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检索条件"任意字段=IEEE International Conference on e-Science and Grid Computing"

共 17242 条记录，以下是11-20 订阅

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Research Progress on computing Power Network for the Power Industry 12

Research Progress on Computing Power Network for the Power I...

引用

12th ieee international conference on Information and Communication Networks, ICICN 2024

作者： Chen, Yanqi Jiang, Congfeng Yan, Longchuan Feng, Zhipeng Guo, Yonghe Niu, Jianing Wu, Chunpeng Wang, Yue Wu, Xingping School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China State Grid Information & State Grid State Grid Information & Telecommunication Branch Beijing China State Grid Smart Grid Research Institute CO . LTD. Beijing China

ISBN: (纸本)9798350355802

With the development of 5G and artificial intelligence technologies, the computing power network aims to address the uneven distribution of computing centers in the power industry and the technical barriers on the supply side, promoting computing power as a social-level service. This article explores key technologies such as data center interconnection architecture, network and resource scheduling with quality of service awareness, collaborative optimization of computing networks, and energy efficiency optimization in computing centers. It emphasizes the importance of establishing computing resource standards and data interface standards. Additionally, the article delves into the the application of artificial intelligence technology in the power industry including power grid load forecasting, power dispatching, and monitoring of transmission. Finally, the article provides an outlook on the future development of the computing power network, highlighting its potential and challenges in the industry's digital transformation. © 2024 ieee.

关键词： Power distribution networks

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An empirical Investigation of Container Building Strategies and Warm Times to Reduce Cold Starts in Scientific computing Serverless Functions 20

An Empirical Investigation of Container Building Strategies ...

引用

20th ieee international conference on e-science (e-science)

作者： Bauer, Andre Gonthier, Maxime Pan, Haochen Chard, Ryan Grzenda, Daniel Straesser, Martin Pauloski, J. Gregory Kamatar, Alok Baughman, Matt Hudson, Nathaniel Foster, Ian Chard, Kyle Illinois Inst Technol Chicago IL 60616 USA Univ Chicago Chicago IL 60637 USA Argonne Natl Lab Lemont IL USA Univ Wurzburg Wurzburg Germany

ISBN: (纸本)9798350365627;9798350365610

Serverless computing has revolutionized application development and deployment by abstracting infrastructure management, allowing developers to focus on writing code. To do so, serverless platforms dynamically create execution environments, often using containers. The cost to create and deploy these environments is known as "cold start" latency, and this cost can be particularly detrimental to scientific computing workloads characterized by sporadic and dynamic demands. We investigate methods to mitigate cold start issues in scientific computing applications by pre-installing Python packages in container images. Using data from Globus Compute and Binder, we empirically analyze cold start behavior and evaluate four strategies for building containers, including fully pre-built environments and dynamic, on-demand installations. Our results show that pre-installing all packages reduces initial cold start time but requires significant storage. Conversely, dynamic installation offers lower storage requirements but incurs repetitive delays. Additionally, we implemented a simulator and assessed the impact of different warm times, finding that moderate warm times significantly reduce cold starts without the excessive overhead of maintaining always-hot states.

关键词： Scientific computing Serverless Cold Start Measurements Simulation

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A Generic Performance Metric for Scientific Data Compression 20

A Generic Performance Metric for Scientific Data Compression

引用

20th ieee international conference on e-science (e-science)

作者： Qin, Zhenlu Gu, Jessie Liu, Maggie Wang, Jinzhen Zhu, Hongjian AUM Montgomery AL 36117 USA Univ Maryland College Pk MD 20742 USA Tenafly High Sch Tenafly NJ USA Univ N Carolina Charlotte NC USA X Byte Res Tenafly NJ USA

ISBN: (纸本)9798350365627;9798350365610

The performance outcomes of scientific data compression are complex in nature, often with one performance metric being more prioritized/optimized over others in the algorithm. To balance between different performance metrics, this paper formulates a new generic metric and uses it for searching an optimal configuration that leads to a balanced compression performance.

关键词： data compression high-performance computing performance

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CWGAN-GP: Fuzzing Testcase Generation Method based on Conditional Generative Adversarial Network 22

CWGAN-GP: Fuzzing Testcase Generation Method based on Condit...

