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

共 34256 条记录，以下是511-520 订阅

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Optimization Method for Microgrid Demand Response Based on Coherent Photonic Quantum computing 14

Optimization Method for Microgrid Demand Response Based on C...

引用

14th international conference on Power and Energy Systems, ICPES 2024

作者： Wan, Yinuo Tian, Guangyuan Xi, Haikuo Fang, Yong School of Economics and Management Beijing University of Chemical Technology Beijing China State Grid Jibei Electric Power Company Chengde Power Supply Company Chengde China

ISBN: (纸本)9798350391329

This paper presents an optimization strategy for microgrid operation using photonic quantum computing. The proposed approach facilitates the formulation of complex power system optimizations and broadens the practical applications of quantum computing. A microgrid system model is developed, incorporating distributed generation, energy storage, and user loads. An optimization scheduling model is formulated to maximize economic benefits by balancing energy supply and demand through demand response incentives and energy storage management. The problem is reformulated into a Quadratic Unconstrained Binary Optimization (QUBO) model, with the innovative use of a qubitsharing mechanism to optimize the model and reduce qubit requirements. The generation of the QUBO matrix lays the foundation for future solving through the Coherent Ising Machine (CIM), potentially achieving over a hundredfold acceleration in solution speed. © 2024 IEEE.

关键词： Battery storage

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Research on resource scheduling optimization technology for power cloud edge collaboration 6

Research on resource scheduling optimization technology for ...

引用

6th international conference on Intelligent computing, Communication, and Devices, ICCD 2023

作者： Wen, Song Jia, Feng Lijun, Wang Hui, Liu Yu, Wu Linhui, Mao State Grid Electric Power Research Institute Jiangsu Nanjing211106 China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

Multi-station integrated power grid system has a large number of cloud and edge data centers. It needs to solve the problem of collaborative use of cloud, edge and terminal resources, realize the rapid migration of computing tasks in the case of failure, and achieve the consistency of primary and standby resources in the cloud and edge. It needs to solve the problem of streaming processing in high concurrent state. This paper studies the resource scheduling optimization technology adapted to the power cloud edge collaboration, innovatively proposes a multi-data center resource optimization and upgrading method based on the graph data structure, adapts to the multi-center resource optimization and upgrading scenario, uses the RDF resource description framework, TLGM data model to build the multi-data center resource database, uses the global scheduler, the edge scheduler to process the calculation request, uses the data linkage state data model, scheduling rules The probability calculation matrix converts the resource consistency and resource utilization into graph query, and uses the original graph retransmission, subgraph merging technology and efficient load balancing to realize graph query, so as to realize the optimization and upgrading of resources in multiple data centers. This paper innovatively proposes a stream data processing method that is suitable for the cloud edge collaborative multi- data center scenario. It is suitable for the cloud edge collaborative multi-data center stream data processing and analysis scenario. The stream business control and orchestration center construct a serial-parallel collaborative flow analysis process based on the pipeline processing model and parallel processing model. The flow control center control terminal, edge data center, and cloud data center implement flow analysis and scheduling according to the business priority, give full play to the advantages of cloud edge collaborative multi-data center distributed computing

关键词： Resource Description Framework (RDF)

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distributed Energy Resources Based EV Charging Station With Seamless Connection to grid 6

Distributed Energy Resources Based EV Charging Station With ...

引用

IEEE 6th international conference on computing, Communication Control and Automation (ICCUBEA)

作者： Shukl, Pavitra Singh, Bhim Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India

ISBN: (纸本)9781665414739

The charging of electric vehicles (EVs) via common DC bus charging infrastructure based on hybrid renewable energy sources such as solar photovoltaic (PV) and fuel cell is presented here. The requisite to incorporate renewable energy based distributed energy resources (DERs) is attributed to the escalating concern for decarbonisation with improved power quality requirements. Furthermore, the bidirectional flow of power enables the charging/discharging of EVs during the grid presence/absence modes of operation. In addition, the utilization of common DC bus charging mechanism for EVs, facilitates fast charging capability at higher voltage levels. The satisfactory operation during the grid availability/unavailability is attained through the current and voltage based control mechanisms, along with the seamless transition capability via switching (STS-1/0) of the static transfer switches. Furthermore, in compliance with the IEEE standards, the power quality (PQ) improvement is obtained with the utilization of an adaptive comb-filter based current control during the grid-tied mode of operation. The need for improving PQ, stems from the fact that an uninterrupted supply is essential to the critical loads along with an improved power quality. Thus, for validation and corroboration of the system behavior, its performance is authenticated during weak grid conditions, in conjunction with grid connected/islanded modes of operation.

