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检索条件"任意字段=23rd International Conference on Distributed Computing and Networking, ICDCN 2022"

共 140 条记录，以下是61-70 订阅

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distributed Consensus Problem in Federated Learning Paradigm 23

Distributed Consensus Problem in Federated Learning Paradigm

引用

23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yan, Xin Qin, Yiming Hu, Xiaodong School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China The University of Hong Kong The Faculty of Education Hong Kong Hong Kong

ISBN: (纸本)9781665494571

Federated learning (FL) framework facilitates more and more applications of deep learning algorithms on the existing network architectures, where the model parameters are aggregated in a centralized manner. However, some of FL participants are often inaccessible, such as in a power shortage or device dormant state. That will force us to explore the probability that the parameter aggregation is operated in an ad hoc manner, which is based on consensus computing. In this paper, we first propose a novel FL paradigm, which supports an ad hoc operation mode for FL participants. Second, a discrete-time dynamic equation and its control law are formulated to satisfy the demands from FL framework, with a quantized caching scheme designed to mask the uncertainties from asynchronous updates and measurement noises. Then, the consensus conditions and the convergence of the consensus protocol are deduced analytically, and a strategy of quantized caching to optimize the convergence speed is provided. Last, the theoretical results are validated by numerical simulations. © 2021 IEEE.

关键词： Uncertainty Smart cities Delay effects Packet loss Collaborative work Particle measurements Numerical simulation

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Collaborative Random Forests Learning 23

Collaborative Random Forests Learning

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作者： Foucade, Yohan Bennani, Younes Aabbou, Zakaria LIPN-CNRS Umr 7030 Université Sorbonne Paris Nord LaMSN La Maison des Sciences Numèriques France

ISBN: (纸本)9781665494571

Collaborative learning is an emerging field of machine learning. In this framework, multiple learning algorithms try to learn from a distributed database. The main idea is to improve the performance of each algorithm by exchanging information about the local structures of the data, without disclosing sensitive information. It is related to ensemble learning and federated learning. In this work, we presents a collaborative supervised learning approach. We used classification trees as a base algorithm, although the framework is generic and could be implemented with a wide class of algorithms. The process is divided into two steps: a local and a collaboration step. The local step uses a classical supervised learning algorithm, locally and independently on each dataset, which will result in obtaining multiple classifiers for each of those bases. During the collaboration step, each algorithm will collaborate with its remote counterparts. Our approach has been tested on several datasets and has shown promising results. © 2021 IEEE.

关键词： Machine learning algorithms Smart cities Supervised learning Collaboration distributed databases Collaborative work Classification algorithms

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distributed data-driven engine framework facing CPS brain-inspired intelligent transportation 23

Distributed data-driven engine framework facing CPS brain-in...

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作者： Yang, Zhengzhuang Xu, Mingyue Zhao, Kaidi Yang, Chao Zhi, Huiyu Zhang, Xihou Chen, Zhuoran Qiu, Song Han, Dingding School of Information Science and Technology Fudan University Shanghai China School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (纸本)9781665494571

Single data source, isolated data islands and low information utilization in intelligent transportation lead to the poor inversion of the cyber space to the physical space including Incomplete data integration, high interdependency of systems and poor anti-control ability. In this paper the data-driven method is adopted to optimize the W parameter in Webster method to reduce the waiting time of vehicles to realize the traffic congestion. In particular, a multi-source heterogeneous data processing method and a unified modeling of the multi-dimensional and multi-layer networks have been introduced. By data-driven engine, we have implemented both massive storage and fast processing system. We have also build a service system which can rapidly realize data integration and services delivery as well as be directly called by researchers from the database through network requests to reduce the data reprocessing. This paper also produces the data interaction process of the data engine framework in traffic management and control, and proposes a solution for the use of massive city data. © 2021 IEEE.

