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检索条件"任意字段=International Symposium on Databases in Parallel and Distributed Systems"

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Constrained Multi-Agent Reinforcement Learning for Managing Electric Self-Driving Taxis

Constrained Multi-Agent Reinforcement Learning for Managing ...

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international Conference on parallel and distributed systems (ICPADS)

作者： Zhaoxing Yang Guiyun Fan Haiming Jin Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665408790

Electric self-driving taxis (es-taxis) draw great attention nowadays and hold the promise for future transportation due to their convenient and environment-friendly nature. However efficiently managing large-scale es-taxis remains an open problem. In this paper, we focus on scheduling es-taxis under charging budget constraint. Specifically, we design safe-controller to guarantee the satisfaction of budget constraint, and propose HAT framework to enlarge the sight for decision-making on deactivating es-taxis. As for the non-stationary induced by HAT, we analyze and limit its influence with theoretical guarantees. The overall framework Safe-HAT achieves superior performance in real-world data against other strong baselines.

关键词： Conferences Decision making distributed databases Reinforcement learning Planning Public transportation

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Energy-aware Preprocessing for distributed Training in D2D Edge Network with Non-iid Data

Energy-aware Preprocessing for Distributed Training in D2D E...

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international symposium on parallel Architectures, Algorithms and Programming (PAAP)

作者： Jiaxin Wu Jigang Wu Long Chen Yifei Sun School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781665496407

Inherent non-iid characteristic of heterogeneous devices' local dataset slows down the model training process and decreases the training accuracy. To tackle this problem, we propose a dataset reconstruction scheme to transform training device's non-iid dataset into iid dataset via data exchange among trusted devices. Two data exchange modes are proposed to serve the scheme. Considering the transmission overhead, we formulate an optimization problem named NORS to minimize the total energy consumption of devices. We then design an approximation algorithm named X-GTR to obtain a near-optimal auxiliary devices set for dataset reconstruction, while meeting the variance constraint of NORS. For comparison, we propose a baseline algorithm that randomly selects auxiliary devices for dataset reconstruction. Numerical results show that, with the total number of devices increasing, our proposed algorithm outperforms baseline algorithm on reducing energy consumption.

关键词： Training Energy consumption Simulation distributed databases Transforms Programming Approximation algorithms

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HPDC 2020 - Proceedings of the 29th international symposium on High-Performance parallel and distributed Computing

HPDC 2020 - Proceedings of the 29th International Symposium ...

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29th international symposium on High-Performance parallel and distributed Computing, HPDC 2020

ISBN: (纸本)9781450370523

The proceedings contain 22 papers. The topics discussed include: spying on the floating point behavior of existing, unmodified scientific applications;high accuracy matrix computations on neural engines: a study of QR factorization and its applications;space-efficient k-d tree-based storage format for sparse tensors;modeling the temporally constrained preemptions of transient cloud VMs;cloud-scale VM-deflation for running interactive applications on transient servers;funcX: a federated function serving fabric for science;towards HPC I/O performance prediction through large-scale log analysis;significantly improving lossy compression for HPC datasets with second-order prediction and parameter optimization;DCDB Wintermute: enabling online and holistic operational data analytics on HPC systems;and FFT-based gradient sparsification for the distributed training of deep neural networks.

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TorqueDB: distributed Querying of Time-Series Data from Edge-local Storage 26th

TorqueDB: Distributed Querying of Time-Series Data from Edge...

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26th international Conference on parallel and distributed Computing (Euro-Par)

作者： Garg, Dhruv Shirolkar, Prathik Shukla, Anshu Simmhan, Yogesh Ericsson Res Bangalore Karnataka India Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9783030576752;9783030576745

The rapid growth in edge computing devices as part of Internet of Things (IoT) allows real-time access to time-series data from 1000's of sensors. Such observations are often queried to optimize the health of the infrastructure. Recently, edge storage systems allow us to retain data on the edge rather than moving them centrally to the cloud. However, such systems do not support flexible querying over the data spread across 10-100's of devices. There is also a lack of distributed time-series databases that can run on the edge devices. Here, we propose TorqueDB, a distributed query engine over time-series data that operates on edge and fog resources. TorqueDB leverages our prior work on ElfStore, a distributed edge-local file store, and InfluxDB, a time-series database, to enable temporal queries to be decomposed and executed across multiple fog and edge devices. Interestingly, we move data into InfluxDB on-demand while retaining the durable data within ElfStore for use by other applications. We also design a cost model that maximizes parallel movement and execution of the queries across resources, and utilizes caching. Our experiments on a real edge, fog and cloud deployment show that TorqueDB performs comparable to InfluxDB on a cloud VM for a smart city query workload, but without the associated monetary costs.

