检索结果-内蒙古大学图书馆

25th IEEE/ACM International Symposium on distributed Simulation and real time Applications (DS-RT)

作者： Cicirelli, Franco Nigro, Libero CNR Inst High Performance Comp & Networking ICAR Natl Res Council Italy I-87036 Arcavacata Di Rende CS Italy Univ Calabria DIMES Dept Informat Modelling Elect & Syst Sci I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9781665433266

This work aims at the development of tools for supporting modelling and analysis of timed systems by Stochastic Reward Nets (SRN). In a first approach it was proposed and experimented a formal reduction of SRN over timed Automata (TA) in the context of the Uppaal popular toolbox. The reduction has the merit to allow both exhaustive model checking of an SRN model, useful for the assessment of qualitative properties (e.g., absence of deadlocks, occurrence of particular event sequences etc.), and quantitative analysis through the statistical model checker, which is based on simulations. However, although Uppaal enabled formal reasoning on the semantics of SRN, its practical usage suffers of scalability problems, that is it can introduce severe limitations in time and space when studying complex models. To cope with this problem, this paper describes a Java implementation of the SRN operational core engine, using the lock-free and efficient Theatre actor system which permits the parallel simulation of large models. The realization can be used for functional property checking on an untimed version of a source SRN model, and quantitative estimation of measurables through simulations. The paper discusses the design and implementation of the core engine of SRN on top of Theatre, together with supported intuitive configuration process of an SRN model, and reports some experimental results using a scalable grid computing model. The experiments confirm Theatre/SRN are capable of exploiting the potential of modern multi-core machines and can deliver good execution performances on large models.

关键词： Stochastic Reward Nets performability analysis actors high-performance computing Theatre Java

来源：评论

学校读者我要写书评

暂无评论

Advanced Analytics as a Service in Smart Factories 20

Advanced Analytics as a Service in Smart Factories

引用

20th IEEE Jubilee World Symposium on Applied Machine Intelligence and Informatics, SAMI 2022

作者： Soderi, Mirco Kamath, Vignesh Morgan, Jeff Breslin, John G. National University of Ireland Data Science Institute Galway Ireland

ISBN: (纸本)9781665497046

A key aspect of Intelligent Manufacturing is the interface between the Edge and Fog layers on one side and the Cloud on the other side. Once that some data extraction and cleaning has been performed in the (logical and/or physical) nearby of the production line, it is necessary to move to the Cloud where distributed data and parallel computations make it possible to train and use Machine Learning models. For a wide range of applications, it is also necessary to restructure and reconfigure the computation network over the time, reacting to relevant events at real-time. This can be achieved (i) by making the network and all of its components fully configurable through API calls, (ii) by using containerization technologies, and (iii) by relying on graphical user interfaces for development, data visualization, monitoring, and interaction. In this work, an architecture for a computation network is presented that (i) spreads across the Edge, Fog and Cloud layers, (ii) is fully configurable through API calls, and (iii) is based on Docker, Node-RED, MQTT, Scala, Spark, and Cloud storage technologies such as Hadoop. Two proofs of concept are presented: a clustering-based alerter, and a demo environment plus a Postman collection including over 600 API calls to demonstrate how the proposed architecture enables Big Data stream transformations and analytics as a Service. © 2022 IEEE.

关键词： Cloud computing distributed databases Computer architecture Standardization Big Data real-time systems Sparks

来源：评论

学校读者我要写书评

暂无评论

Nocal-Siam: Refining Visual Features and Response With Advanced Non-Local Blocks for real-time Siamese Tracking

引用

IEEE TRANSACTIONS ON IMAGE PROCESSING 2021年 30卷 2656-2668页

作者： Tan, Huibin Zhang, Xiang Zhang, Zhipeng Lan, Long Zhang, Wenju Luo, Zhigang Natl Univ Def Technol Dept Sci & Technol Parallel & Distributed Proc Changsha 410073 Peoples R China Natl Univ Def Technol Inst Quantum Informat Changsha 410073 Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha 410073 Peoples R China Chinese Acad Sci Inst Automat Pattern Recognit & Intelligent Syst Beijing 100190 Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Changsha 410073 Peoples R China

