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共 4331 条记录，以下是681-690 订阅

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MIMO based Uncertainty-aware Learning-to-Rank Query Optimizer

MIMO Based Uncertainty-aware Learning-to-Rank Query Optimize...

引用

International conference on parallel and distributed Systems (ICPADS)

作者： Jincan Xiong Rui Xi Xue Chen Zihan Yan Junxin Zhu Mengshu Hou SCSE UESTC Chengdu China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

The query optimizer is a critical component within the database management system(DBMS), tasked with translating user-input SQL queries into efficient execution plans. Traditional query optimizers rely on statistical data that may become outdated, resulting in inaccuracies leading to suboptimal execution plans and consequent declines in query performance. In response, learning-based optimizers have emerged, utilizing historical data to improve plan selection quality. However, these approaches often struggle to handle dynamic workloads and fail to consistently achieve robust optimization outcomes. This paper introduces MIMO-Lero, an uncertainty-aware learning-based query optimizer designed for seamless integration into existing DBMS platforms without requiring extensive modifications. Inspired by neural network uncertainty principles, we design a Multi-in Multi-out (MIMO) architecture-based plan ranker. This ranker predicts both the relative ranking order of plans and their associated uncertainty levels, signifying the confidence in the order prediction. Furthermore, we propose two novel selection strategies that incorporate uncertainty to enhance the robustness of query optimization. By implementing MIMO-Lero and evaluating its performance using PostgreSQL, experimental results on diverse datasets showcase significant improvements in robust query optimization while maintaining high prediction accuracy.

关键词： Uncertainty Accuracy Databases Query processing Neural networks Robustness Optimization

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Microservice-based Design and Key Technology of the Dispatching and Control Business Middle Platform for the Large Power Grid

Microservice-Based Design and Key Technology of the Dispatch...

引用

IEEE conference on Energy Internet and Energy System Integration (EI2)

作者： Lingtao Zhang Yanyan Liu Wenxi Cui Haitao Dong Shuai Cui Beijing Kedong Electric Power Control System Corporation Limited NARI Group (State Grid Electric Power Research Institute) Corporation Limited Nanjing China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

In order to reduce the service coupling degree of the large power grid dispatching and control system and improve its ability to quickly respond to user needs, a microservice-based large power grid dispatching and control business middle platform has also been proposed and constructed. The microservice-based large power grid model cloud technology, wide-area data distributed streaming processing technology and new human-machine cloud terminal technologies are proposed, and the core business functions such as power grid model service, distributed data processing and human-machine browsing that constitute the business middle platform are developed, And the technical problems such as model on-demand service, high-speed parallel data processing, and local and remote non-differential dispatching are solved, and the business middle platform for the whole network multiplexing of the large power grid control system is constructed, which provides strong support for various applications in the front desk. The application effects of the business middle platform in specific scenarios have been presented, demonstrating the validity of the business middle platform proposed in this paper. The demonstration effect shows that the business middle platform proposed can improve the speed of the front platform of the control system to respond to user needs.

关键词： Cloud computing Human-machine systems Microservice architectures System integration Control systems Data processing Power grids Dispatching Data models Business

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Towards network-accelerated ML-based distributed Computer Vision Systems 27

Towards Network-accelerated ML-based Distributed Computer Vi...

引用

27th IEEE International conference on parallel and distributed Systems, ICPADS 2021

作者： Siddique, Hisham Neves, Miguel Kuzniar, Carson Haque, Israat Dalhousie University Faculty of Computer Science HalifaxNS Canada

ISBN: (纸本)9781665408783

Computer vision is a crucial component in many modern applications (e.g., medical image analysis, environmental monitoring and self-driving cars). However, their stringent computational, latency and bandwidth requirements still pose a huge challenge to system architects, which must seek for alternatives to both the limited resources (e.g., low-end CPU) on client devices and the hurdles of moving data from clients to cloud/edge servers for analysis. In this work, we advocate for the usage of emerging programmable network devices to speed up ML-based computer vision tasks, particularly image classification, on resource constrained environments. To take the first step towards this new paradigm, we propose NetPixel, a framework that enables P4-programmable switches to classify images in realtime, accurately and at scale. We implemented a prototype of NetPixel in a software switch to show its feasibility and conducted a preliminary evaluation on widely adopted datasets. Our results show that NetPixel can classify images with an accuracy within 8% that of a server-based implementation even for shallow classifiers and low-resolution images. © 2021 IEEE.

关键词： network protocols

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Machine learning based network intrusion detection for data streaming IoT applications 21

Machine learning based network intrusion detection for data ...

