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检索条件"任意字段=Proceedings of the First International Conference on Parallel and Distributed Information Systems"

共 6615 条记录，以下是141-150 订阅

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A Improved Consistent Hash Algorithm in Heterogeneous Edge Computing 29

A Improved Consistent Hash Algorithm in Heterogeneous Edge C...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Chai, Ruonan Gao, Shuai Hu, Zhenghao Beijing Jiaotong University School of Electronic Information Engineering Beijing China

ISBN: (纸本)9798350330717

Load balancing plays a critical role in large-scale heterogeneous edge computing, aiming to enhance the accuracy of computing resource matching and reduce processing delays caused by imbalanced resource allocation. In highly heterogeneous distributed computing environments, scalability and accuracy in matching computing resources are essential. The Consistent Hashing (CH) algorithm is well-known for its simplicity and strong scalability, enabling extensive and flexible scheduling strategies. It achieves load balancing by evenly distributing requests among different nodes. However, the CH algorithm primarily focuses on load balancing in storage resource scenarios and often neglects load balancing for computing resources, potentially leading to reduced accuracy in matching computing resources and prolonged processing delays. This paper introduces an improved CH algorithm called chordDC, specifically designed for distributed computing scenarios. It takes into account the unified representation of user tasks and computing resources, incorporating additional discriminative criteria in the selection of computing nodes to achieve a more precise resource allocation. Through a comparative analysis with two baseline algorithms, the proposed algorithm demonstrates effective performance improvements, enhancing the accuracy of computing resource matching by 40%-50% and significantly reducing computing processing latency. © 2023 IEEE.

关键词： Heterogeneous edge computing Large scale Load balancing Matching accuracy of computing resources

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Data Quality Evaluation Method for Power Grid Service Transformation Based on Isolation Boundary 4

Data Quality Evaluation Method for Power Grid Service Transf...

引用

4th international conference on information Science, parallel and distributed systems, ISPDS 2023

作者： Mao, Zhengxiong Chuan, Tao Zhou, Jing Su, Wenwei He, Yingjun Mei, Donghui Li, Chenglin Information Center of Yunnan Power Grid Co. Ltd Kunming650011 China

ISBN: (纸本)9798350337181

In order to improve the security and evaluation accuracy of power grid business transformation data, this paper proposes a data quality evaluation method for power grid business transformation based on isolation boundary. first, improve the security of grid business transformation data through the isolation boundary between the internal and external networks of data transmission. Secondly, within the security range of the isolation boundary, the data of power grid business transformation is collected through the compression sensing theory. Finally, accuracy, integrity, consistency, timeliness and redundancy are selected as the evaluation indicators of data quality, and the weight of indicators is analyzed to determine the evaluation model. According to the comprehensive evaluation score, the evaluation of data quality of power grid business transformation is completed. The experimental results show that, compared with the traditional methods, this method can safely and completely collect the data of power grid business transformation, and can improve the accuracy of data quality evaluation. © 2023 IEEE.

关键词： Electric power transmission

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Learning Mean-Field Control for Delayed information Load Balancing in Large Queuing systems 51

Learning Mean-Field Control for Delayed Information Load Bal...

引用

51st international conference on parallel Processing (ICPP)

作者： Tahir, Anam Cui, Kai Koeppl, Heinz Tech Univ Darmstadt Dept Elect Engn & Informat Technol Darmstadt Germany

ISBN: (纸本)9781450397339

Recent years have seen a great increase in the capacity and parallel processing power of data centers and cloud services. To fully utilize the said distributed systems, optimal load balancing for parallel queuing architectures must be realized. Existing state-of-the-art solutions fail to consider the effect of communication delays on the behaviour of very large systems with many clients. In this work, we consider a multi-agent load balancing system, with delayed information, consisting of many clients (load balancers) and many parallel queues. In order to obtain a tractable solution, we model this system as a mean-field control problem with enlarged state-action space in discrete time through exact discretization. Subsequently, we apply policy gradient reinforcement learning algorithms to find an optimal load balancing solution. Here, the discrete-time system model incorporates a synchronization delay under which the queue state information is synchronously broadcasted and updated at all clients. We then provide theoretical performance guarantees for our methodology in large systems. Finally, using experiments, we prove that our approach is not only scalable but also shows good performance when compared to the state-of-the-art power-of-d variant of the Join-the-Shortest-Queue ( JSQ) and other policies in the presence of synchronization delays.

