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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4411-4420 订阅

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Smart grid and Blockchain Technology for Community-Based P2P Renewable Energy Trading

Smart Grid and Blockchain Technology for Community-Based P2P...

引用

Electrical, Energy, and Artificial Intelligence (EICEEAI), international Engineering conference on

作者： Muhammad Imran Sarwar Louai A. Maghrabi Department of Computer Science & IT The Superior University Lahore Lahore Pakistan Department of Software Engineering College of Engineering University of Business & Technology Jeddah Saudi Arabia

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Current electric grids were designed a century ago for simple and limited needs. An electric grid helps the power generation company (PGC) supply electricity to homes and industrial consumers. Traditional electric grids have one-way interactions that make it difficult to cater to ever-changing and increased demand. The Smart grids (SGs) introduced the bidirectional flow of electricity and the exchange of information between consumers and PGCs. SG is a combination of networks of communication, controls, computers, and automations that allow its integration with emerging technologies. Blockchain (BC) is a digital distributed ledger (DDL) that ensures the security, reliability and immutability of data. The integration of BC and SGs is an emerging idea that could also provide a platform for community-based electricity trading. This study aims to review community-based energy trading with BC-based SGs and discusses the first ever community-based peer-to-peer (P2P) solar energy trading project, Quartierstrom, by the Swiss Federal Office of Energy (SFOE), Switzerland. The findings of this study contribute a valuable reference to the knowledge body and enhance the comprehension of P2P energy trading for both new and experienced researchers, enabling them to identify new opportunities and challenges.

关键词： Renewable energy sources Reviews Electricity Solar energy Power markets Smart grids Peer-to-peer computing

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Dynamic Load Balance for Hot-spot and Unbalance Region Problems in HBase 8

Dynamic Load Balance for Hot-spot and Unbalance Region Probl...

引用

8th IEEE international conference on Big Data (Big Data)

作者： Chen, Yibo Xiang, Xing Ling, Xiao Zhang, Xiangchi Wu, Fan Gao, Jianliang State Grid Hunan Elect Power Co Ltd Informat & Commun Branch Changsha Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China

ISBN: (纸本)9781728162515

In distributed storage, dynamic loads usually cause a huge burden such as hot-spot and unblance region problems. In this paper, we focus on the problems of dynamic unbalance loads in distributed database HBase. Firstly, we propose a new intelligent load balance framework to support the collection and analysis of dynamic load information. Then, two efficient load balance strategies are proposed to solve hot-spot problem and unbalance region problem. Finally, we evaluate the strategies on HBase using real-world big data on distributed clusters. Compared to the existing balance strategies, the proposed load balance strategies can make more effective use of the resources and reduce the response time for large dynamic loads.

关键词： distributed storage parallel and distributed computing dynamic loads intelligent load balance

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RITA: Efficient Memory Allocation Scheme for Containerized parallel Systems to Improve Data Processing Latency 13

RITA: Efficient Memory Allocation Scheme for Containerized P...

引用

IEEE 13th international conference on Cloud computing (CLOUD)

作者： Jia, Danlin Bayati, Mahsa Lee, Ron Mi, Ningfang Northeastern Univ Boston MA 02115 USA Samsung Semicond San Jose CA USA

ISBN: (纸本)9781728187808

The emerging resource-sharing container-based virtualization is prevalent in IT, as it is a much lighter deployment in the cloud environment compared to VM-based virtualization. distributed data-processing workloads executing in parallel take advantages of resource sharing, fast delivery, and excellent portability of containerization, but also suffer from resource competition and performance interference. Especially for memory virtualization, data-processing frameworks allocate physical memory (i.e., RAM) and swap to applications specified by users, without considering cache-characteristics and parallelism of applications, which induces performance degradation and significantly protracted latency which is worse given over-provisioning. We design an efficient memory allocation scheme (RITA) for containerized parallel systems to improve data processing latency. RITA monitors memory usage and cache characteristics of applications, and dynamically re-allocates memory resources. We implement RITA in a real-world system, which can easily migrate to other container-based virtualization environments. Our experimental results show that RITA provides remarkable latency improvement for memory intensive distributed data-processing workloads.