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21st ACIS International Winter conference on Software Engineering, Artificial Intelligence, networking and parallel/distributed Computing (SNPD-Winter)

作者： Yahyaoui, Aymen Lakhdhar, Haithem Abdellatif, Takoua Attia, Rabah Univ Carthage SERCOM Lab Mil Acad Fondouk Jedid Tunis Tunisia Mil Acad Fondouk Jedid Tunis Tunisia Univ Carthage SERCOM Lab Tunis Tunisia

ISBN: (纸本)9781665418430

In recent years, Internet of Things (IoT) technologies have been widely used in many fields such as surveillance, healthcare, smart metering and environment monitoring. This extensive usage leads to massive data management and a complexity in data analysis. A huge number of IoT sensors are deployed for monitoring task and send continuously their collected data to gateways. IoT applications are analyzing these data flows and making real time decisions about specific monitored events (fire, flood, terrorist attacks, etc.). Anomalies that may be related to sensor failures or network intrusions are affecting such decisions. Therefore, they should be detected and eliminated as soon as they arrive. This task requires real time data processing detectors for making accurate and fast predictions. In this paper, we design an architecture for a real time network intrusion detection system for IoT streaming data. The system was developed, deployed and tested with the two leading stream processing frameworks (Apache fink and Apache Spark Streaming). We used two different public datasets and different machine learning algorithms. Results show considerable throughputs and high detection accuracy especially for Apache Flink.

关键词： Anomaly detection Intrusion detection Stream processing Machine Learning Internet of Things

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An Iterative Cleaning Method for Redundant Data in Multi-Source Heterogeneous Data based on Decision Tree Algorithm

An Iterative Cleaning Method for Redundant Data in Multi-Sou...

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Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Jiaxuan Hou Wenqi Huang Lingyu Liang Shang Cao Huanming Zhang Xiangyu Zhao Hanju Li Digital Grid Research Institute China Southern Power Grid. Guangzhou Guangdong China

The current conventional iterative cleaning method for multi-source heterogeneous data mainly calculates the data repetition to achieve the redundant data rejection, which leads to poor data cleaning effect due to the lack of parsimonious processing for data. In this regard, an iterative cleaning method based on decision tree algorithm is proposed for redundant data in multi-source heterogeneous data. By calculating the information gain value of redundant data in multi-source heterogeneous data, and using the highest value as the classification criterion, a decision tree is constructed to realize data classification. And the deep belief network model is constructed to extract the data mixture features, and finally the data is iteratively cleaned by data table repetition judgment rule pairs for data parsimony. In the experiments, the proposed method is verified for the cleaning effect. The experimental results show that when the proposed method is used to clean multi-source heterogeneous data, the data checking accuracy is high and has a more desirable data cleaning performance.

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Control of Unmanned Bicycle based on Tensor Product Model Transformation and Hammersley Sampling Method

Control of Unmanned Bicycle Based on Tensor Product Model Tr...

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International conference on Intelligent Control and Information processing (ICICIP)

作者： Depeng Xie Degang Wang Guoliang Zhao Hongxing Li School of Control Science and Engineering Dalian University of Technology Dalian China College of Electronic Information Engineering Inner Mongolia University Hohhot China

In this paper, path planning and balance control of unmanned bicycles are investigated. Firstly, the Deep Deterministic Policy Gradient (DDPG) algorithm is used to train the Artificial Potential Field (APF) algorithm to implement path planning in complex maps. Secondly, a dual closed-loop control strategy is adopted for the path tracking and the balance control. In the inner-loop control, the Hammersley sampling (HS) method is used to obtain the hyper-cube network model of the unmanned bicycle, and the balance controller is designed based on the tensor product (TP) model transformation and parallel distributed compensation (PDC) method for stabilization of the unmanned bicycle. The outer-loop control system uses the Ackermann steering geometry model to track the planned path. Finally, the simulation results demonstrate that the path planning algorithm plans the path that satisfies the constraints and the designed balance controller can keep the unmanned bicycle balanced well during trajectory tracking.

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A Multi-attention based CNN-BiLSTM Intrusion Detection Model for In-vehicle networks

A Multi-attention Based CNN-BiLSTM Intrusion Detection Model...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Kai Gao Hao Huang Linhong Liu Ronghua Du Jinlai Zhang College of Automotive and Mechanical Engineering Changsha University of Science & Technology Changsha China

The development of vehicular networking technology continuously enhances the internet connectivity of modern vehicles. However, for in-vehicle networks, constant communication with the outside world dramatically increases vehicle security risks, posing serious security threats to automotive systems and occupants. This paper proposes a CNN-BiLSTM vehicular network intrusion detection model based on multi-head attention by analyzing the information security vulnerabilities in the Controller Area network (CAN) bus. By extracting only the communication features of CAN messages, including CAN ID and Data field, this paper conducted a multiclass classification study on the real-time traffic dataset CarHacking. The model achieved a detection accuracy of 99.992%. The reduction of features effectively simplifies the model and improves its computation efficiency.