关键词： load balancing parallel systems mean-field control reinforcement learning

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Finite-Time Multi-Parameter Smoothing distributed Algorithm for Delayed Multi-Agent systems Subject to Switching Topology

Finite-Time Multi-Parameter Smoothing Distributed Algorithm ...

引用

2024 IEEE international conference on systems, Man, and Cybernetics, SMC 2024

作者： Leng, Jiahao Zhong, Qishui Han, Sheng Li, Guoyi Zhang, Gangming School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Electrical Engineering Southwest Minzu University Chengdu610041 China Glasgow College University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9781665410205

This brief tackles the finite-time consensus and distributed optimization issues in multi-agent systems (MASs) affected by time-varying delays (TDs). It centers on scenarios where the agents' communication network topology is characterized by a switching undirected graph. distributed time-varying optimization involves a collective effort by multiple agents to collaboratively minimize the aggregate of local objective functions that change over time. This process is subject to time-dependent equality constraints and relies solely on information available at the local level and from neighboring agents. first, a finite-time multi-parameter smooth distributed algorithm with input TDs is proposed to solve the optimization problem of MASs with arbitrary switching topologies. Secondly, combined with the Lyapunov stability theory, the states of each agent can achieve consensus and asymptotically track the optimal solution trajectory within a finite-time is proved. At last, an illustrative simulation example of target tracking with MASs is given to verify the effectiveness and applicability of the theoretical results. © 2024 IEEE.

关键词： Multi agent systems

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A Global Task Scheduling Method Based on Network Measurement and Prediction in Computing Power Networks 29

A Global Task Scheduling Method Based on Network Measurement...

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Zhang, Chuanfu Li, Wen Chen, Jing Ge, Jing Wang, Di Li, Na Sun, Hao Geng, Yudong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

To improve the task scheduling efficiency in computing power networks, this paper proposes a global task scheduling method based on network measurement and prediction in computing power networks (GTS-MP), which selects a more suitable computing power cluster and storage platform with a higher network availability score between them to implement tasks. The experiments are conducted on a test bench of urban area networks in 12 cities. The results show that this method greatly reduces the execution time of artificial intelligence model training tasks, with a maximum reduction of 40% compared to other plans. © 2023 IEEE.

关键词： computing power network global scheduling network availability score task offloading

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Demystifying Decentralized Matrix Communication Network: Ecosystem and Security 29

Demystifying Decentralized Matrix Communication Network: Eco...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Li, Hao Wu, Yanbo Huang, Ronghong Mi, Xianghang Hu, Chengyu Guo, Shanqing Shandong University China Chinese Academy of Sciences Institute of Information Engineering China School of Cyber Security University of Chinese Academy of Sciences China University of Science and Technology of China China Quancheng Laboratory China

ISBN: (纸本)9798350330717

With the emergence of Web3, decentralized network protocol technologies have been vigorously developed. As a pioneer for decentralized real-time communication systems, Matrix is an open standard based on a federation specification protocol. Anyone can set up a self-hosted homeserver to participate in the global Matrix network and communicate with others in chat rooms. In this paper, we conduct the first in-depth measurement and exploratory research on Matrix's ecosystem and security. We designed and implemented several investigation techniques to empirically delve into Matrix federation from various aspects (homeservers, rooms, and users). In the end, we identified a number of interesting findings and potential vulnerabilities, including anti-decentralization phenomena, cybersecurity threats in homeservers, and the confidentiality of encrypted rooms being compromised. © 2023 IEEE.

关键词： decentralization ecosystem Matrix security

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Edge Computing Scheduling Method for Related Tasks of Industrial Internet of Things 29

Edge Computing Scheduling Method for Related Tasks of Indust...

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Tan, Aiping Wang, Chang Dong, Chenglong Wang, Yan Liaoning University College of Information China

ISBN: (纸本)9798350330717

The Industrial Internet of Things (IIoT) generally uses cloud computing mode to process tasks. However, in cases of excessive task volume, a high workload will lead to significant processing delays. Edge computing, due to its ability to achieve load balancing and reduce processing latency, is an effective solution. Existing research mainly focuses on how to schedule multiple tasks and multiple edge nodes effectively but lacks consideration of the correlation between tasks. In view of the shortcomings of current work, this paper defines an edge computing scheduling problem for related tasks in IIoT scenario and proves that this problem is NP-Hard. A related task scheduling method is proposed based on a Directed Optimization Genetic Algorithm(DO-GA). Finally, an experimental environment is set up to perform a performance comparison analysis of the proposed algorithm. Experimental results show that, in terms of convergence, scheduling success rate and stability, the method proposed in this paper outperforms traditional Genetic Algorithm(GA) and Particle Swarm Optimization(PSO). © 2023 IEEE.

