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Dynamic heatmap pyramid computation for massive high-parallel spatial streaming in urban environments

INTERNATIONAL JOURNAL OF DIGITAL EARTH 2024年第1期17卷

作者： Xu, Qi Xiang, Longgang Wu, Huayi Tao, Chuang Wang, Haocheng Yu, Liebing Liu, Quan Wang, Xumei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re 129 Luoyu Rd Wuhan 430079 Peoples R China Shanghai Weizhizhuoxin Informat Technol Co Ltd Shanghai Peoples R China Wuhan Univ Econ & Management Sch Wuhan Peoples R China

It is necessary to integrate data and information effectively in order to build an integrated digital and refined urban system. A multi-granularity and multi-view urban heat map formed by integrating multi-source urban information flow can assist in immediate decision-making. Managing urban big data in the form of streams requires a stable operating environment that can efficiently handle millions of sensors and devices connected to the Internet of Things (IoT) in a highly concurrent environment. Urban management data contain spatiotemporal multi-dimensional information that presents the complexity of spatiotemporal dynamic associations, further increasing the difficulty of data streaming. To overcome these challenges, we have proposed a spatiotemporal-pyramid (ST-pyramid) model that organizes multidimensional and dynamic data streams logically using a data partition strategy based on geographical grid subdivision. Accordingly, we have proposed a load-balanced heatmap pyramid computation framework that can be used to build streaming processing procedures in a distributed environment. In addition, we implemented an urban traffic heatmap prototype system based on an open-source Flink framework. The experimental results show that the real-time heatmap pyramid construction algorithm proposed in this paper has high throughput, low latency, and flexible scalability and can provide large-scale public services in time in digital earth construction.

关键词： Big data stream processing urban system distributed computing heatmap

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Measuring and Enabling Transmission Systems Resiliency With Renewable Wind Energy Systems

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第2期60卷 2321-2331页

作者： Tushar, Z. Nie, Z. Srivastava, A. Basumallik, S. Washington State Univ Pullman WA 99164 USA GE Digital Bothell WA 98011 USA West Virginia Univ Morgantown WV 26506 USA

This paper introduces a resiliency quantification approach for transmission power system, that incorporates a decentralized Remedial Action Scheme (RAS). RAS is designed to alleviate transmission line overloads under high wind conditions. A resiliency metric is computed to quantify the ability of the system to supply power to critical loads in the presence of RAS. First, a de-centralized RAS is designed to curtail excess wind optimally, in order to limit excess power flow on the transmission lines. Next, the effect of RAS operation on the transmission system is quantified using the proposed resiliency metric. The metric is based on system configuration and real-time measurements and incorporates a graph theory-based approach to quantify the redundancy and vulnerabilities of the transmission network. This makes the metric adaptive and flexible to changing grid conditions. The algorithm is fully implemented and validated on a hardware-in-the-loop testbed consisting of a Real-Time Digital Simulator (RTDS), Phasor Measurement Units (PMUs), and Cisco FOG routers with a real-time distributed computing platform for online implementation. The effectiveness of the proposed approach is validated through offline simulations on the IEEE 39 bus system and real-time simulations on an IEEE 14 bus system considering high wind and cyber attack scenarios.

关键词： Cyber attack cyber-physical systems distributed computing hardware-in-the-loop resiliency metric wind power

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DNS-embedded service endpoint registry for distributed e-Infrastructures

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第3期25卷 1645-1664页

作者： Salnikov, Andrii Konya, Balazs Taras Shevchenko Natl Univ Kyiv Comp Engn Dept 64-13 Volodymyrska Str UA-01601 Kiev Ukraine Lund Univ Box 118 S-22100 Lund Sweden

distributed e-Infrastructure is a key component of modern BIG Science. Service discovery in e-Science environments, such as Worldwide LHC computing Grid (WLCG), is a crucial functionality that relies on service registry. In this paper we re-formulate the requirements for the service endpoint registry based on our more than 10 years experience with many systems designed or used within the WLCG e-Infrastructure. To satisfy those requirements the paper proposes a novel idea to use the existing well-established Domain Name System (DNS) infrastructure together with a suitable data model as a service endpoint registry. The presented ARC Hierarchical Endpoints Registry (ARCHERY) system consists of a minimalistic data model representing services and their endpoints within e-Infrastructures, a rendering of the data model embedded into DNS-records, a lightweight software layer for DNS-record management and client-side data discovery. Our approach for the ARCHERY registry required minimal software development and inherits all the benefits of one of the most reliable distributed information discovery source of the internet, the DNS infrastructure. In particular, deployment, management and operation of ARCHERY is fully relying on DNS. Results of ARCHERY deployment use-cases are provided together with performance analysis.

