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Dataflow Model-based Software Synthesis Framework for parallel and distributed Embedded systems

ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC systems 2021年第5期26卷 1–38页

作者： Jeong, Eunjin Jeong, Dowhan Ha, Soonhoi Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea

Existing software development methodologies mostly assume that an application runs on a single device without concern about the non-functional requirements of an embedded system such as latency and resource consumption. Besides, embedded software is usually developed after the hardware platform is determined, since a non-negligible portion of the code depends on the hardware platform. In this article, we present a novel model-based software synthesis framework for parallel and distributed embedded systems. An application is specified as a set of tasks with the given rules for execution and communication. Having such rules enables us to perform static analysis to check some software errors at compile-time to reduce the verification difficulty. Platform-specific programs are synthesized automatically after the mapping of tasks onto processing elements is determined. The proposed framework is expandable to support new hardware platforms easily. The proposed communication code synthesis method is extensible and flexible to support various communication methods between devices. In addition, the fault-tolerant feature can be added by modifying the task graph automatically according to the selected fault-tolerance configurations by the user. The viability of the proposed software development methodology is evaluated with a real-life surveillance application that runs on six processing elements.

关键词： Code generation fault tolerance synchronous dataflow embedded soft-ware development

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Framework for IoT ecosystems based on distributed ledger technologies and decentralized identifiers

Framework for IoT ecosystems based on distributed ledger tec...

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IEEE International workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0 and IoT)

作者： Pescetelli, Giordano Petrosino, Lorenzo Della Valle, Stefano Ronga, Giulia Merone, Mario Vollero, Luca Univ Campus Biomed Roma Unit Comp Syst & Bioinformat Rome Italy Teleconsys SpA Competence Ctr IOTA Rome Italy Teleconsys SpA Engn Rome Italy

ISBN: (纸本)9781665410939

Blockchains are the first distributed Ledger Technologies (DLT), technologies based on decentralized networks of devices which share data on a distributed ledger. They possess qualities such as resilience, reliability, and traceability of sensitive data. The low scalability and frequent inefficiency of Blockchain result in ineffective integration with Internet of Things (IoT) systems. Other DLTs such as DAGs (Directed Acyclic Graph DLTs) possess benefits comparable to those of Blockchains without presenting most of the limitations that prevent their application in the IoT domain. In particular, the objective of this work was to design a framework for IoT systems based on DLT with focus on security, reliability, traceability, and decentralization. The benefit obtained in terms of security, scalability, traceability, and resilience is bound to the architecture utilized. This makes our solution usable for most IoT applications that do not need to process external data in real-time. Nevertheless, a hybrid edge-DLT architecture would be sufficient to extend the system capabilities to any IoT application.

关键词： IoT Blockchain DLT Directed Acyclic Graph security Decentralized Identifiers

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Unsupervised Multiview distributed Hashing for Large-Scale Retrieval

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IEEE TRANSACTIONS ON CIRCUITS AND systems FOR VIDEO TECHNOLOGY 2022年第12期32卷 8837-8848页

作者： Shen, Xiaobo Tang, Yunpeng Zheng, Yuhui Yuan, Yun-Hao Sun, Quan-Sen Nanjing Univ Sci & Technol Sch Comp & Engn Nanjing 210094 Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Yangzhou Univ Dept Comp Sci & Technol Yangzhou 225000 Peoples R China

Multi-view hashing (MvH) learns compact hash code by efficiently integrating multi-view data, and has achieved promising performance in large-scale retrieval task. In real-world applications, multi-view data is often stored or collected in different locations, and learning hash code in such case is more challenging yet less studied. In addition, unsupervised MvHs hardly achieve impressive retrieval performance due to absence of supervision. To fulfill this gap, this paper introduces a novel unsupervised multi-view distributed hashing (UMvDisH) to learn hash code from multi-view data, which is distributed in different nodes of a network. UMvDisH jointly performs latent factor model and spectral clustering to generate latent hash code and pseudo label respectively in each node. The consistency between hash code and pseudo label improves discrimination of hash code. The proposed distributed learning problem is divided into a set of decentralized subproblems by imposing local consistency among neighbor nodes. As such, the subproblems can be solved in parallel, and training time can be reduced. The communication cost is low due to no exchange of training data. Experimental results on four benchmark image datasets including a very large-scale image dataset show that UMvDisH achieves comparable retrieval performance and trains faster than state-of-the-art unsupervised MvHs in the distributed setting.