关键词： Edge Computing IIoT Task Scheduling

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Execution Time Optimization for Pipeline Deep Network Training on Multiple GPUs 33

Execution Time Optimization for Pipeline Deep Network Traini...

引用

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

作者： Wu, Bing-Jou Hong, Ding-Yong Liu, Pangfeng Wu, Jan-Jan National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (纸本)9798331524937

As neural network models become gigantic, they increasingly demand more time and memory for training. To meet these demands, advanced parallel computing techniques have become essential. Our research focuses on hybrid parallelism, an extension of pipeline parallelism. Pipeline parallelism splits the neural network into sub-networks distributed across a sequence of processing units, enabling simultaneous processing of different data segments on each device. Hybrid parallelism extends this concept by allocating multiple devices to each sub-network. Our research focuses on optimizing hybrid parallelism by improving how the model is partitioned and how computational devices are assigned. We address these issues by modeling the neural network as a directed acyclic graph of tensor operators, and then demonstrating that optimally partitioning this graph is NP-complete. Then, we propose a two-step approach. The first step is to determine a sequence of nodes. The second step is dynamic programming, which partitions the sequence to maintain balance across the assigned devices. In transforming the graph into a sequence, we explore two methods: one employs topological sorting, while the other clusters non-sequential subgraphs. We apply both methods and select the more effective one based on performance outcomes. We implement our algorithm and conduct experiments. The results show substantial enhancements in both the speed of partitioning and training throughput, with speedups reaching up to 23 in partitioning time and a 1.3 -fold increase in training throughput. © 2025 IEEE.

关键词： Deep neural networks

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distributed Model Predictive Control for Multi-area LFC Considering the Terminal Costs 24

Distributed Model Predictive Control for Multi-area LFC Cons...

引用

2nd international conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Yu, Shuaikang Chen, Chaoyang Li, Zhengwu Zhong, Bin Tan, Bo School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China Hunan Vanguard Group Co. Ltd Changsha China

ISBN: (纸本)9798400710681

With the increasing size of the power grid, maintaining the frequency stability of the power grid is becoming more and more challenge, Model Predictive Control is one of the promising solutions to this problem. In this paper, we propose a distributed Model Predictive Control (DMPC) method for the load-frequency control (LFC) of an interconnected multi-area grid, which incorporates the terminal states into the cost function and considers the non-zero steady-state error, thereby improving the convergence speed and effectiveness. first, the traditional multi-area LFC model is adapted and simplified into a new model suitable for the proposed DMPC method and discretized. Then, the design of the new cost function is given with a detailed derivation and a system stability analysis is performed. Finally, simulations are performed in Simulink to verify the effectiveness of the proposed method. © 2024 Copyright held by the owner/author(s).

关键词： Predictive control systems

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Enhancing Path information with Reinforcement Learning for Few-shot Knowledge Graph Completion 29

Enhancing Path Information with Reinforcement Learning for F...

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Ma, Ruixin Yu, Mengfei Gao, Buyun Guo, Jiaqi Chen, Zhikui Zhao, Liang Dalian University of Technology School of Software Technology Dalian China

ISBN: (纸本)9798350330717

The emergence of big data has made knowledge graphs (KGs) an effective means of representing structured knowledge, and few-shot knowledge graph completion (FKGC) has recently received increasing attention, which attempts to forecast missing information for relations with few-shot related facts. In this regard, several deep learning-based and embedding-based methods have been proposed for FKGC. However, most existing methods overlook multi-hop path information and only utilize the immediate neighbors of relevant entities when encoding and matching entity pairs, potentially limiting their performance. In this paper, we propose an Enhancing Path information with Reinforcement Learning (EPIRL) approach for FKGC. Specifically, we introduce a reinforcement learning framework to construct a reasoning subgraph, aiming to thoroughly uncover the inferential path rules between support and query triples. Then, we utilize an interaction focused matching model to capture the inherent connections among these reasoning paths. To further improve performance, we incorporate a relational attention mechanism aimed at emphasizing the influence of pivotal paths. Extensive experiments demonstrate that our model outperforms several state-of-the-art methods on the frequently-used benchmark datasets FB15k237-One and NELL-One. © 2023 IEEE.

关键词： Few-shot learning Knowledge graph completion Reinforcement learning

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