At present, large amount distributed energy storages (DESs) connected to the distribution network lack of effective scheduling methods. An centralized control strategy of DESs with random access and output can be util...
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The proceedings contain 55 papers. The special focus in this conference is on Cloud computing. The topics include: Rendering of Three-Dimensional Cloud Based on Cloud computing;distributed Stochastic Alternating Direc...
ISBN:
(纸本)9783030485122
The proceedings contain 55 papers. The special focus in this conference is on Cloud computing. The topics include: Rendering of Three-Dimensional Cloud Based on Cloud computing;distributed Stochastic Alternating Direction Method of Multipliers for Big Data Classification;personalized Recommendation Algorithm Considering Time Sensitivity;cloud-Based Master Data Platform for Smart Manufacturing Process;a Semi-supervised Classification Method for Hyperspectral Images by Triple Classifiers with Data Editing and Deep Learning;A Survey of Image Super Resolution Based on CNN;design and Development of an Intelligent Semantic Recommendation System for Websites;a Lightweight Neural Network Combining Dilated Convolution and Depthwise Separable Convolution;resource Allocation Algorithms of Vehicle Networks with Stackelberg Game;research on Coordination Control Theory of Greenhouse Cluster Based on Cloud computing;a Multi-objective Computation Offloading Method in Multi-cloudlet Environment;anomalous Taxi Route Detection System Based on Cloud Services;collaborative Recommendation Method Based on Knowledge Graph for Cloud Services;efficient Multi-user Computation Scheduling Strategy Based on Clustering for Mobile-Edge computing;Grazing Trajectory Statistics and Visualization Platform Based on Cloud GIS;Cloud-Based AGV Control System;a parallel Drone Image Mosaic Method Based on Apache Spark;cycleSafe: Safe Route Planning for Urban Cyclists;prediction of Future Appearances via Convolutional Recurrent Neural Networks Based on Image Time Series in Cloud computing;video Knowledge Discovery Based on Convolutional Neural Network;time-Varying Water Quality Analysis with Semantical Mining Technology;a Survey of QoS Optimization and Energy Saving in Cloud, Edge and IoT;data-Driven Fast Real-Time Flood Forecasting Model for Processing Concept Drift;intelligent System Security Event Description Method.
Checkpointing is basically a technique of fault tolerance for various computing systems. In Checkpointing we save the state of a process during execution periodically, so that applications can restart from that point ...
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Cloud resources can be dynamically provisioned according to application-specific requirements and are payed on a per-use basis. This gives rise to a new concept for parallel processing: Elastic parallel computations. ...
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ISBN:
(数字)9783030494322
ISBN:
(纸本)9783030494315;9783030494322
Cloud resources can be dynamically provisioned according to application-specific requirements and are payed on a per-use basis. This gives rise to a new concept for parallel processing: Elastic parallel computations. However, it is still an open research question to which extent parallel applications can benefit from elastic scaling, which requires resource adaptation at runtime and corresponding coordination mechanisms. In this work, we analyze how to address these system-level challenges in the context of developing and operating elastic parallel tree search applications. Based on our findings, we discuss the design and implementation of TASKWORK, a cloud-aware runtime system specifically designed for elastic parallel tree search, which enables the implementation of elastic applications by means of higher-level development frameworks. We show how to implement an elastic parallel branch-and-bound application based on an exemplary development framework and report on our experimental evaluation that also considers several benchmarks for parallel tree search.
Kyber, an IND-CCA-secure key encapsulation mechanism (KEM) based on the MLWE problem, has been shortlisted for the third round evaluation of the NIST Post-Quantum Cryptography Standardization. In this paper, we explor...
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Multi-Agent Systems (MAS) are naturally good candidates for large-scale parallel simulations. However, implementing MAS simulations for distributed memory architectures, such as High Performance computing clusters, is...
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Multi-Agent Systems (MAS) are naturally good candidates for large-scale parallel simulations. However, implementing MAS simulations for distributed memory architectures, such as High Performance computing clusters, is still complex for non-experts. In this article we present the principle of a Dynamic distributed Graph structure, that enables the native distribution of MAS simulations. Most of the distribution related issues such as dynamic load-balancing, time synchronization and data migration across processes can be completely automated and abstracted for the user, who can safely design distribution independent MAS models. The major interest of our contribution is the transparent management of concurrent read / write requests across distant processes, a significant feature not provided by surveyed platforms. We also present FPMAS, an open source C++ implementation of a distributed Multi-Agent System Simulation platform based on the distributed Graph structure.
Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducin...
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Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution Network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale Knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.
In the future heterogeneous integrated wireless network environment, delay limited network has the characteristics of distributed self-organization, multi hop transmission, delay tolerance and intermittent link connec...
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The proceedings contain 11 papers. The topics discussed include: accelerate distributed stochastic descent for Nonconvex optimization with momentum;accelerating GPU-based machine learning in python using MPI library: ...
ISBN:
(纸本)9780738110783
The proceedings contain 11 papers. The topics discussed include: accelerate distributed stochastic descent for Nonconvex optimization with momentum;accelerating GPU-based machine learning in python using MPI library: a case study with MVAPICH2-GDR;deep learning-based low-dose tomography reconstruction with hybrid-dose measurements;EventGraD: event-triggered communication in parallel stochastic gradient descent;a benders decomposition approach to correlation clustering;high-bypass learning: automated detection of tumor cells that significantly impact drug response;deep generative models that solve PDEs: distributedcomputing for training large data-free models;automatic particle trajectory classification in plasma simulations;reinforcement learning-based solution to power grid planning and operation under uncertainties;and predictions of steady and unsteady flows using machine-learned surrogate models.
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