关键词： Smart cities Soft sensors Transportation Data integration Process control distributed databases Data models

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Efficiently Managing Large-Scale Keys in HDFS 23

Efficiently Managing Large-Scale Keys in HDFS

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作者： Jin, Wei Geng, Kui Yu, Mingjie Guo, Yunchuan Li, Fenghua Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China School of Cyber Security University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9781665494571

To transparently encrypt/decrypt data stored in Hadoop distributed File System (HDFS), the Key Management Server (KMS) has been designed to automatically assign encryption zone keys (EZK) to encryption zones. However, the current key-management scheme suffers from performance bottleneck. In this paper, dividing key files for storing key data into metadata file and material file, we propose a memory-disk coordinated key management architecture for Hadoop EZK to manage massive keys, where a tree structure is designed to index key metadata stored in memory, and the inverted index structure is used to record materials stored on hard disk. Further balance-tree-based management algorithms without Java serialization is designed to efficiently query keys (including metadata and materials). A series of theoretical analysis are conducted to show the upper and lower boundary of metadata query. Finally, we implement the above scheme in the Hadoop KMS without modifying the original key-management interfaces. A series of experiments are carried out on the simulated data. Experimental results show that, our scheme can efficiently manage ten billion keys and perform much better than the existing KMS in Hadoop. © 2021 IEEE.

关键词： Java Smart cities File systems Memory management distributed databases Metadata Hard disks

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distributed Trust Evaluation Mechanism of LEO Satellites for 6G Network 23

Distributed Trust Evaluation Mechanism of LEO Satellites for...

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作者： Li, Hui Shi, DongCong Zhou, Ruijin Liao, Dan Zhang, Ming Zhou, Yuliang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Chengdu Research Institute University of Electronic Science and Technology of China Chengdu China School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665494571

In 6G network, the LEO satellite communication is popular because of the lower development costs and lower transmission delay. However, due to the instability, openness and exposure of links between satellites, LEO satellite is vulnerable to internal attacks during data routing. Considering that trust management has a better performance in solving the internal attacks between satellite communications, this paper proposed a new distributed trust evaluation mechanism (DTEM) of LEO satellites based on D-S evidence theory. In the proposed trust evaluation mechanism, it evaluates the trust degree of satellite nodes by direct trust, indirect trust and aggregate trust. Then it detects the malicious satellite nodes for 6G networks and takes corresponding isolation measures. The simulations verifies that the proposed trust evaluation mechanism has superior performance in packet delivery rate and packet loss rate for data routing. © 2021 IEEE.

关键词： 6G mobile communication Satellites Smart cities Evidence theory Low earth orbit satellites Packet loss Routing

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A Real-time Demand Response Strategy of Home Energy Management by Using distributed Deep Reinforcement Learning 23

A Real-time Demand Response Strategy of Home Energy Manageme...

引用

作者： Liu, Weirong Wang, Yujie Jiang, Fu Cheng, Yijun Rong, Jieqi Wang, Chenglong Peng, Jun School of Computer Science and Engineering Central South University Changsha China School of Automation Central South University Changsha China

ISBN: (纸本)9781665494571

Home energy management system (HEMS) autonomously schedules the energy usage of home electricity consuming devices to reduce the electricity cost based on real-time electricity price. However, it is very challenging to design an optimal energy management strategy under the uncertainty of the resident's behavior, outdoor temperature and renewable generation. To solve this problem, a real-time demand response (DR) strategy based on deep reinforcement learning (DRL) is proposed. Firstly, the real-time DR management problem is modeled as a Markov game, and then a home energy management algorithm is proposed based on Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3). It is worth mentioning that the proposed algorithm builds the thermal dynamics model and takes energy storage system (ESS) into account, which improves the utilization of renewable power generation effectively. The simulation result based on real-world data demonstrates the effectiveness of the proposed algorithm. © 2021 IEEE.

关键词： Renewable energy sources Schedules Costs Uncertainty Heuristic algorithms Simulation Reinforcement learning

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Analyzing I/O Performance of a Hierarchical HPC Storage System for distributed Deep Learning 23rd

Analyzing I/O Performance of a Hierarchical HPC Storage Sy...

