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6th IFAC workshop on Advanced Maintenance Engineering, Services and Technology (AMEST)

作者： Vukolov, Andrey van Winkle, Erik Tykhonov, Vyacheslav Pugliese, Roberto Elettra Sincrotrone Trieste Str Statale 14163-5kmAREA Sci Pk I-34149 Trieste Italy DeSci Fdn Rue Rhone 65POB 3199 CH-1204 Geneva Switzerland Data Archiving & Networked Serv DANS Anna van Saksenlaan 51 NL-2593 HW The Hague Netherlands

In the realm of industrial automation and control, the advent of Decentralized Persistent Identifiers (dPID) presents a novel paradigm for enhancing data identification and management. Traditional centralized and federated identification systems often struggle with scalability, resilience, and interoperability challenges, particularly in complex, distributed manufacturing environments. This paper explores the potential of dPID technology to address these issues by aligning with the FAIR principles-findability, accessibility, interoperability, and reusability. We introduce dPID as a transformative solution for the persistent identification, versioning, storage, and management of assets within modern industrial settings. Through dPID, assets such as machines and equipment gain unique, immutable identifiers, facilitating improved tracking, maintenance, and operational efficiency. We present scenarios demonstrating dPID's application in asset management, from real-time tracking and preventive maintenance to enhancing supply chain transparency and enabling seamless interoperability across diverse systems and platforms. By leveraging decentralized technologies, dPID offers a promising approach to achieving a more resilient, efficient, and transparent industrial ecosystem. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Remote and distributed control decentralized technologies tele-maintenance telecommunication-based automation systems

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I/O Latency Management in Private Cloud Infrastructures 25th

I/O Latency Management in Private Cloud Infrastructures

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25th International Conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2024

作者： HoseinyFarahabady, MohammadReza Zomaya, Albert Y. School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW Australia

ISBN: (纸本)9789819642069

This paper examines how co-locating multiple VMs on a single physical server with shared storage impacts I/O performance, specifically focusing on latency of I/O operations, and the overall throughput. We introduce a Proportional-Integral-Derivative (PID) controller to maintain I/O latency close to a predefined target by dynamically adjusting I/O requests based on real-time measurements. Through a series of experiments, we evaluated the effectiveness of our PID controller in comparison to VMware’s default QoS mechanism within a private cloud environment. The results indicate that the PID controller significantly enhances performance for latency-critical workloads, achieving substantially higher compliance with latency targets compared to the default mechanism. However, this prioritization of high-priority VMs comes at the expense of increased latency and reduced throughput for lower-priority workloads during periods of I/O contention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Three term control systems

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A Reliability-Based Task Scheduling Method for Wireless distributed systems 23rd

A Reliability-Based Task Scheduling Method for Wireless Dist...

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23rd International Conference on Computational Science and its Applications (ICCSA)

作者： Bodkhe, Sonali Mahajan, Anjali Sant Gadge Baba Amravati Univ Dept Comp Sci Amravati Maharashtra India Govt Polytech Nagpur Maharashtra India

ISBN: (纸本)9783031368042;9783031368059

parallel computing and distributed computing are the popular terminologies of scheduling. With advancement in technology, systems have become much more compact and fast and need of parallelization plays a major role for this compaction. Wireless computing is also a common concept associated with each new development. Scheduling of tasks has always been a challenging area and is an NP-complete problem. Moreover, when it comes to wireless distributed computing, reliable scheduling plays an important role in order to complete a task in a wireless distributed system. This work proposes an algorithm to dynamically schedule tasks on heterogeneous processors within a wireless distributed computing system. A lot of heuristics, meta-heuristics & genetics have been used earlier with scheduling strategies. However, most of them haven't taken reliability into account before scheduling. Here a heuristic that deals with reliable scheduling is considered. The scheduler also works within an environment which has dynamically changing resources and adapts itself to changing system resources. The testing was carried out with up to 200 tasks being scheduled while testing in a real time wireless distributed environment. Experiments have shown that the algorithm outperforms the other strategies and can achieve a better reliability along with no increase in make-span, in spite of wireless nodes.

关键词： distributed Computing Scheduling Reliability Wireless distributed systems (WDS)

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A real-time and Robust Multivariate Estimator for Dynamic systems With Heavy-Tailed Additive Uncertainties

A Real-Time and Robust Multivariate Estimator for Dynamic Sy...