引用

ieee 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSe conference / CSe conference / eUC conference / ISCI conference

作者： Qin, Zhongyuan Fan, Jiarong Li, Zeru Liu, Xujian Sun, Xin Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China State Grid Zhejiang Elect Power Co Ltd Res Inst Internet Technol Ctr Hangzhou Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Fuzzing is widely used in vulnerability mining because of its simplicity and efficiency. The fuzzing tool generates numerous testcases according to the mutation of the initial seed and inputs them into the program to be tested. At the same time, it monitors exceptions of the running program to find possible software bugs. In order to improve the performance of fuzzing, many researchers are committed to generating various initial testcases. However, at present, fuzzing testcase generation does not make full use of the information of testcases, the generation process is uncontrollable, and the generation effect is general. In view of the problems mentioned above, this paper proposes a testcase generation method based on conditional generative adversarial networks. The CWGAN-GP (Conditional Wasserstein Generic Adversarial Network-Gradient Penalty) learns the format features of testcases, and generates testcases covering corresponding branches according to the input branch information. This paper conducted experiments on 6 common programs, and the experiments showed that compared to the initial training set, testcases generated by (C)WGAN-GP can extend the exploration of the program, and thus find more vulnerabilities. The CWGAN-GP model uses the branch vector in the training set as the condition to generate testcases, which can find more crashes and hangs, and improve the final fuzzing performance.

关键词： fuzzing branch coverage testcase generation one dimensional convolutions CWGAN-GP

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A Fast Belief Propagation-Based Distributed Gauss-Newton Method for Power System State estimation 8

A Fast Belief Propagation-Based Distributed Gauss-Newton Met...

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8th international conference on Cloud computing and Big Data Analytics (ICCCBDA)

作者： Guo, Peng Shi, Danni Wang, Xuan Shi, Xinghua State Grid Smart Grid Res Inst Co Ltd State Grid Lab Grid Adv Comp & Applicat Beijing 102209 Peoples R China State Grid Smart Grid Res Inst Co Ltd Zhejiang 310007 Peoples R China

ISBN: (纸本)9781665455336

State estimation is the foundation for a variety of online power system applications in energy management systems, and the stability of power systems is directly impacted by the speed with which current system states can be obtained through state estimation. This paper proposed a fast Gaussian-Newton state estimation method for power systems based on parallel belief propagation, which implements the Gaussian belief process via multi-core and multi-thread parallel computation to achieve efficient state estimation. Simulation findings on numerous ieee-standard power systems show that the suggested technique outperforms the traditional algorithm.

关键词： Gauss-Newton state estimation factor graphs belief propagation parallel computing

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Loss Sensitivity Calculation and Analysis Based on Graph computing 9

Loss Sensitivity Calculation and Analysis Based on Graph Com...

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9th international conference on Power and Renewable energy

作者： Wu, Yuezhou Yang, Tianlin Lu, Jiangang Guo, Wenxin Zhao, Ruifeng Tan, Huijuan Guangdong Power Grid Co Ltd Power Dispatching Control Ctr Kaiping City Guangdong Peoples R China

ISBN: (纸本)9798350377477;9798350377460

Loss sensitivity in electricity systems refers to the responsiveness of power losses in electrical transmission and distribution networks to various influencing factors. This concept is critical for optimizing the efficiency and reliability of power delivery from generation sources to end consumers. especially in the electricity market trading process, network losses, although only a small part of the total cost, have a significant impact on each specific transaction and grid user. Therefore, in-depth understanding and analysis of the impact of generation transactions on the grid network loss can provide a basis for the power purchase plan for the grid to provide reference for the grid network loss management, which is of great significance. The current mainstream calculation and analysis method for network loss sensitivity is the marginal loss coefficient method, which determines the network loss share of each bus by analyzing the impact of bus injected power on the total network loss changes, but the nature of its serial calculation cannot properly solve the balance between real-time analysis of network loss optimization and accuracy, and with the increase in system size, this problem has gradually become a major obstacle to the realization of real-time analysis. This paper presents a novel parallel computing method based on graph computing for determining the sensitivity of system network loss and node-injected power. Graph computing is a parallel computing method that stores, computes, and analyzes the information in a system by constructing a topological graph model and based on it. The method alleviates the computational burden associated with calculating network loss sensitivity as the scale of the power system expands by leveraging the parallel computing capabilities of graph databases. Furthermore, it facilitates the graphical calculation and analysis of network loss sensitivity through the utilization of graph storage.

关键词： Loss Sensitivity Graph computing Generators Loss Sensitivity Loads Loss Sensitivity Flexible Load

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SFeTeC: Split-Federated Learning Scheme Optimized for Thing-edge-Cloud environment 20

SFETEC: Split-FEderated Learning Scheme Optimized for Thing-...

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20th ieee international conference on e-science (e-science)

作者： Van An Le Haga, Jason Tanimura, Yusuke Truong Thao Nguyen Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9798350365627;9798350365610

This paper introduces SFeTeC, an innovative federated learning framework addressing the limitations of traditional methods like FedAvg. SFeTeC splits training into base models and core models, reducing communication overhead, mitigating non-IID data issues, and enhancing training speed. Base models are trained at client devices, while core models are trained at edge servers and aggregated at the cloud. Preliminary results show that SFeTeC significantly reduces communication overhead and training duration compared to the state-of-the-art baselines while enhancing privacy.

关键词： Federated Learning IoT edge computing

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Fast grid emissions Sensitivities using Parallel Decentralized Implicit Differentiation

Fast Grid Emissions Sensitivities using Parallel Decentraliz...

