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4th workshop on distributed Ledger Technology, DLT 2022

作者： Di Pilla, Piergiuseppe Pareschi, Remo Salzano, Francesco Zappone, Federico Stake Lab University of Molise Campobasso Italy BB-Smile Srl Rome Italy

distributed systems have become ubiquitous in recent years, with those based on distributed ledger technology (DLT), such as blockchains, gaining more and more weight. Indeed, DLT ensures strong data integrity thanks to complex cryptographic protocols and high distribution. That said, even the most powerful systems will never be perfect, and, in fact, the larger they get, the more exposed they become to threats. For traditional systems, log auditing effectively addresses the problem and makes it possible to analyze the use of applications. However, DLT systems still lack a wide range of log analyzers due to the particularities of their distribution. To help remedy this weakness, we propose here a generic auditing system called DELTA (for distributed Elastic Log Text Analyzer). By coupling Natural Language Processing with the Docker Engine of the Filebeat, Logstash stack, Elasticsearch and the visual tool Kibana, DELTA tracks, analyzes and classifies logs generated by DLT systems. Additionally, it enables real-time monitoring thanks to visual analysis and querying of structured data. DELTA is the first auditing system applicable to blockchains that can be integrated with the Docker Engine. In addition to describing its general principles and specific components, we illustrate its application to Hyperledger Fabric, the most popular of the platforms for building private blockchains. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR workshop Proceedings (***)

关键词： Blockchain

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Multi-core multi-node parallelization of the radio interferometric imaging pipeline DDFacet 36

Multi-core multi-node parallelization of the radio interfero...

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36th IEEE workshop on Signal Processing systems (SiPS)

作者： Monnier, Nicolas Guibert, David Tasse, Cyril Gac, Nicolas Orieux, Francois Raffin, Erwan Smirnov, Oleg M. Hugo, Benjamin V. Univ Paris Saclay Lab Signaux & Syst CNRS CentraleSupelec F-91190 Gif Sur Yvette France Atos Bull CEPP F-35700 Rennes France Univ Paris Diderot CNRS Observ Paris GEPI Meudon France South African Radio Astron Observ Cape Town South Africa

ISBN: (数字)9781665485241

ISBN: (纸本)9781665485241

The next generation of radio telescopes, such as the Square Kilometer Array (SKA), will need to process an incredible amount of data in real-time. In addition, the sensitivity of SKA will require a new generation of calibration and imaging software to exploit its full potential. The wide-field direction-dependent spectral deconvolution framework, called DDFacet, has been successfully used in several existing SKA pathfinders and precursors like MeerKAT and LOFAR. This imager allows a multi-core execution based on facets parallelization and a multinode execution based on observations parallelization. However, because of the amount of data to be processed, the data on a single observation will have to be distributed on several nodes. This paper proposes the first two-level parallelization of DDFacet in the case of a single observation. A multi-core parallelization based on facets and a multi-node parallelization based on frequency distribution grouped in Measurement Sets. We show that this multi-core multi-node parallelization has successfully reduced the total execution time by a factor of 5:7 on a LOFAR dataset.

关键词： HPC DDFacet parallelization distributed memory SKA

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Optimised Lambda Architecture for Monitoring Scientific Infrastructure

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IEEE TRANSACTIONS ON parallel AND distributed systems 2021年第6期32卷 1395-1408页

作者： Suthakar, Uthayanath Magnoni, Luca Smith, David Ryan Khan, Akram Brunel Univ London Dept Elect & Comp Engn Coll Engn Design & Phys Sci Uxbridge UB8 3PH Middx England CERN European Org Nucl Res CH-1208 Geneva Switzerland