关键词： e-Science distributed computing Domain Name System Information system Service endpoint registry ARCHERY Advanced resource connector WLCG

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distributed LOWER BOUNDS FOR RULING SETS\ast

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SIAM JOURNAL ON computing 2022年第1期51卷 70-115页

作者： Balliu, Alkida Brandt, Sebastian Olivetti, Dennis Gran Sasso Sci Inst I-67100 Laquila Italy Swiss Fed Inst Technol CH-8092 Zurich Switzerland

Given a graph G = (V, E), an (\alpha , \beta )-ruling set is a subset S \subseteq V such that the distance between any two vertices in S is at least \alpha , and the distance between any vertex in V and the closest vertex in S is at most \beta . We present lower bounds for distributedly computing ruling sets. More precisely, for the problem of computing a (2, \beta )-ruling set (and hence also any (\alpha , \beta )-ruling set with \alpha > 2) in the LOCAL model of distributed computing, we show the following, where n denotes the number of vertices, \Delta the maximum degree, and c is some universal constant independent of n and \Delta : (a) Any deterministic algorithm requires S2(min{ log \Delta rounds for all \beta \leq c \cdot min{ log log \Delta , log\Delta n}. By optimizing \Delta , this implies a deterministic lower bound of S2( \beta log log n ) for all \beta \leq c 3 log log n. (b) Any randomized algorithm requires \beta loglog \Delta , log\Delta log n}) rounds for all \beta \leq c \cdot min{log log \Delta , log\Delta log n}. By optimizing \Delta , \sqrt{}log log n \sqrt{}log log n this implies a randomized lower bound of S2( \beta log log log n ) for all \beta \leq c 3 log log log n. For \beta > 1, this improves on the previously best lower bound of S2(log* n) rounds that follows from the 30-yearold bounds of Linial [SIAM J. Comput., 21(1992), pp. 193--201] and Naor [SIAM J. Discrete Math., 4(1991), pp. 409--412] (resp., S2(1) rounds if \beta \in \omega (log* n)). For \beta = 1, i.e., for the problem of computing a maximal independent set (which is nothing else than a (2, 1)-ruling set), our results improve on the previously best lower bound of S2(log* n) on trees, as our bounds already hold on trees. For the maximal independent set on general graphs, a deterministic lower bound of S2(min{\Delta , log\Delta n}) and a randomized lower bound of S2(min{\Delta , log\Delta log n}) were already known due to Balliu et al. [Proceedings of FOCS, 2019, pp. 481--497].

关键词： distributed computing LOCAL model lower bounds maximal independent set ruling set round elimination

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DisCOV: distributed COVID-19 Detection on X-Ray Images With Edge-Cloud Collaboration

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IEEE TRANSACTIONS ON SERVICES computing 2022年第3期15卷 1206-1219页

作者： Xu, Xiaolong Tian, Hao Zhang, Xuyun Qi, Lianyong He, Qiang Dou, Wanchun Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Engn Res Ctr Digital Forens Minist Educ Nanjing 211544 Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Jiangsu Peoples R China Macquarie Univ Dept Comp Sydney NSW 2109 Australia Qufu Normal Univ Sch Informat Sci & Engn Rizhao 276826 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia

Currently, the world is experiencing the rapid spread of Coronavirus Disease 2019 (COVID-19). Since the epidemic continues to take a devastating impact on the society, economy, and healthcare, the real-time detection of COVID-19 is essential for fast and cost-effective diagnosis services. Fortunately, deep learning (DL), as a promising technology, enables the COVID-19 diagnosis services on chest X-ray (CXR) images. The training task of DL model is generally implemented at the centralized cloud. However, due to the geo-distributed data sources and the transmission of large amounts of raw data to the centralized cloud, the transmission latency becomes a bottleneck of the COVID-19 diagnosis model training. In this paper, we propose a distributed COVID-19 detection model training method on CXR images with edge-cloud collaboration, named DisCOV. Specifically, to improve the training efficiency and guarantee the model accuracy, a distributed lightweight model-based training algorithm is designed with the cooperation of edge computing and cloud computing. In addition, a resource allocation algorithm is developed during the training to jointly minimize the time cost and energy consumption. Extensive experiments based on real-world CXR image datasets demonstrate that DisCOV is better performed and more promising than the existing baselines.