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SNIP: Southbound Message Delivery with In-network Pruning in Clouds

SNIP: Southbound Message Delivery with In-network Pruning in...

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International conference on parallel and distributed Systems (ICPADS)

作者： Yan Yu Gongming Zhao Hongli Xu Huaqing Tu Luyao Luo Liguang Xie School of Data Science University of Science and Technology of China School of Computer Science and Technology University of Science and Technology of China Futurewei Technologies USA

In a hyper-scale cloud data center, a large number of control messages are distributed to hundreds of thousands of compute nodes from a logically centralized control plane. The delivery of these control messages, a.k.a. southbound messages, is critical to cloud infrastructure management, as it greatly affects customer experience. Existing works mainly deal with southbound message delivery by two methods: point-to-point transmission and Message Queue (MQ)-based solutions. With the point-to-point transmission method, each message is sent from the control plane to compute nodes directly, which may cause high control complexity and overhead in hyper-scale clouds. The MQ-based method can address the challenge of high complexity through message aggregation and subscribe/publish model. However, it usually brings in redundant messages, and further causes extra load on compute nodes. To solve the problem above, we design SNIP, which exploits the ability of programmable switches to perform in-network message pruning and to reduce message redundancy. Specffically, forwarding and processing information computed by the control plane is attached to the package header of every control message. Redundant messages can be identified and processed by programmable switches. In addition, we propose a rounding-based algorithm to prune messages with minimal redundancy. The simulation results show that SNIP can reduce the control overhead by 80%-85% and the total traffic of redundant messages by 35% compared with existing solutions.

关键词： Data centers Simulation Computational modeling Redundancy Process control Switches Approximation algorithms

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Optimizing Resource Allocation in Pipeline parallelism for distributed DNN Training

Optimizing Resource Allocation in Pipeline Parallelism for D...

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International conference on parallel and distributed Systems (ICPADS)

作者： Yubin Duan Jie Wu Temple University USA

Deep Neural network (DNN) models have been widely deployed in a variety of applications. Driven by privacy concerns and great improvement in the computational power of mobile devices, the idea of training machine learning models on mobile devices has become more and more important. Directly applying parallel training frameworks designed for data center networks to train DNN models on mobile devices may not achieve the ideal performance, since mobile devices usually have multiple types of computation resources such as ASIC, neural engine, and FPGA. Moreover, the communication time is not negligible when training on mobile devices. With the objective of minimizing DNN training time, we propose to extend the pipeline parallelism, which can hide the communication time behind computation for DNN training by integrating the resource allocation. Fine-tuning the ratio of resources allocated to forward and backward propagation can improve resource utilization. We focus on homogeneous workers and theoretically analyze the ideal cases where resources are linearly separable. We also discuss the model partition and resource allocation for a more realistic case. Additionally, we investigate the heterogeneous worker case. Trace-based simulation results show that our scheme can efficiently reduce the time cost of a training iteration.

关键词： Training Performance evaluation Privacy Computational modeling Simulation Pipelines parallel processing

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A Novel Multi-scale Spatial Tiling based Adaptive Streaming Method for 360° Video

A Novel Multi-scale Spatial Tiling Based Adaptive Streaming ...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Haipeng Du Huichuan Liu Geli Lv Xin Lv Zhiwen Wang Weizhan Zhang School of Computer Science and Technology BDKE Lab Xi’an Jiaotong University Xi’an China School of Continuing Education Xi’an Jiaotong University Xi’an China Network Information Center Xi’an Jiaotong University Xi’an China

The surging popularity of 360° videos has presented new challenges in the domains of video processing and streaming. In this paper, we introduce a novel 360° video adaptive streaming methodology grounded in multi-scale spatial tiling. This approach seamlessly navigates intricate network conditions, while assuring an optimal user experience. Firstly, through the utilization of a transformer-based saliency detection model, we pinpoint the salient regions within 360° videos that are poised to engage the user’s attention. Subsequently, we undertake Region of Interest (RoI) encoding and multi-scale spatial tiling, facilitating the efficient representation and delivery of 360° videos. Finally, we utilize reinforcement learning to achieve effective adaptive bitrate *** results demonstrate the advantages of our approach in terms of enhancing the overall Quality of Experience (QoE) across intricate network environments.

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