关键词： Codes distributed databases Costs Training Hash functions Distance learning Computer aided instruction Hashing distributed learning multi-view data Index Terms

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Probability of Delamination Detection for CFRP DCB Specimens Using Rayleigh distributed Optical Fiber Sensors 10th

Probability of Delamination Detection for CFRP DCB Specimens...

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10th European workshop on Structural Health Monitoring (EWSHM)

作者： Falcetelli, Francesco Cristiani, Demetrio Yue, Nan Sbarufatti, Claudio Di Sante, Raffaella Zarouchas, Dimitrios Univ Bologna Dept Ind Engn I-47121 Forli Italy Delft Univ Technol Fac Aerosp Engn NL-2629 HS Delft Netherlands Politecn Milan Dipartimento Meccan I-20156 Milan Italy Fac Aerosp Engn Ctr Excellence Artificial Intelligence Struct Pro NL-2629 HS Delft Netherlands

ISBN: (纸本)9783031072581;9783031072574

distributed Optical Fiber Sensors (DOFS) show several inherent benefits with respect to conventional strain-sensing technologies and represent a key technology for Structural Health Monitoring (SHM). Despite the solid motivation behind DOFS-based SHM systems, their implementation for real-time structural assessment is still unsatisfactory outside academia. One of the main reasons is the lack of rigorous methodologies for uncertainty quantification, which hinders the performance assessment of the monitoring system. The concept of Probability of Detection (POD) should function as the guiding light in this process, but precautions must be taken to apply this concept to SHM, as it has been originally developed for Non-Destructive Evaluation techniques. Although DOFS have been the object of numerous studies, a well-established methodology for their performance evaluation in terms of PODs is still missing. In the present work, the concept of Probability of Delamination Detection (POD2) is proposed for a DOFS network;Carbon Fiber-Reinforced Polymers (CFRP) Double-Cantilever Beam (DCB) specimens equipped with DOFS have been tested under static loading, and the strain patterns along with the relative observed delamination size have been collected to generate an adequate database for the POD analysis, suggesting a reference methodology to quantify the performance of DOFS for delamination detection.

关键词： Structural health monitoring Probability of detection Delamination distributed optical fiber sensors Carbon fiber-reinforced polymers

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ADSTS: Automatic distributed Storage Tuning System Using Deep Reinforcement Learning 51

ADSTS: Automatic Distributed Storage Tuning System Using Dee...

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51st International Conference on parallel Processing (ICPP)

作者： Lu, Kai Li, Guokuan Wan, Jiguang Ma, Ruixiang Zhao, Wei Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Wuhan Peoples R China SenseTime Res Shenzhen Peoples R China

ISBN: (纸本)9781450397339

Modern distributed storage systems with the immense number of configurations, unpredictable workloads and difficult performance evaluation pose higher requirements to parameter tuning. Providing an automatic parameter tuning solution for distributed storage systems is in demand. Lots of researches have attempted to build automatic tuning systems based on deep reinforcement learning (RL). However, they have several limitations in the face of these requirements, including lack of parameter spaces processing, less advanced RL models and time-consuming and unstable training process. In this paper, we present and evaluate the ADSTS, which is an automatic distributed storage tuning system based on deep reinforcement learning. A general preprocessing guideline is first proposed to generate standardized tunable parameter domain. Thereinto, Recursive Stratified Sampling without the nonincremental nature is designed to sample huge parameter spaces and Lasso regression is adopted to identify important parameters. Besides, the twin-delayed deep deterministic policy gradient method is utilized to find the optimal values of tunable parameters. Finally, Multiprocessing Training and Workload-directed Model Fine-tuning are adopted to accelerate the model convergence. ADSTS is implemented on Park and is used in the real-world system Ceph. The evaluation results show that ADSTS can recommend near-optimal configurations and improve system performance by 1.5x similar to 2.5x with acceptable overheads.