关键词： X-ray imaging COVID-19 Computational modeling Image edge detection Data models Task analysis Collaboration Pandemica Cloud computing Edge computing distributed computing Biomedical imaging Training data COVID-19 deep learning CXR image classification edge computing edge-cloud collaboration

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BLB-gcForest: A High-Performance distributed Deep Forest With Adaptive Sub-Forest Splitting

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2022年第11期33卷 3141-3152页

作者： Chen, Zexi Wang, Ting Cai, Haibin Mondal, Subrota Kumar Sahoo, Jyoti Prakash East China Normal Univ Software Engn Inst Shanghai Key Lab Trustworthy Comp Shanghai 200050 Peoples R China Macau Univ Sci & Technol Fac Informat Technol Macau 999078 Peoples R China Siksha O Anusandhan Univ Inst Tech Educ & Res Dept Comp Sci & Informat Technol Bhubaneswar 751030 Odisha India

As an emulous alternative to deep neural networks, Deep Forest emerges with features like low complexity, fewer hyper-parameters, and good robustness, which are predominantly desired in distributed computing applications and ecosystems. Recently, an efficient distributed Deep Forest system, named ForestLayer, was proposed, designing a fine-grained sub-Forest-based task-parallel algorithm to improve the parallel computing efficiency of Deep Forest. However, the sub-Forest splitting of ForestLayer is static and one-off without adaptability to the computing environment, nevertheless, the size of splitting granularity has a significant impact on the system performance. To further improve the computing efficiency and scalability of the distributed Deep Forest, in this paper, we propose a novel distributed Deep Forest algorithm, named BLB-gcForest (Bag of Little Bootstraps-gcForest), which augments the gcForest (multi-Grained Cascade Forest) approach for constructing Deep Forest. BLB-gcForest carries out parallel computation for each tree in sub-Forests at a finer parallel granularity and integrates with the Bag of Little Bootstraps (BLB) mechanism to reduce massive transmitted feature instances for Cascade Forest Layers, utterly improving both computation efficiency and communication efficiency. Moreover, to solve the problem of the forest splitting granularity, we further design an adaptive sub-Forest splitting algorithm to ensure the maximum resource utilization for parallel computation of each sub-Forest. Experimental results on four well-known large-scale datasets, namely YEAST, LETTER, MNIST, CIFAR10, show that the training efficiency of BLB-gcForest achieves up to 20.3x and 1.64x speedups compared with the state-of-the-art gcForest and ForestLayer, respectively while guaranteeing higher accuracy and better robustness

关键词： Forestry Computational modeling Training Parallel processing distributed computing Data models Adaptation models Deep forest distributed computing big data bootstrap distributed AI

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***: An AI-based operating system for autonomous robotics and complex automation

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JOURNAL OF FIELD ROBOTICS 2024年第2期42卷 474-492页

作者： Grigorescu, Sorin Zaha, Mihai Transilvania Univ Brasov Vis & Control Lab RovisLab CyberCortex Robot & Robot Brasov Romania

The underlying framework for controlling autonomous robots and complex automation applications is Operating Systems (OS) capable of scheduling perception-and-control tasks, as well as providing real-time data communication to other robotic peers and remote cloud computers. In this paper, we introduce ***, a robotics OS designed to enable heterogeneous AI-based robotics and complex automation applications. *** is a decentralized distributed OS which enables robots to talk to each other, as well as to High Performance Computers (HPC) in the cloud. Sensory and control data from the robots is streamed toward HPC systems with the purpose of training AI algorithms, which are afterwards deployed on the robots. Each functionality of a robot (e.g., sensory data acquisition, path planning, motion control, etc.) is executed within a so-called DataBlock of Filters shared through the internet, where each filter is computed either locally on the robot itself or remotely on a different robotic system. The data is stored and accessed via a so-called Temporal Addressable Memory (TAM), which acts as a gateway between each filter's input and output. *** has two main components: (i) the *** system, which is a real-time implementation of the DataBlock running on the robots' embedded hardware, and (ii) the ***, which runs on an HPC computer in the cloud, and it is used to design, train and deploy AI algorithms. We present a quantitative and qualitative performance analysis of the proposed approach using two collaborative robotics applications: (i) a forest fires prevention system based on an Unitree A1 legged robot and an Anafi Parrot 4K drone, as well as (ii) an autonomous driving system which uses *** for collaborative perception and motion control.