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作者： Snyder, Nathaniel Jeffrey University of California Los Angeles

学位级别：Ph.D., Doctor of Philosophy

In this dissertation, a real-time, analytic and recursive multivariate state-estimation algorithm is developed for time-invariant, time-varying and nonlinear dynamical systems. Unlike Gaussian based state-estimation algorithms, the proposed state-estimation algorithm uses Cauchy random variables to model the uncertainties in the process and measurement functions. For this reason, it is referred to as the multivariate Cauchy estimator (MCE). The MCE uses a characteristic function representation of the conditional probability density function of the system state vector, given the measurement history, which generates the conditional mean and covariance estimates of the system state vector at each estimation step. The characteristic function of the MCE is enhanced in this dissertation from its previous form by an innovative, computationally tractable, and reduced structure. In particular, the backward recursive, or tree-like, evaluation procedure of the previously-used characteristic function is replaced by a linear parameterization. This linear parameterization compresses the backward recursive characteristic function at each estimation step and allows similar terms of the characteristic function to now be combined together, which was previously not possible. Compressing the characteristic function is shown to lead to the elimination of over 99% of terms that previously comprised it after several estimation steps, although the number of terms after a measurement update still grows. Therefore, a method is developed to run the MCE for arbitrary simulation lengths and for the multivariate setting, despite the growing size of the characteristic function. Furthermore, the estimation structure of the MCE is extended to handle nonlinearities in both the system dynamics and the measurement model, in a fashion similar to that of the extended Kalman filter. It is then shown that the MCE algorithm can achieve real-time computational performance by exploiting the parallel structur

关键词： distributed computation Heavy tailed densities Kalman filtering Multivariate dynamic systems State estimation

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Performance optimization of Learning Trees modules in the ECL repository 3

Performance optimization of Learning Trees modules in the EC...

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3rd International Conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Shashank, B. Sudershan, K.S. Shobha, G. Manonmani, S. Dev, Roger Rv College of Engineering Dept. of Computer Science Bangalore India LexisNexis Risk Solutions United States

ISBN: (纸本)9798331528201

Multi-Node computation, also known as distributed computing, is a paradigm that allows for the efficient utilization of multiple interconnected nodes or machines to perform complex computational tasks. By dividing the workload and distributing it across multiple nodes, multi-node computation enables parallel processing, scalability, and improved performance for a wide range of applications. HPCC systems® from LexisNexis Risk Solutions is a proven, open source solution for big data insights that can be implemented by businesses of all sizes. HPCC systems offers a consistent data-centric programming language, two processing platforms and a single, complete end-to-end architecture for efficient processing. ECL is the Enterprise Control Language designed specifically for huge data projects using the HPCC systems platform. It is extremely scalable and a powerful declarative data-processing language with native multi-processing capabilities and used for HPCC systems. Some core ECL functions, like the ones handling Learning Tree algorithms of Machine Learning Bundle are recursive in nature and hence high computational time is needed. This paper thus presents a solution to achieve optimized processing of ML learning tree algorithms in ECL through embedding python libraries. The proposed approach was tested on standard datasets and a significant decrease in computation time. For real Estate Valuation Regression dataset the computation time was reduced from 11.371 seconds to 0.886 seconds using the proposed method showing 1324% improvement with no impact on the accuracy. Random Forests are also implemented using the proposed Decision Tree method allowing the users to specify the number of decision trees to be used in the forest. © 2024 IEEE.

关键词： parallel processing systems

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HyperGAC: Information Entropy-Driven Resource Allocation for Cloud-Edge-End Computing

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JOURNAL OF GRID COMPUTING 2025年第2期23卷 1-23页

作者： Chen, Juan Bai, Lin Chen, Ningjiang Yang, Yubin Guangxi Univ Sch Comp & Elect & Informat Nanning 530004 Peoples R China Guangxi Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Parallel Distributed & Intelligent Comp Nanning 530004 Peoples R China Guangxi Ctr Technol Innovat Intelligent Digital Se Nanning 530004 Peoples R China

Cloud-Edge-End computing provides favorable support for emerging applications by virtue of its low-latency and high reliability. Resource allocation strategies significantly impact the quality of service and response time of cloud-edge-end systems. Due to the decentralized storage of resources, large fluctuations in task demands, and the complexity of cloud-edge-end environments, achieving effective resource allocation remains challenging. Existing resource allocation strategies rely on static models or predefined rules, and cannot flexibly respond to real-time network conditions and changes in task requirements. Consequently, they are limited in achieving optimal resource utilization and effective load balancing. Therefore, this paper proposes an Information Entropy (IE)-driven cloud-edge-end resource allocation mechanism called HyperGAC. First, IE is employed to quantify resource fluctuations, so as to accurately capture the dynamic changes in resource demands. And an Entropy-based Correlation Recurrent Unit (ECRU) is designed to predict server resource utilization. Second, the hypergraph is used to model the complex relationships between resources and tasks, breaking through the limitations of traditional graph structures in multi-dimensional relationships modeling. Finally, the Hypergraph Neural Network (HGNN) is integrated into the actor-critic framework to extract the features from the hypergraph association model, while dynamically optimizing resource allocation strategy based on the prediction results. Experimental results demonstrate that the HyperGAC mechanism significantly improves resource utilization and load balancing performance while effectively reducing the task rejection rate.