关键词： Adaptive comb-filter distribution network EV charging fuel cell stack grid connected mode islanded mode power quality seamless power transfer solar PV array

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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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distributed Exact Structural Clustering on Large Graph 28

Distributed Exact Structural Clustering on Large Graph

引用

IEEE 28th international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Zhou, Jinyu Rong, Chuitian Liu, Ding Chai, Zhengyi Tiangong Univ Tianjin Peoples R China

ISBN: (纸本)9781665473156

Graph clustering is an important technique to detect community clusters in complex networks. SCAN (Structural Clustering Algorithm for Networks) is a well-studied graph clustering algorithm that has been widely applied over the years. However, the processing time cost of sequential SCAN and its variants cannot be tolerable on large graphs. The existing parallel variants of SCAN are focusing on fully utilizing the computing capacity of multi-core computer architectures and inventing sophisticated optimization techniques on single computing node. As the objects and their relationships in cyberspace are varying over time, the scale of graph data is increasing with high rate. The graph clustering algorithms on single node are facing challenges from limited computing resources, such as computing performance, memory size and storage volume. The distributed processing algorithm is called for processing large graphs. This work presents a distributed structural graph clustering algorithm using Spark. Furthermore, the edge pruning technique and adaptive checking are optimized to improve clustering efficiency. And the label propagation clustering is simplified to reduce the communication cost in the distributed clustering iterations. It also conduct extensive experiments on real-world datasets to testify the efficiency and scalability of the distributed algorithm. Experimental results show that efficient clustering performance can be achieved and it scales well under different settings.

关键词： Structural Clustering distributed computing Spark

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Early Exploration of Using ChatGPT for Log-based Anomaly Detection on parallel File Systems Logs 23

Early Exploration of Using ChatGPT for Log-based Anomaly Det...

引用

32nd international Symposium on High-Performance parallel and distributed computing (HPDC) part of the ACM Federated computing Research conference (FCRC)

作者： Egersdoerfer, Chris Zhang, Di Dai, Dong Univ N Carolina Charlotte NC 27599 USA

ISBN: (纸本)9798400701559

Log-based anomaly detection has been extensively studied to help detect complex runtime anomalies in production systems. However, existing techniques exhibit several common issues. First, they rely heavily on expert-labeled logs to discern anomalous behavior patterns. But labelling enough log data manually to effectively train deep neural networks may take too long. Second, they rely on numeric model prediction based on numeric vector input which causes model decisions to be largely non-interpretable by humans which further rules out targeted error correction. In recent years, we have witnessed groundbreaking advancements in large language models (LLMs) such as ChatGPT. These models have proven their ability to retain context and formulate insightful responses over entire conversations. They also present the ability to conduct few-shot and in-context learning with reasoning ability. In light of these abilities, it is only natural to explore their applicability in understanding log content and conducting anomaly classification among parallel file system logs.

关键词： anomaly detection parallel file system large language model

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An Efficient and Scalable Approach to Build Co-occurrence Matrix for DNN's Embedding Layer 24

An Efficient and Scalable Approach to Build Co-occurrence Ma...

引用

38th ACM international conference on Supercomputing (ACM ICS)

作者： Petit, Quentin Li, Chong Emad, Nahid Univ Paris Saclay Gif Sur Yvette France Huawei Technol France Gif Sur Yvette France Paris Distributed & Parallel Technol Lab Boulogne France Huawei Technol France Boulogne France Maison Simulat Gif Sur Yvette France LI PaRAD Gif Sur Yvette France

ISBN: (纸本)9798400706103

Embedding is a crucial step for deep neural networks. Datasets, from different applications, with different structures, can all be processed through an embedding layer and transformed into a dense matrix. The transformation must minimize both the loss of information and the redundancy of data. Extracting appropriate data features ensures the efficiency of the transformation. The co-occurrence matrix is an excellent way of representing the links between elements in a dataset. However, the dataset size becomes a problem in terms of computation power and memory footprint for using the co-occurrence matrix. In this paper, we propose a parallel and distributed approach to efficiently constructing the co-occurrence matrix in a scalable way. Our solution takes advantage of different features of boolean datasets to minimize the construction time of the co-occurrence matrix. Our experimental results show that our solution outperforms traditional approaches up to 34x. We also demonstrate the efficacy of our approach with a cost model.

关键词： Deep Learning distributed computing Embedding Sparse matrix Co-occurrence matrix Large-scale data analytics

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A new framework for distributed clustering based data aggregation in WSN

A new framework for distributed clustering based data aggreg...