Siamese trackers contain two core stages, i.e., learning the features of both target and search inputs at first and then calculating response maps via the cross-correlation operation, which can also be used for regression and classification to construct typical one-shot detection tracking framework. Although they have drawn continuous interest from the visual tracking community due to the proper trade-off between accuracy and speed, both stages are easily sensitive to the distracters in search branch, thereby inducing unreliable response positions. To fill this gap, we advance Siamese trackers with two novel non-local blocks named Nocal-Siam, which leverages the long-range dependency property of the non-local attention in a supervised fashion from two aspects. First, a target-aware non-local block (T-Nocal) is proposed for learning the target-guided feature weights, which serve to refine visual features of both target and search branches, and thus effectively suppress noisy distracters. This block reinforces the interplay between both target and search branches in the first stage. Second, we further develop a location-aware non-local block (L-Nocal) to associate multiple response maps, which prevents them inducing diverse candidate target positions in the future coming frame. Experiments on five popular benchmarks show that Nocal-Siam performs favorably against well-behaved counterparts both in quantity and quality.

关键词： Target tracking Visualization Feature extraction real-time systems Oceans Convolution Task analysis Siamese trackers non-local attention supervisedly attentive

来源：评论

学校读者我要写书评

暂无评论

A Novel Neural Network-Based Droop Control Strategy for Single-Phase Power Converters

引用

ENERGIES 2024年第23期17卷 5825-5825页

作者： Belgana, Saad Fortin-Blanchette, Handy Ecole Technol Super Dept Elect Engn Montreal PQ H3C 1K3 Canada

Managing parallel-connected single-phase distributed generators in low-voltage microgrids is challenging due to the volatility of renewable energy sources and fluctuating load demands. Traditional droop control struggles to maintain precise power sharing under dynamic conditions and varying line impedances, leading to inefficiency. This paper presents a novel adaptive droop control strategy integrating artificial neural networks and particle swarm optimization to enhance microgrid performance. Unlike prior methods that optimize artificial neural network parameters, the proposed approach uses particle swarm optimization offline to generate optimal dq-axis voltage references that compensate for line effects and load variations. These serve as training data for the artificial neural network, which adjusts voltage in real time based on line impedance and load variations without online optimization. This decoupling ensures computational efficiency and responsiveness, maintaining voltage and frequency stability during rapid load changes. Addressing dynamic load fluctuations and line impedance mismatches without inter-generator communication enhances reliability and reduces complexity. Simulations demonstrate that the proposed strategy maintains stability, achieves accurate power sharing with errors below 0.5%, and reduces total harmonic distortion, outperforming conventional droop control methods. These findings advance adaptive control in microgrids, supporting seamless renewable energy integration and enhancing the reliability and stability of distributed generation systems.

关键词： droop control micro grid dq control distributed generation artificial neural network particle swarm optimization single phase inverter islanding control

来源：评论

学校读者我要写书评

暂无评论

A Droop Control Method Based on Adaptive Virtual Impedance for parallel Control of distributed Generators

A Droop Control Method Based on Adaptive Virtual Impedance f...

引用

International Conference on Power Electronics systems and Applications (ICPESA)

作者： Taozi Mo Neng Li Zhang Zhang Junhua Li Zhenghong Zhang Jianfei Pan College of Mechatronics and Control Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350365801

ISBN: (纸本)9798350365818

Traditional droop control method cannot achieve effective reactive power distribution when distributed generators are paralleled. Therefore, this paper proposes a droop control scheme based on adaptive virtual impedance to solve the problem of uneven distribution of reactive power. The proposed method calculates the impedance difference between the two distributed power sources based on the feedback of line current and voltage, and introduces the adaptive virtual impedance compensation scheme accordingly. The proposed scheme reduces the amount of data to be transmitted during communication, and the system design is simple. The simulation in this paper demonstrate that, the proposed method reduces the percentage error in reactive power distribution from 10.5% to 0.4% under load variations and from 21% to 0.1%, when there is a sudden change in the impedance of one of the lines.