关键词： artificial intelligence autonomous navigation autonomous robots distributed computing embedded artificial intelligence embedded systems heterogeneous robots multi-robot systems robot autonomy robotics operating systems

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ServiceNet: resource-efficient architecture for topology discovery in large-scale multi-tenant clouds

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第7期27卷 8965-8982页

作者： Garcia, Angel Gama Calero, Jose M. Alcaraz Mora, Higinio Mora Wang, Qi Univ West Scotland High St Paisley PA1 2BE Renfrewshire Scotland Univ Alicante Carr San Vicente del Raspeig Alicante 03690 Spain

Modern computing infrastructures are evolving due to virtualisation, especially with the advent of 5G and future technologies. While this transition offers numerous benefits, it also presents challenges. Consequently, understanding these complex systems, including networks, services, and their interconnections, is crucial. This paper introduces ServiceNet, a groundbreaking architecture that accurately performs the important task of providing understanding of a multi-tenant architecture by discovering the complete topology, crucial in the realm of high-performance distributed computing. Experimental results have been carried out in different scenarios in order to validate our approach, demonstrating the effectiveness of our approach in comprehensive multi-tenant topology discovery. The experiments, involving up to forty tenant, highlight the adaptability of ServiceNet as a valuable tool for real-time monitoring in topology discovery purposes, even in challenging scenarios.

关键词： Topology discovery distributed computing Resource management Multi-tenancy 5G networks

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AFSD: Adaptive Feature Space Distillation for distributed Deep Learning

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IEEE ACCESS 2022年 10卷 84569-84578页

作者： Khaleghian, Salman Ullah, Habib Johnsen, Einar Broch Andersen, Anders Marinoni, Andrea UiT Arctic Univ Norway Fac Sci & Technol N-9019 Tromso Norway Norwegian Univ Life Sci NMBU Fac Sci & Technol N-1430 As Norway Univ Oslo Dept Informat N-0315 Oslo Norway

We propose a novel and adaptive feature space distillation method (AFSD) to reduce the communication overhead among distributed computers. The proposed method improves the Codistillation process by supporting longer update interval rates. AFSD performs knowledge distillates across the models infrequently and provides flexibility to the models in terms of exploring diverse variations in the training process. We perform knowledge distillation in terms of sharing the feature space instead of output only. Therefore, we also propose a new loss function for the Codistillation technique in AFSD. Using the feature space leads to more efficient knowledge transfer between models with a longer update interval rates. In our method, the models can achieve the same accuracy as Allreduce and Codistillation with fewer epochs.

关键词： Convolutional neural networks Feature extraction Computational modeling Deep learning Adaptation models Data models Concurrent computing Knowledge management distributed computing distributed deep learning convolutional neural networks knowledge distillation codistillation

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On implementing SWMR registers from SWSR registers in systems with Byzantine failures

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distributed computing 2024年第2期37卷 145-175页

作者： Hu, Xing Toueg, Sam Univ Toronto Dept Comp Sci Toronto ON Canada

The implementation of registers from (potentially) weaker registers is a classical problem in the theory of distributed computing. Since Lamport's pioneering work (Lamport in Distrib Comput 1(2):77-101, 1986), this problem has been extensively studied in the context of asynchronous processes with crash failures. In this paper, we investigate this problem in the context of Byzantine process failures, with and without process signatures. We first prove that, without signatures, there is no wait-free linearizable implementation of a 1-writer n-reader register from atomic 1-writer 1-reader registers. In fact, we show a stronger result, namely, even under the assumption that the writer can only crash and at most one reader can be malicious, there is no linearizable implementation of a 1-writer n-reader register from atomic 1-writer (n - 1)-reader registers that ensures that every correct process eventually completes its operations. In light of this impossibility result, we give two implementations of a 1-writer n-reader register from atomic 1-writer 1-reader registers that work under different assumptions. The first implementation is linearizable (under any combination of Byzantine process failures), but it guarantees that every correct process eventually completes its operations only under the assumption that the writer is correct or no reader is Byzantine-thus matching the impossibility result. The second implementation assumes process signatures;it is wait-free and linearizable under any number and combination of Byzantine process failures.

关键词： distributed computing Concurrency Linearizability Shared registers

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