关键词： distributed Storage System Auto-tuning Reinforcement Learning Parameter Identification

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Adaptive Fragment-Based parallel State Recovery for Stream Processing systems

Adaptive Fragment-Based Parallel State Recovery for Stream P...

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作者： Xu, Hailu Liu, Pinchao Ahmed, Sarker Tanzir Da Silva, Dilma Hu, Liting California State University Department of Computer Engineering and Computer Science Long BeachCA90840 United States Florida International University School of Computing and Information Science MiamiFL33199 United States Texas AandM University College StationTX77843 United States University of California Santa Cruz Department of Computer Science and Engineering Santa CruzCA95064 United States

Today, large-scale cloud organizations are deploying datacenters and 'edge' clusters globally to provide low-latency access to services. Running stream applications across geo-distributed sites are emerging as a daily requirement. However, existing efforts have dominantly centered around stateless stream processing, leaving another urgent trend-stateful stream processing-much less explored. A driving need is to store and update states during processing, and most importantly, successfully recover large distributed states when faults and failures happen. Existing studies exhibit major limitations including: (1) they mostly inherit MapReduce's 'single master/many workers' architecture, where the central master can easily become ascalability bottleneck;(2) they offer state recovery mainly through three approaches: replication recovery, checkpointing recovery, and DStream-based lineage recovery, which are either slow, resource-expensive or failing to handle multiple failures;and (3) they are not adaptive to heterogeneous hardware settings. We present A-FP4S, a novel adaptive fragments-based parallel state recovery mechanism for stream processing systems. A-FP4S organizes stream operators into a distributed hash table based peer-to-peer overlay and divides each node's local state into many fragments. These fragments are periodically stored in node's multiple neighbors, ensuring different sets of available fragments can reconstruct failed states in parallel. This mechanism is extremely scalable to the lost state, significantly reduces failure recovery time, and can tolerate multiple node failures. A-FP4S is adaptive to heterogeneous hardware settings by automatic parameter tuning over phases. Compared to Apache Storm, A-FP4S achieves 31.8% to 50.5% reduction in recovery latency. Large-scale experiments using real-world datasets demonstrate A-FP4S's attractive scalability and adaptivity properties. © 1990-2012 IEEE.

关键词： Fault tolerance

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A distributed Approach for the Implementation of Geometric Reconstruction-Based Visual SLAM systems

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ROBOTICA 2021年第5期39卷 749-771页

作者： Ramos Neto, Otacilio de Araujo Lima Filho, Abel Cavalcante Nascimento, Tiago P. Inst Fed Educ Ciencia & Tecnol Paraiba Embedded & Distributed Comp Lab LACED Guarabira Brazil Univ Fed Paraiba Dept Mech Engn Joao Pessoa Paraiba Brazil Univ Fed Paraiba Dept Comp Syst Lab Syst Engn & Robot LaSER Joao Pessoa Paraiba Brazil

Visual simultaneous localization and mapping (VSLAM) is a relevant solution for vehicle localization and mapping environments. However, it is computationally expensive because it demands large computational effort, making it a non-real-time solution. The VSLAM systems that employ geometric reconstructions are based on the parallel processing paradigm developed in the parallel Tracking and Mapping (PTAM) algorithm. This type of system was created for processors that have exactly two cores. The various SLAM methods based on the PTAM were also not designed to scale to all the cores of modern processors nor to function as a distributed system. Therefore, we propose a modification to the pipeline for the execution of well-known VSLAM systems so that they can be scaled to all available processors during execution, thereby increasing their performance in terms of processing time. We explain the principles behind this modification via a study of the threads in the SLAM systems based on PTAM. We validate our results with experiments describing the behavior of the original ORB-SLAM system and the modified version.