关键词： Containerization distributed Data-processing Latency Memory Allocation

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Massively parallel Causal Inference of Whole Brain Dynamics at Single Neuron Resolution 26

Massively Parallel Causal Inference of Whole Brain Dynamics ...

引用

26th IEEE international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Watanakeesuntorn, Wassapon Takahashi, Keichi Ichikawa, Kohei Park, Joseph Sugihara, George Takano, Ryousei Haga, Jason Pao, Gerald M. Nara Inst Sci & Technol Nara Japan US Dept Interior Gainesville FL USA Univ Calif San Diego San Diego CA 92103 USA Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki Japan Salk Inst Biol Studies 10010 N Torrey Pines Rd La Jolla CA 92037 USA

ISBN: (纸本)9781728190747

Empirical Dynamic Modeling (EDM) is a nonlinear time series causal inference framework. The latest implementation of EDM, cppEDM, has only been used for small datasets due to computational cost. With the growth of data collection capabilities, there is a great need to identify causal relationships in large datasets. We present mpEDM, a parallel distributed implementation of EDM optimized for modern GPU-centric supercomputers. We improve the original algorithm to reduce redundant computation and optimize the implementation to fully utilize hardware resources such as GPUs and SIMD units. As a use case, we run mpEDM on AI Bridging Cloud Infrastructure (ABCI) using datasets of an entire animal brain sampled at single neuron resolution to identify dynamical causation patterns across the brain. mpEDM is 1,530x faster than cppEDM and a dataset containing 101,729 neuron was analyzed in 199 seconds on 512 nodes. This is the largest EDM causal inference achieved to date.

关键词： Empirical Dynamic Modeling Causal Inference parallel distributed computing GPU High-Performance computing Neuroscience

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Efficient Context-Aware Edge computing Algorithm in Mobile Cloud

Efficient Context-Aware Edge Computing Algorithm in Mobile C...

引用

Computer Science and Management Technology (ICCSMT), international conference on

作者： Jian Chang Muchuan Yang Yan Gong Jiayu Liu Yuan Tian Qi Li State Grid Sichuan Information & Communicatioon Company Chengdu China

Edge computing provides a feasible technical solution to solve the problem of resource and energy supply constraints in terminal device deep learning applications. By migrating part of the deep learning application computing from the terminal to the cloud edge server, the computing pressure of the terminal device can be greatly relieved, and the energy consumption of the terminal can also be reduced to a certain extent. However, in the process of computing migration, the context switching loss of deep learning applications results in a long delay. This paper studies the problem of minimizing the completion time of deep learning applications under edge computing. We model the deep learning application as a task graph, and consider the constraints such as task switching delay, energy consumption and task dependency. Without changing the structure of the task graph, we transform it into a task scheduling-based application completion time minimization problem. In this paper, we first design a parallel scheduling algorithm based on greedy search. Considering the loss of context switching and the dependence of task data, we can allocate tasks to multiple processors for parallel processing by calculating the expected completion time of tasks, thus reducing the completion time of applications. Based on the experimental results, the greedy scheduling algorithm and comparison algorithm are analyzed and verified to optimize the completion time of different depth learning applications.

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Improved PSO Resource Allocation Method in Cloud Environment 12

Improved PSO Resource Allocation Method in Cloud Environment

引用

12th international conference on Signal Processing Systems

作者： Zhao, Shengye Wu, Wenjiang Chinese Acad Sci Shenyang Inst Comp Technol Shenyang 110168 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Shenyang High Precis CNC Intelligent Technol Co L Shenyang 110168 Peoples R China