关键词： Edge storage Time-series database distributed querying Internet of Things Cloud computing

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A Real-Time Hardware-in-the-Loop (HIL) Cybersecurity Testbed for Power Electronics Devices and systems in Cyber-Physical Environments

A Real-Time Hardware-in-the-Loop (HIL) Cybersecurity Testbed...

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IEEE international symposium on Power Electronics for distributed Generation systems (PEDG)

作者： Jinchun Choi Deneesh Narayanasamy Bohyun Ahn Seerin Ahmad Jianwu Zeng Taesic Kim Texas A&M University-Kingsville Kingsville TX USA Minnesota State Univeristy Mankato Mankato USA

Cybersecurity of power electronics (PE) is increasingly essential as more systems from renewable energy systems, energy storage systems, and electric vehicle charging stations utilize PE devices to connect them into power grids with complex communication and computation systems for advanced control and situational awareness improvement in smart grid environments. However, cybersecurity research and development (R&D) of networked power electronics (PE) is hampered by the lack of real-time security testbed incorporating real cyber events. This paper proposes a real-time hardware-in-the-loop cybersecurity tested for PE systems in cyber-physical environments. The proposed security testbed consists of: 1) a real-time PE system simulator; 2) a real-time cyber system using real network systems and a server; and 3) penetration testing tools generating real Cyber-attacks as cyber events. Several real cyber-attacks are created and their impacts in a PE system are provided to validate the feasibility of the proposed security testbed.

关键词： Renewable energy sources distributed databases Tools Control systems Real-time systems Smart grids Servers

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Issues and Algorithm of distributed Shared Memory

Issues and Algorithm of Distributed Shared Memory

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international Conference on Innovative Computing (ICIC)

作者： Mian Basam Ahmad Sundas Sagheer University of Management and Technology Lahore Pakistan

ISBN: (纸本)9781665400923

Internet of things is a rapidly growing industry, and there are more connected devices than humans. There are unlimited internet of things devices around us. The scope of these devices has increased manifold in recent years in different areas like healthcare, unmanned vehicles, automatic monitoring systems, and smart homes, etc. These connected devices produce lots of data that need to be processed to achieve the goal of automation in daily life. The cloud provides many centralized solutions to process data using powerful resources in real-time, but there could be latency issues due to increased network traffic. So a distributed solution is much needed to meet this growing demand of processing data in parallel. In this paper, we propose a distributed shared memory [18] abstraction for the internet of things that uses a graph-aware partitioning algorithm and uses smart message passing techniques to process data in distributed and parallel fashion [19].

关键词： Manifolds Message passing distributed databases Telecommunication traffic Smart homes Medical services Real-time systems

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distributed Tree-Pattern Matching in Big Data Analytics systems 24th

Distributed Tree-Pattern Matching in Big Data Analytics Syst...

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24th East-European Conference on Advances in databases and Information systems/24th international Conference on Theory and Practice of Digital Libraries/16th Workshop on Business Intelligence and Big Data (ADBIS/TPDL/EDA)

作者： Diestelkamper, Ralf Hersche, Melanie Univ Stuttgart Stuttgart Germany Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9783030548315;9783030548322

Big data analytics systems such as Apache Spark offer built-in support for nested data, which abounds, for instance, as JSON data available online. However, these systems typically have to transform the data to gain access to (deeply) nested data for further processing. This adds complexity to big data analytics pipelines and may result in an unnecessary runtime overhead. Therefore, this paper introduces tree-pattern matching as a first-class operator in big data analytics systems. It reduces the complexity of big data analytics pipelines and accelerates the pipeline processing by up to four times, compared to state-of-the-art pipelines for nested data. The novelty of our operator lies in the distributed and data-parallel processing supported by its underlying tree-pattern matching algorithm. Experiments validate that our operator, implemented in Spark, can improve pipeline complexity and runtime.