关键词： Visual SLAM distributed computing Mobile robotics

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Local outlier factor for anomaly detection in HPCC systems

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JOURNAL OF parallel AND distributed COMPUTING 2024年 192卷

作者： Adesh, Arya Shobha, G. Shetty, Jyoti Xu, Lili RV Coll Engn Bengaluru India RV Univ Bengaluru India HPCC Syst LexisNexis Risk Solut Alpharetta GA USA

Local Outlier Factor (LOF) is an unsupervised anomaly detection algorithm that finds anomalies by assessing the local density of a data point relative to its neighborhood. Anomaly detection is the process of finding anomalies in datasets. Anomalies in real-time datasets may indicate critical events like bank frauds, data compromise, network threats, etc. This paper deals with the implementation of the LOF algorithm in the HPCC systems platform, which is an open-source distributed computing platform for big data analytics. Improved LOF is also proposed which efficiently detects anomalies in datasets rich in duplicates. The impact of varying hyperparameters on the performance of LOF is examined in HPCC systems. This paper examines the performance of LOF with other algorithms like COF, LoOP, and kNN over several datasets in the HPCC systems. Additionally, the efficacy of LOF is evaluated across big-data frameworks such as Spark, Hadoop, and HPCC systems, by comparing their runtime performances.

关键词： Anomaly detection Local outlier factor algorithm distributed systems HPCC systems ECL Unsupervised algorithm Big data

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Digital twin-driven identification of fault situation in distribution networks connected to distributed wind power

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY systems 2024年 155卷

作者： Yin, Yikun Chen, Houhe Meng, Xiangping Xie, Huiyang Northeast Elect Power Univ Changchun Rd 169 Jilin 132012 Peoples R China Changchun Inst Technol Kuanping Rd 395 Changchun 130012 Peoples R China Hanyang Univ Hanyangdaehak Rd 55 Ansan 15588 North Korea

The gradual integration of distributed wind power into distribution networks presents significant challenges for identifying faults, as it requires accurate and timely fault identification based on large-scale data. This paper proposes a novel fault situation identification framework driven by digital twins to overcome the existing bottleneck of asynchronous online fault identification and offline post-event analysis. The framework employs a high-precision digital twin avatar with parallel strategies of fully electromagnetic transient calculation to obtain the real-time operation status of distribution networks connected with distributed wind power. Furthermore, the framework employs skip-connected dilated causal convolution to mine meaningful multi-time-scale features from massive data, while using an automatic hyper-parameter tuning strategy. A case study based on IEEE 33node standard distribution network and a real-world distribution network demonstrates the effectiveness of this framework, achieving ultra-real-time and high-precision simulation and effective fault identification even under noise or missing data, accelerating traditional post-fault analysis to microsecond-level situation identification.

关键词： distributed wind power Distribution network Fault situation identification Digital twin End-to-end Automatic hyper-parameter tuning

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FlexScience'22: 12th workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures 31

FlexScience'22: 12th Workshop on AI and Scientific Computing...

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31st International Symposium on High-Performance parallel and distributed Computing, HPDC 2022

作者： Ferrucci, Luca Coppola, Massimo Kavalionak, Hanna Kontopoulos, Ioannis Consiglio Nazionale Delle Ricerche Pisa Italy Harokopio University of Athens Athens Greece

ISBN: (纸本)9781450391993

The 2nd International workshop on Flexible Resource and Application Management on the Edge (FRAME 2022) is dedicated to the so-called Cloud/Edge Continuum, where Cloud and Edge infrastructures can work together to fulfill requirements from a variety of NextGen applications. Clouds provide appropriate levels of performance to large groups of different users, whereas Edge resources act as a first layer of computing capacity that is closer to the user, to reduce the service latency. With respect to Clouds, Edge infrastructures typically are composed of heterogeneous and constrained resources and introduce new challenges from the viewpoint of security, orchestration and resource management. Tackling these new issues calls for innovative combinations of tools and abstractions, where AI and machine learning techniques complement algorithmic orchestration and optimization, bringing about new levels of distributed adaptivity and self-management. As real-time data-driven decisions can be promptly taken on the spot, without the need to wait for data to travel to the Cloud and back, also interactive and time-sensitive services like the immersive data processing of Extended reality (XR) applications can be partially extended toward the edge, thus exploiting a better computation to communication tradeoff and smoother connections to improve their QoE and remote collaboration. The FRAME'22 workshop proceedings are available at: https://***/***?id=3526059. © 2022 Owner/Author.

关键词： Edge computing

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