引用

international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Singh, Anuj Kumar Bhushan, Shashi Kumar, Ashish School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science ASET Amity University Punjab Mohali India Department of Computer Science and Engineering AKG Engineering College Ghaziabaad India

ISBN: (纸本)9781032669663

Wireless sensor networks (WSNs) are used in a variety of applications, such as industrial control systems, environmental monitoring, military applications, and structural health monitoring. Energy consumption is a key factor in the lifespan of a WSN, as all sensor nodes are energy-constrained devices. In order to improve energy efficiency, various routing protocols have been developed. These protocols aim to establish paths between sensor nodes and a data sink, while also maximizing the lifespan of the network. However, designing such protocols can be challenging due to the resource and power constraints, as well as the harsh conditions in which sensor nodes often operate. In this paper, a novel approach is proposed for improving energy efficiency in WSNs through the use of a distributed clustering-based data aggregation (DCDA) algorithm. In this approach, we use a grid structure as the basis for clustering, with each cluster having a designated cluster head (CH). Rather than transmitting data directly to the base station (BS), sensor nodes (SNs) send their data to their cluster head, which then aggregates and transmits the data to the BS. This approach can extend the network lifetime by reducing the energy required for communication between SNs. Simulations were conducted to evaluate the performance of the DCDA algorithm in a grid-based WSN. The results showed that using the specified parameters for clustering in the protocol significantly reduces the overall energy consumption of the entire network. © 2024 The Author(s).

关键词： Sensor nodes

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Hardware feature extraction technology for resource aggregation control communication channel and signal source 6

Hardware feature extraction technology for resource aggregat...

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6th international conference on Intelligent computing, Communication, and Devices, ICCD 2023

作者： Nige, Li Zhipeng, Shao Wenting, Wang Ziang, Lu Tengyan, Wang State Grid Smart Grid Research Institute Co. Ltd. Jiangsu Nanjing210003 China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Jiangsu Nanjing210003 China State Grid Shandong Electric Power Research Institute Shandong Jinan250013 China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

In the distributed resource access scenario, the growing number of terminal devices brings pressure and test to the wireless communication network. Unauthenticated terminal access to the network will bring huge security risks. This paper focuses on the hardware features of resource aggregation control communication channel and signal source. For the distributed power system with complex environment, this paper proposes a hardware feature extraction technology for resource aggregation-oriented control communication channel and signal source. On the basis of the research on the theory of generic feature extraction of hardware fingerprint, the specific feature extraction methods of several common communication methods in typical distributed resource aggregation and control scenarios are studied. This paper designs a hardware fingerprint implementation scheme in a typical distributed resource aggregation and control scenario. Experiments show that the proposed fingerprint extraction scheme can effectively distinguish different devices according to different characteristics of devices. It provides a terminal access method with high security and low complexity in the distributed edge aggregation control network of power system. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Feature extraction

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Optimizing Big Data Analytics Architecture for Edge computing Using Container Technology 30

Optimizing Big Data Analytics Architecture for Edge Computin...

引用

30th IEEE international conference on parallel and distributed Systems, ICPADS 2024

作者： Peng, Hong Bai, YunTian Kang, GuanYing Li, Yang Chen, TingWei LiaoNing University LiaoNing ChongShan Middle Road Huanggu District ShenYang China

ISBN: (纸本)9798331515966

This study is dedicated to the integration of big data analytics with edge computing, a critical need driven by the exponential growth of Internet of Things (IoT) technologies and smart device data. We introduce an optimized architecture that integrates Docker containerization with Apache Hadoop YARN, designed specifically for decentralized, resource-constrained edge environments. This innovative framework not only facilitates dynamic resource management but also ensures robust fault tolerance under the heterogeneous and often unstable network conditions typical of edge settings. By integrating Docker and Docker Swarm for container orchestration along with YARN for resource scheduling, our architecture significantly enhances data processing localization. This improvement reduces computational latency and boosts system performance. Through empirical evaluation, we demonstrate marked enhancements in task flexibility, resource efficiency, and operational continuity, particularly adept at managing the diversity and sporadic conditions of edge nodes. Additionally, the deployment of advanced load-balancing tools and multiple management nodes within containers enables the system to autonomously reallocate tasks during node failures or resource-constrained periods. Experimental results show that our architecture achieves an average failover recovery time of less than five seconds and maintains an average CPU utilization below 3% for management nodes, demonstrating significant advantages in handling large-scale IoT data at edge nodes. © 2024 IEEE.

关键词： Resource allocation

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