Within scientific infrastructuscientists execute millions of computational jobs daily, resulting in the movement of petabytes of data over the heterogeneous infrastructure. Monitoring the computing and user activities over such a complex infrastructure is incredibly demanding. Whereas present solutions are traditionally based on a Relational Database Management System (RDBMS) for data storage and processing, recent developments evaluate the Lambda Architecture (LA). In particular these studies have evaluated data storage and batch processing for processing large-scale monitoring datasets using Hadoop and its MapReduce framework. Although LA performed better than the RDBMS following evaluation, it was fairly complex to implement and maintain. This paper presents an Optimised Lambda Architecture (OLA) using the Apache Spark ecosystem, which involves modelling an efficient way of joining batch computation and real-time computation transparently without the need to add complexity. A few models were explored: pure streaming, pure batch computation, and the combination of both batch and streaming. An evaluation of the OLA on the CERN IT on-premises Hadoop cluster and the public Amazon cloud infrastructure for the monitoring WLCG Data acTivities (WDT) use case are both presented, demonstrating how the new architecture can offer benefits by combining both batch and real-time processing to compensate for batch-processing latency.

关键词： Computer architecture Monitoring real-time systems Sparks distributed databases Computational modeling Complexity theory Big data distributed systems lambda architecture low-latency computation parallel computing

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A parallel strategy for predicting the quality of welded joints in automotive bodies based on machine learning

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JOURNAL OF MANUFACTURING systems 2022年 62卷 636-649页

作者： Chen, Geng Sheng, Buyun Luo, Ruipin Jia, Pengzhen Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China

Due to the complexity of the resistance spot welding process, it is still a challenge to accurately know the operating status of the welding robot under the current parameter settings and to assess the welding quality of electrode caps under different types of plates in real time with large data sizes. To solve this problem, this paper classifies the overall data set and proposes a parallel strategy method for predicting the quality of weld joints using machine learning for subsets of the data with different distribution patterns. Firstly, the PCA dimensionality reduction model was used to set the number of principal components to reduce the dimensionality of the welding process feature value dataset and reduce the difficulty of classifying the data subgroups, and the elbow method was used to set the number of clustering centers to complete the classification of the sub-datasets by applying the k-means model on the basis of the dimensionality reduction data. Finally, the feature parameters of each sub-dataset are used as input for machine learning, and a parallel prediction strategy for weld joint quality is developed based on the data distribution characteristics of each sub-dataset. The test results show that the model in this paper outperforms the static BP neural network in predicting the quality of all types of welded joints, the machine learning parallel strategy tailored to the characteristics of the data population works well with more complexly distributed welded big data. This paper provides accurate and effective estimation of body resistance welding condition, which can provide some guidance for online inspection of body resistance spot welding quality in automotive production lines.

关键词： Resistance spot welding Quality prediction Principal component analysis (PCA) K-means clustering algorithm Back propagation neural network (BPN) parallel strategies

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LOFS: A Lightweight Online File Storage Strategy for Effective Data Deduplication at Network Edge

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IEEE TRANSACTIONS ON parallel AND distributed systems 2022年第10期33卷 2263-2276页

作者： Cheng, Geyao Guo, Deke Luo, Lailong Xia, Junxu Gu, Siyuan Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

Edge computing responds to users' requests with low latency by storing the relevant files at the network edge. Various data deduplication technologies are currently employed at edge to eliminate redundant data chunks for space saving. However, the lookup for the global huge-volume fingerprint indexes imposed by detecting redundancies can significantly degrade the data processing performance. Besides, we envision a novel file storage strategy that realizes the following rationales simultaneously: 1) space efficiency, 2) access efficiency, and 3) load balance, while the existing methods fail to achieve them at one shot. To this end, we report LOFS, a Lightweight Online File Storage strategy, which aims at eliminating redundancies through maximizing the probability of successful data deduplication, while realizing the three design rationales simultaneously. LOFS leverages a lightweight three-layer hash mapping scheme to solve this problem with constant-time complexity. To be specific, LOFS employs the Bloom filter to generate a sketch for each file, and thereafter feeds the sketches to the Locality Sensitivity hash (LSH) such that similar files are likely to be projected nearby in LSH tablespace. At last, LOFS assigns the files to real-world edge servers with the joint consideration of the LSH load distribution and the edge server capacity. Trace-driven experiments show that LOFS closely tracks the global deduplication ratio and generates a relatively low load std compared with the comparison methods.

