is becoming essential solutions to mitigate latency, bandwidth, and privacy issues linked to centralized cloud system. A significant challenge in utilizing digital twins for edge-enabled data centers is attaining accurate time synchronization between the digital twin and its physical equivalent. Precise synchronization is crucial for real-time state representation, facilitating efficient monitoring, control, and optimization. The distributed characteristics of edge devices, varying network conditions, and constrained computational resources present considerable synchronization challenges. Factors such as clock drift, network jitter, and protocol constraints can result in temporal inconsistencies, compromising the reliability of digital twin applications. This paper offers an extensive examination of time synchronization issues in digital twins for edge-enabled data centers within smart city contexts. The study examines essential factors affecting synchronization accuracy and reliability, identifies significant bottlenecks, and suggests strategies to alleviate their impact. The results aid in the creation of effective synchronization mechanisms, facilitating the implementation of dependable digital twin systems. © 2025 IEEE.

关键词： Mobile edge computing

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parallel computing Optimization of Tourism Big Data based on Crawler Programs 1

Parallel Computing Optimization of Tourism Big Data based on...

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1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Zhou, Xiaoyu Wuhan Donghu University School of Management Hubei Wuhan China

ISBN: (纸本)9798331529246

With the rapid development of the tourism industry, traditional tourism methods are undergoing significant transformation, and online tourism is gradually becoming a new highlight in the market. However, faced with the challenges of big data, online tourism data experiences tremendous pressure in terms of storage, computation, and management, and the issue of information overload seriously affects the user experience. Therefore, there is an urgent need for innovative platforms and recommendation algorithms to optimize the screening and recommendation of tourism information. This paper proposes an optimization scheme based on the Spark cloud computing platform to improve the efficiency of tourism data processing. First, utilizing Spark's distributed computing and memory management capabilities, tourism data can be stored and processed in a distributed manner, thus accelerating the computation speed of recommendation algorithms. Secondly, a Python-written crawler program is used to obtain real tourism data, including user ratings and comments on attractions, to evaluate the effectiveness of the improved algorithm. Finally, a weighted algorithm combining the LDA topic model is proposed. This algorithm analyzes the topic distribution of comment texts and weights the comments based on user ratings, calculating the predicted score for unrated attractions for the target user, thereby achieving accurate attraction recommendations. The experimental results show that the algorithm proposed in this study demonstrates significant advantages in prediction accuracy. © 2025 IEEE.

关键词： Cloud platforms

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