引用

2024 international conference on Communications Control and computing Technologies for Smart grids

作者： Degleris, Anthony Valenzuela, Lucas Fuentes Rajagopal, Ram Pavone, Marco el Gamal, Abbas Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Dept Civil & Environm Engn Stanford CA USA Stanford Univ Dept Aeronaut & Astronaut Stanford CA USA

ISBN: (纸本)9798350318562;9798350318555

Marginal emissions rates-the sensitivity of carbon emissions to electricity demand-are important for evaluating the impact of emissions mitigation measures. Like locational marginal prices, locational marginal emissions rates (LMes) can vary geographically, even between nearby locations, and may be coupled across time periods because of, for example, storage and ramping constraints. This temporal coupling makes computing LMes computationally expensive for large electricity networks with high storage and renewable penetrations. Recent work demonstrates that decentralized algorithms can mitigate this problem by decoupling timesteps during differentiation. Unfortunately, we show these potential speedups are negated by the sparse structure inherent in power systems problems. We address these limitations by introducing a parallel, reversemode decentralized differentiation scheme that never explicitly instantiates the solution map Jacobian. We show both theoretically and empirically that parallelization is necessary to achieve non-trivial speedups when computing grid emissions sensitivities. Numerical results on a 500 node system indicate that our method can achieve greater than 10x speedups over centralized and serial decentralized approaches.

关键词： Sensitivity analysis distributed optimization implicit differentiation locational marginal emissions

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economy-based Greedy Bidding for Resources for CAe Workflows in Hybrid Cloud Infrastructure 20

Economy-based Greedy Bidding for Resources for CAE Workflows...

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20th ieee international conference on e-science (e-science)

作者： Dasgupta, Srishti Uustalu, Tahvend Gerndt, Michael Gholami, Babak Tech Univ Munich Chair Comp Architecture & Parallel Syst Garching Germany BMW Grp Munich Germany

ISBN: (纸本)9798350365627;9798350365610

The advent of generative design in the automotive sector, characterised by the automatic and iterative exploration of expansive solution spaces to discover optimal design configurations, has significantly increased the demand for computational resources to run intensive computer-aided engineering (CAe) simulations within constrained time frames. The inherent limitations of static high-performance computing (HPC) clusters have necessitated the adoption of cloud resources due to their flexible and elastic nature, thereby enhancing the capacity to accommodate the computational demands of these iterative workflows. These workflows, represented as Directed Acyclic Graphs (DAGs), involve the serial and parallel execution of tasks, which can dynamically share resources with other workflows during idle periods. In this paper, we propose an economy-based approach to exploit the gaps generated by these idle periods through a bidding system, thereby enabling more efficient resource utilisation and reducing the average wait time, makespan, cost and deadline miss by more than 40%, 6%, 13% and 45%respectively against certain infrastructures and baselines. Furthermore, we explore the potential for generating revenue by renting out idle resources in a hybrid cloud setup. This approach not only aims to optimise the use of computational resources but also seeks to provide cost-effective solutions to meet the escalating demands of generative design in the automotive sector.

关键词： Cloud computing High Performance computing Hybrid Infrastructures CAe Workflows

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A Comprehensive Design and Analysis of the Split Inductor Buck-Boost Converter for Automotive Applications 10

A Comprehensive Design and Analysis of the Split Inductor Bu...

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10th ieee international conference on electronics, computing and Communication Technologies (ieee CONeCCT)

作者： Asish, Jaldu Megha, T. S. Laqueta, N. Seethur, Rashmi PES Univ Dept Elect & Commun Engn Bangalore Karnataka India

ISBN: (纸本)9798350385939;9798350385922

Renewable energy is growing more popular as a result of its low carbon footprint and the need to reduce greenhouse gas emissions. This new energy vision has fueled advances in engineering science, particularly in grid integration, energy storage systems, power converter topologies, and enhanced control approaches. This Research Topic on Advancements in Power electronic Converters and Renewable energy Integration aims to focus on recent advances and new trends in the field of renewable energy integration, energy storage, control techniques, stability, and reliability, including power converter topologies, and renewable energy management systems. This article proposes such Novel Power Converter topology specifically a Buck Boost Converter which aims to achieve better power conversion range and also with comparatively lesser diode and switch voltage stress. State Space Analysis has been carried out to design the plant transfer function. A voltage mode controller has been designed with the help of Ziegler-Nichols method for PID tuning. A simulation test model with specifications of 24V to 48V and 12V with an output power of 45W operating at a frequency of 100kHz has been built and simulated and experimental results are tabulated.

关键词： Buck Boost Converter energy storage systems enhanced control approaches grid integration,HeV Applications Power converter topologies Power electronic Converters and Renewable energy Integration Reliability Semiconductors Stability Ziegler-Nichols method for PID tuning

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