关键词： Servers Indexes Costs Redundancy Throughput Image edge detection Resource management Data deduplication locality sensitivity hash edge computing storage systems

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3rd workshop in distributed Computing for Emerging Smart Networks, DiCES-N 2022

3rd Workshop in Distributed Computing for Emerging Smart Net...

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3rd workshop in distributed Computing for Emerging Smart Networks, DiCES-N 2022

ISBN: (纸本)9783030990039

The proceedings contain 6 papers. The special focus in this conference is on distributed Computing for Emerging Smart Networks. The topics include: Leveraging Machine Learning for WBANs;blockchain and Cooperative Intelligent Transport systems: Challenges and Opportunities;blockchain-Based real time Healthcare Emergency;A Survey of Machine Learning Methods for DDoS Threats Detection Against SDN.

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real time OPERATION IN ELECTRIC VEHICLE SMART CHARGING STATIONS: A MATHEURISTIC APPROACH CONSIDERING DEMAND SIDE MANAGEMENT, ENERGY STORAGE AND distributed GENERATION

REAL TIME OPERATION IN ELECTRIC VEHICLE SMART CHARGING STATI...

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CIRED Porto workshop 2022: E-Mobility and Power Distribution systems

作者： Medeiros, Aérton Canha, Luciane Garcia, Vinícius Motta, Rodrigo Moreira, Juliano Dos Santos, Rodrigo Federal University of Santa Maria Brazil Parana Electricity Company COPEL-Dis. Brazil

ISBN: (数字)9781839537059

ISBN: (纸本)9781839537196

The management of distributed energy resources has gained special attention in the field of research with the aim of exploiting the capacity of these resources to the maximum, where many expect from the correct management the solution to the problems of intermittence and increase participation of renewable energy sources. This work presents a management system for electric vehicles charging stations in the presence of distributed energy resources. In this context, uncertainties related to scheduled charges and the service of unscheduled charges are met by an algorithm that seeks to carry out the operation of the charging station in real time. Furthermore, the ability of the proposed management system to meet the demand response signaling imposed by the distribution system operator is evaluated. © 2022 CIRED workshop on E-mobility and power distribution systems. All rights reserved.

关键词： Demand side management

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Trust-3DM: Trustworthiness-Based Data-Driven Decision-Making Framework Using Smart Edge Computing for Continuous Sensing

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IEEE ACCESS 2022年 10卷 133095-133108页

作者： Lamaazi, Hanane Mizouni, Rabeb Otrok, Hadi Singh, Shakti Damiani, Ernesto Khalifa Univ Sci & Technol Ctr Cyber Phys Syst Dept Elect Engn & Comp Sci Abu Dhabi U Arab Emirates

Mobile Edge Computing (MEC) has been proposed as an efficient solution for Mobile crowdsensing (MCS). It allows the parallel collection and processing of data in real time in response to a requested task. A sensing task can be one-time or continuous, with multiple readings collected over time. Integrating MEC and continuous sensing in MCS is challenging due to many factors, including workers' mobility, edge node placement, task location, Reputation, and data quality. In addition, guarantying cooperative communication in the presence of Anomalous data while maintaining a high quality of service (QoS) is a fundamental issue in continuous sensing. A stability-based edge node selection and anomaly detection-based decision-making framework for worker recruitment in continuous sensing is proposed to address these challenges. It can a) Select the most stable edge nodes in the area of interest (AoI), b) Dynamically cluster the workers according to their movement in the AoI, c) Locally detect and eliminate anomalies within the sensing data, and d) Adopt a feedback mechanism that ensures the cooperation between the edge nodes to eliminate untrustworthy workers in the whole sensing period and future tasks. A real-life dataset is used to evaluate the efficiency of the proposed framework. Results show that the framework outperforms the baselines by achieving higher QoS while introducing lower delay, energy consumption, and less resource consumption.

关键词： Smart edge computing crowdsensing distributed architecture data quality anomaly detection trustworthiness

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

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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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