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23rd international conference on Parallel and distributed computing, Applications, and Technologies, PDCAT 2022

作者： Fukai, Takaaki Sato, Kento Hirofuchi, Takahiro Tokyo Japan RIKEN Center for Computational Science Kobe Japan

ISBN: (纸本)9783031299261

Deep learning is a vital technology in our lives today. Both the size of training datasets and neural networks are growing to tackle more challenging problems with deep learning. distributed deep neural network (DDNN) training technique is necessary to train a model with large datasets and networks. For large-scale DDNN training, HPC clusters are excellent computation environments. I/O performance is critical in large-scale DDNN on HPC clusters because it is becoming a bottleneck. Most flagship-class HPC clusters have hierarchical storage systems. It is necessary to quantify the performance improvement effect of the hierarchical storage system on the workloads to design future HPC storage systems. This study demonstrates the quantitative performance analysis of the hierarchical storage system for DDNN workload in a flagship-class supercomputer. Our analysis shows how much performance improvement and storage volume increment will be required to achieve the performance goal. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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BOBBLE: A Mixed Routing-Granularity distributed Load Balancing for Data Center Networks 23

BOBBLE: A Mixed Routing-Granularity Distributed Load Balanci...

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作者： Xu, Zhengzhi Lu, Yifei Ma, Xu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210096 China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi214215 China

ISBN: (纸本)9781665494571

In current Datacenter Networks (DCNs), Equal-Cost Multipath (ECMP) is a default load balancing scheme. However, using ECMP may result in rapid growth of Flow Completion Time (FCT) due to its well-known drawbacks. In order to solve the problem of ECMP, load balancing schemes with per-packet granularity (such as RPS) and per-flowlet granularity (such as LetFlow) are proposed. These two different granularities have their own features, but there's no existing load balancing considering both their advantages. In this paper, we mix per-flowlet and per-packet in scheduling decisions and introduce the potential performance bottleneck in mixed granularities load balancing. Based on this idea, we propose a hybrid scheme for load balancing, referred to as BOBBLE. The key idea of BOBBLE is to distinguish elephant and mouse flows and schedule them with different granularity. We evaluate BOBBLE in simulations with NS-3. Experiment shows that BOBBLE slightly sacrifices elephant flows FCT but it is worthy because our results demonstrate that BOBBLE is a good trade-off which outperforms RPS and LetFlow in mouse flows FCT in both symmetric and asymmetric topology. © 2021 IEEE.

关键词： Data centers Network topology Smart cities Load management Routing Mice Computer networks

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Message from the conference Chair

Proceedings - 2022 23rd ACIS International Summer Virtual Co...

引用

Proceedings - 2022 23rd ACIS international Summer Virtual conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed computing, SNPD-Summer 2022 2022年 XI页

作者： Hirata, Hiroaki Kyoto Institute of Technology Japan

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The Impact of Importance-Aware Dataset Partitioning on Data-Parallel Training of Deep Neural Networks 23rd

The Impact of Importance-Aware Dataset Partitioning on Data-...

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23rd IFIP WG 6.1 international conference on distributed Applications and Interoperable Systems (DAIS) Held as Part of the 18th international Federated conference on distributed computing Techniques (DisCoTec)

作者： Sheikholeslami, Sina Payberah, Amir H. Wang, Tianze Dowling, Jim Vlassov, Vladimir KTH Royal Inst Technol Stockholm Sweden Hopsworks AB Stockholm Sweden

ISBN: (纸本)9783031352591;9783031352607

Deep neural networks used for computer vision tasks are typically trained on datasets consisting of thousands of images, called examples. Recent studies have shown that examples in a dataset are not of equal importance for model training and can be categorized based on quantifiable measures reflecting a notion of "hardness" or "importance". In this work, we conduct an empirical study of the impact of importance-aware partitioning of the dataset examples across workers on the performance of data-parallel training of deep neural networks. Our experiments with CIFAR-10 and CIFAR-100 image datasets show that data-parallel training with importance-aware partitioning can perform better than vanilla data-parallel training, which is oblivious to the importance of examples. More specifically, the proper choice of the importance measure, partitioning heuristic, and the number of intervals for dataset repartitioning can improve the best accuracy of the model trained for a fixed number of epochs. We conclude that the parameters related to importance-aware data-parallel training, including the importance measure, number of warmup training epochs, and others defined in the paper, may be considered as hyperparameters of data-parallel model training.

关键词： Data-parallel training Example importance distributed deep learning

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