关键词： Reactive power Simulation Load management Generators real-time systems Power electronics Impedance

来源：评论

学校读者我要写书评

暂无评论

ADRL: A Hybrid Anomaly-Aware Deep Reinforcement Learning-Based Resource Scaling in Clouds

引用

IEEE TRANSACTIONS ON parallel AND distributed systems 2021年第3期32卷 514-526页

作者： Kardani-Moghaddam, Sara Buyya, Rajkumar Ramamohanarao, Kotagiri Univ Melbourne Sch Comp & Informat Syst Cloud Comp & Distributed Syst CLOUDS Lab Parkville Vic 3010 Australia

The virtualization concept and elasticity feature of cloud computing enable users to request resources on-demand and in the pay-as-you-go model. However, the high flexibility of the model makes the on-time resource scaling problem more complex. A variety of techniques such as threshold-based rules, time series analysis, or control theory are utilized to increase the efficiency of dynamic scaling of resources. However, the inherent dynamicity of cloud-hosted applications requires autonomic and adaptable systems that learn from the environment in real-time. Reinforcement Learning (RL) is a paradigm that requires some agents to monitor the surroundings and regularly perform an action based on the observed states. RL has a weakness to handle high dimensional state space problems. Deep-RL models are a recent breakthrough for modeling and learning in complex state space problems. In this article, we propose a Hybrid Anomaly-aware Deep Reinforcement Learning-based Resource Scaling (ADRL) for dynamic scaling of resources in the cloud. ADRL takes advantage of anomaly detection techniques to increase the stability of decision-makers by triggering actions in response to the identified anomalous states in the system. Two levels of global and local decision-makers are introduced to handle the required scaling actions. An extensive set of experiments for different types of anomaly problems shows that ADRL can significantly improve the quality of service with less number of actions and increased stability of the system.

关键词： Cloud computing Adaptation models Resource management Computational modeling Quality of service Dynamic scheduling Decision making Cloud computing anomaly detection deep reinforcement learning performance management vertical scaling

来源：评论

学校读者我要写书评

暂无评论

RENDA: Resource and Network Aware Data Placement Algorithm for Periodic Workloads in Cloud

引用

IEEE TRANSACTIONS ON parallel AND distributed systems 2021年第12期32卷 2906-2920页

作者： Thakkar, Hiren Kumar Sahoo, Prasan Kumar Veeravalli, Bharadwaj SRM Univ Dept Comp Sci & Engn Mangalagiri 522240 Andhra Pradesh India Chang Gung Univ Dept Comp Sci & Informat Engn Guishan 333 Taiwan Chang Gung Mem Hosp Dept Neurol Linkou 33305 Taiwan Natl Univ Singapore Dept Elect & Comp Engn Singapore 119077 Singapore

The Hadoop enabled cloud platforms are gradually becoming preferred computational environment to execute scientific big data workloads in a periodic manner. However, it is observed that the default data placement approach of such cloud platforms is not the efficient one and often ends up with significant data transfer overhead leading to degradation of the overall job completion time. In this article, a Resource and Network-aware Data Placement Algorithm (RENDA) is proposed to reduce the non-local executions and thereby reduce the overall job completion time for periodic workloads in the cloud environment. The entire job execution is modeled as a two-stage execution characterized as data distribution and data processing. The RENDA reduces the time of the stages as mentioned above by estimating the heterogeneous performance of the nodes on a real-time basis followed by careful allocation of data in several installments to participating nodes. The experimental results show that the proposed RENDA algorithm consistently outperforms over the recent state-of-the-art alternatives with as much as 28 percent reduction in data transfer overhead leading to 16 percent reduction in average job completion time with 27 percent average speedup on average job execution.

关键词： Cloud computing Data models distributed databases Data centers Task analysis Data transfer Switches MapReduce cloud computing data placement periodic workloads

来源：评论

学校读者我要写书评

暂无评论

DAUSK: A Transactional Graph Structure for Skewed Dynamic Graph Storage

DAUSK: A Transactional Graph Structure for Skewed Dynamic Gr...

引用

International Symposium on parallel and distributed Processing with Applications, ISPA

作者： Anna Ding Yujun Li Yuening Feng Sicong Hu Mengshu Hou Yueyang Zhan University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Dynamic graphs are widely used in real-world applications and exhibit structural skew, which leads to skewed updates. Existing systems that support transactional updates often fail to fully consider this phenomenon, struggling to balance update and query performance. Inspired by the concepts of hierarchical thinking and uneven rebalancing on PMA, we propose DAUSK, a high-performance in-memory structure for dynamic graph storage that supports efficient graph analytics and rapid transactional updates. DAUSK addresses the skewed characteristics of graphs by employing two types of data structures: an unrolled skip list for frequently updated high-degree vertices, allowing asymptotically faster searches and updates, and a compact array-like structure called UPMA for medium and low-degree vertices, enabling efficient sequential scans. At the same time, DAUSK uses a vertex-centric strategy to partition UPMA and applies uneven gap allocation based on vertex degrees. Futhermore, considering the characteristics of transactions on graphs, DAUSK uses the well-established 2PL concurrency protocol to support millions of transactional updates per second. Experimental results demonstrate that DAUSK is up to 10.17×, 3.29× and 1.52× faster in ingesting graph updates compared to three state-of-the-art transactional graph systems, i.e., LiveGraph, Teseo and Sortledton. As for graph analytics, DAUSK achieves comparable, if not superior, performance compared to the other three graph systems.