关键词： Tree-pattern matching Nested data Big data analytics

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Remove-Win: a Design Framework for Conflict-free Replicated Data Types

Remove-Win: a Design Framework for Conflict-free Replicated ...

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international Conference on parallel and distributed systems (ICPADS)

作者： Yuqi Zhang Hengfeng Wei Yu Huang National Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu Province China

ISBN: (纸本)9781665408790

distributed storage systems employ replication to improve performance and reliability. To provide low latency data access, replicas are often required to accept updates without coordination with each other, and the updates are then propagated asynchronously. This brings the critical challenge of conflict resolution among concurrent updates. Conflict-free Replicated Data Type (CRDT) is a principled approach to addressing this challenge. However, existing CRDT designs are tricky, and hard to be generalized to other data types. A design framework is in great need to guide the systematic design of new CRDTs. To address this challenge, we propose RWF - the Remove-Win design Framewerk for CRDTs. RWF leverages the simple but powerful remove-win strategy to resolve conflicting updates, and provides generic design for a variety of data container types. Two exemplar implementations following RWF are given over the Redis data type store, which demonstrate the effectiveness of RWF. Performance measurements of our implementations further show the efficiency of CRDT designs following RWF.

关键词： Measurement Systematics Conferences distributed databases Containers Reliability Low latency communication

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Digital Twin of Overseas Opera Relics: Case Study of Arhat Costumes in the Collection of the Metropolitan Museum of Art

Digital Twin of Overseas Opera Relics: Case Study of Arhat C...

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IEEE international symposium on parallel and distributed Processing with Applications and IEEE international Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Jingyuan Ren Ying Sun Xiaping Shu School of Applied Technology Soochow University Suzhou China School of Art Shandong Agriculture And Engineering University Jinan China School of Art Soochow University Suzhou China

Overseas opera relics are the materialized expression of Chinese opera civilization with a long history. The collection of overseas opera relics information, cataloguing and sorting, and the establishment of a digital database help to enrich and improve the physical data of domestic opera research. As an emerging digital modeling and virtual restoration technology, “digital twin” is of great significance in collecting digital resources of overseas opera relics and realizing digital restoration and global sharing of overseas opera relics. In this paper, after data collection of arhat costumes from the Metropolitan Museum of Art, database establishment, and the textual research and analysis of the shape, pattern and fabric, a “digital twin” of the arhat costumes through 3D modeling was created with the help of “digital twin” technology. which enables the preservation of overseas opera relics with digital technology and promotes the wide dissemination and creative transformation of overseas opera relics.

关键词： Art Shape databases Clothing Data collection Ubiquitous computing Museums

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BSDP: A Novel Balanced Spark Data Partitioner

BSDP: A Novel Balanced Spark Data Partitioner

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international Conference on parallel and distributed systems (ICPADS)

作者： Aibo Song Bowen Peng Jingyi Qiu Yingying Xue Mingyang Du School of Computer Science and Engineering Southeast University Nanjing China Nanjing Foreign Language School Nanjing China

ISBN: (纸本)9781665408790

As a memory-based distributed big data computing framework, Spark has been widely used in big data processing systems. However, during the execution of Spark, due to the imbalance of input data distribution and the shortage of existing data partitioners in Spark, it is easy to cause partition skew problem and reduce the execution efficiency of Spark. Aiming at this problem, this paper proposes a balanced Spark data partitioner called BSDP (Balanced Spark Data Partitioner). By deeply analyzing the partitioning characteristics of Shuffle intermediate data, the Spark Shuffle intermediate data equalization partitioning model is established. The model aims to minimize the partition skew and find a Shuffle intermediate data equalization partitioning strategy. Based on the model, this paper designs and implements a data equalization partitioning algorithm of BSDP. This algorithm transforms the Shuffle intermediate data equalization partitioning problem into a classic List-Scheduling task scheduling problem, effectively realizes the balanced partitioning of Shuffle intermediate data. The experiment verifies that the BSDP can effectively realize the balanced partitioning of the Shuffle intermediate data and improve the execution efficiency of Spark.

关键词： Analytical models Computational modeling distributed databases Transforms Big Data Data models Scheduling

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