ISBN: (纸本)9781510642768;9781510642751

In cloud computing, the resource allocation of cloud platform faces not only single node resource request, but also complex multi-node resource request. Especially for users who need to run parallel or distributed tasks, there are very strict delay and bandwidth requirements for communication between nodes in cloud cluster. Existing cloud platforms often allocate resources one by one virtual machine, ignoring or making it difficult to guarantee the link resources between nodes, that is to say, there is a problem of multi resource allocation in cloud clusters. Therefore, this paper proposes a new cloud resource description method, and improves the allocation method of particle swarm cloud resource. The simulation results show that the proposed method can effectively allocate cloud resources, improve the average revenue and resource utilization of cloud resources, reduce the resource cost by at least 10% compared with the traditional methods, and have shorter task execution time (within 30ms).

关键词： Cloud computing Cloud Resources Resource Allocation Particle Swarm

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Graph Structure Learning via Transfer Entropy for Multivariate Time Series Anomaly Detection

Graph Structure Learning via Transfer Entropy for Multivaria...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mingyu Liu Yijie Wang Xiaohui Zhou Yongjun Wang National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multivariate time series anomaly detection (MTAD) poses a challenge due to temporal and feature dependencies. The critical aspects of enhancing the detection performance lie in accurately capturing the dependencies between variables within the sliding window and effectively leveraging them. Existing studies rely on domain knowledge to pre-set the window size, and overlook the strength of dependencies while calculating direction based on variable similarity. This paper proposes GSLTE, a graph structure learning method for MTAD. GSLTE employs Fast Fourier Transform to conduct iterative segmentation of the whole series, selecting the dominant Fourier frequency as the window size for each subsequence within the minimum interval. GSLTE quantifies the direction and strength of the dependencies based on variable-lag transfer entropy which is achieved through Dynamic Time Warping method to learn asymmetric links between variables. Extensive experiments show that GNN-based MTAD methods applying GSLTE can further improve anomaly detection performance while outperforming state-of-the-art competitors.

关键词： Learning systems Time-frequency analysis Fast Fourier transforms Time series analysis Signal processing Feature extraction Entropy Iterative methods Speech processing Anomaly detection

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Deep Time Series Anomaly Detection with Local Temporal Pattern Learning

Deep Time Series Anomaly Detection with Local Temporal Patte...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yizhou Li Yijie Wang Hongzuo Xu Xiaohui Zhou National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China Intelligent Game and Decision Lab (IGDL) Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Self-supervised time series anomaly detection (TSAD) demonstrates remarkable performance improvement by extracting high-level data semantics through proxy tasks. Nonetheless, most existing self-supervised TSAD techniques rely on manual- or neural-based transformations when designing proxy tasks, overlooking the intrinsic temporal patterns of time series. This paper proposes a local temporal pattern learning-based time series anomaly detection (LTPAD). LTPAD first generates sub-sequences. Pairwise sub-sequences naturally manifest proximity relationships along the time axis, and such correlations can be used to construct supervision and train neural networks to facilitate the learning of temporal patterns. Time intervals between two sub-sequences serve as labels for sub-sequence pairs. By classifying these labeled data pairs, our model captures the local temporal patterns of time series, thereby modeling the temporal pattern-aware "normality". Abnormal scores of testing data are acquired by evaluating their conformity to these learned patterns shared in training data. Extensive experiments show that LTPAD significantly outperforms state-of-the-art competitors.

关键词： Time series analysis Semantics Neural networks Training data Manuals Signal processing Data models Speech processing Anomaly detection Testing

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Message from conference Chairs

Message from Conference Chairs

引用

ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed computing (SNPD)

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

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PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain

PPSS: A Privacy-Preserving Supervision Scheme Based on Block...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Ming Chen Peichang Shi Xiang Fu Feng Jiang Fei Gao Penghui Ma Jinzhu Kong National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Kylin Software Co.LTD Changsha China Zhejiang Lab Hangzhou China

In JointCloud computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud computing, and combines the “double encryptions” and “threshold encryption” technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.

关键词： Privacy Uncertainty Decision making Buildings Reliability engineering Blockchains Complexity theory

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