关键词： Concurrent computing distributed processing Analytical models Protocols Memory management distributed databases Data models Safety Resource management Arrays

来源：评论

学校读者我要写书评

暂无评论

G-thinker: a general distributed framework for finding qualified subgraphs in a big graph with load balancing

引用

VLDB JOURNAL 2022年第2期31卷 287-320页

作者： Yan, Da Guo, Guimu Khalil, Jalal Ozsu, M. Tamer Ku, Wei-Shinn Lui, John C. S. Univ Alabama Birmingham Dept Comp Sci Birmingham AL 35294 USA Univ Waterloo David R Cheriton Sch Comp Sci Waterloo ON Canada Auburn Univ Dept Comp Sci & Software Engn Auburn AL 36849 USA Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China

Finding from a big graph those subgraphs that satisfy certain conditions is useful in many applications such as community detection and subgraph matching. These problems have a high time complexity, but existing systems that attempt to scale them are all IO-bound in execution. We propose the first truly CPU-bound distributed framework called G-thinker for subgraph finding algorithms, which adopts a task-based computation model, and which also provides a user-friendly subgraph-centric vertex-pulling API for writing distributed subgraph finding algorithms that can be easily adapted from existing serial algorithms. To utilize all CPU cores of a cluster, G-thinker features (1) a highly concurrent vertex cache for parallel task access and (2) a lightweight task scheduling approach that ensures high task throughput. These designs well overlap communication with computation to minimize the idle time of CPU cores. To further improve load balancing on graphs where the workloads of individual tasks can be drastically different due to biased graph density distribution, we propose to prioritize the scheduling of those tasks that tend to be long running for processing and decomposition, plus a timeout mechanism for task decomposition to prevent long-running straggler tasks. The idea has been integrated into a novelty algorithm for maximum clique finding (MCF) that adopts a hybrid task decomposition strategy, which significantly improves the running time of MCF on dense and large graphs: The algorithm finds a maximum clique of size 1,109 on a large and dense WikiLinks graph dataset in 70 minutes. Extensive experiments demonstrate that G-thinker achieves orders of magnitude speedup compared even with the fastest existing subgraph-centric system, and it scales well to much larger and denser real network data. G-thinker is open-sourced at with detailed documentation.

关键词： Graph mining Subgraph-centric CPU-bound Compute-intensive Clique Triangle Subgraph matching

来源：评论

学校读者我要写书评

暂无评论

Near-Linear Scaling Data parallel Training with Overlapping-Aware Gradient Compression

Near-Linear Scaling Data Parallel Training with Overlapping-...

引用

International Conference on parallel and distributed systems (ICPADS)

作者： Lin Meng Yuzhong Sun Weimin Li Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

Existing Data parallel (DP) trainings for deep neural networks (DNNs) often experience limited scalability in speedup due to substantial communication overheads. While Overlapping technique can mitigate such problem by paralleling communication and computation in DP, its effectiveness is constrained by the high communication-to-computation ratios (CCR) of DP training tasks. Gradient compression (GC) is a promising technique to obtain lower CCR by reducing communication volume directly. However, it is challenging to obtain real performance improvement by applying GC into Overlapping because of (1) severe performance penalties in traditional GCs caused by high compression overhead and (2) decline of Overlapping benefit owing to the possible data dependency in GC schemes. In this paper, we propose COVAP, a novel GC scheme designing a new coarse-grained filter, makes the compression overhead close to zero. COVAP ensures an almost complete overlap of communication and computation by employing adaptive compression ratios and tensor sharding tailored to specific training tasks. COVAP also adopts an improved error feedback mechanism to maintain training accuracy. Experiments are conducted on Alibaba Cloud ECS instances with different DNNs of real-world applications. The results illustrate that COVAP outperforms existent GC schemes in time-to-solution by 1.92x-15.39x and exhibits near-linear scaling. Furthermore, COVAP achieves best scalability under experiments on four different cluster sizes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：