关键词： Cloud-edge-end computing Resource allocation Information entropy Resource utilization prediction Deep reinforcement learning

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timed Loops for distributed Storage in Wireless Networks

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IEEE TRANSACTIONS ON parallel AND distributed systems 2022年第3期33卷 698-709页

作者： Mukherjee, Anandarup Deb, Pallav Kumar Misra, Sudip Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur 721302 W Bengal India

IoT deployments that have limited memories lack sustained computation power and have limited connectivity to the Internet due to intermittent last-mile connectivity, particularly in rural and remote locations. For maintaining congestion-free operations, most of the collected data from these networks are discarded, instead of being transmitted remotely for further processing. In this article, we propose the paradigm timed Loop Storage to distribute the data and use the underutilized bandwidth of local network links for sequentially queuing packets of computational data that are being operated on in parts in one of the IoT nodes. While the sequenced packets are executed sequentially on the target IoT device, the remaining packets, which are currently not being operated on, distribute and keep looping over the network links until they are required for processing. A time-synchronized packet deflection mechanism on each node handles data transfer and looping of individual packets. In our implementation, although we observe that the proposed approach requires data rates of 6 Mbps, it incurs only 45 Kb usage of primary storage systems even for sizeable data, ensuring scalability of the connected IoT devices' temporary storage capabilities, thereby making it useful for real-life applications.

关键词： Internet of Things Delays Task analysis Servers Performance evaluation Hardware Computer architecture Wireless networks Internet of Things resource allocation connectivity distributed storage networks

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Proceedings of the 6th ACM SIGMOD Joint International workshop on Graph Data Management Experiences and systems (GRADES) and Network Data Analytics (NDA) 2023, GRADES-NDA 2023

Proceedings of the 6th ACM SIGMOD Joint International Worksh...

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6th ACM SIGMOD Joint International workshop on Graph Data Management Experiences and systems and Network Data Analytics, GRADES-NDA 2023

ISBN: (纸本)9798400702013

The proceedings contain 7 papers. The topics discussed include: the commercial side of graph analytics: big uses, big mistakes, big opportunities;graph feature management: impact, challenges and opportunities;better distributed graph query planning with scouting queries;EAGER: explainable question answering using knowledge graphs;going with the flow: real-time max-flow on asynchronous dynamic graphs;future-time temporal path queries;fast synthetic data-aware log generation for temporal declarative models;learning graph neural networks using exact compression;and a demonstration of interpretability methods for graph neural networks.

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DEBS 2023 - Proceedings of the 17th ACM International Conference on distributed and Event-based systems

DEBS 2023 - Proceedings of the 17th ACM International Confer...

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17th ACM International Conference on distributed and Event-based systems, DEBS 2023

ISBN: (纸本)9798400701221

The proceedings contain 26 papers. The topics discussed include: a selective and biased choice of techniques for building a distributed data store;accelerating the performance of distributed stream processing systems with in-network computing;secure distributed data and event processing at scale: where are we now?;adaptive distributed streaming similarity joins;I will survive: an event-driven conformance checking approach over process streams;on improving streaming system Autoscaler behavior using windowing and weighting methods;practical forecasting of cryptocoins timeseries using correlation patterns;an exploratory analysis of methods for real-time data deduplication in streaming processes;considerations for integrating virtual threads in a Java framework: a Quarkus example in a resource-constrained environment;and discovery of breakout patterns in financial tick data via parallel stream processing with in-order guarantees.

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Safeguarding Sensitive Data in the Era of IoT: A Study on Security Protocols for distributed Measurement systems

Safeguarding Sensitive Data in the Era of IoT: A Study on Se...

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2024 IEEE International workshop on Metrology for Living Environment, MetroLivEnv 2024

作者： Gentile, Antonio Francesco Macri, Davide Lamonaca, Francesco Rende Italy Rende Italy

ISBN: (纸本)9798350385014

IoT devices have led to the development of distributed Measurement systems (DMS). However, cyber-attacks have increased, making it crucial to implement security protocols without reducing network throughput. Open firmware on constrained devices offers another layer of customization, essential for the flexibility required in DMS. This allows software updates and modifications in real-time, which is crucial for adapting to evolving cybersecurity threats and managing device functionality. The objective is to identify algorithms that ensure optimal data transmission and encryption ratios and maximum compatibility with existing infrastructures and commercial hardware that support MQTT technology. This approach utilizes local MQTT brokers and TLS tunnels to secure local sensor data transmissions. This design enhances security and maintains the efficiency of data flow across the network. The primary objective moving forward is to refine and identify algorithms that ensure optimal data transmission and encryption ratios and uphold maximum compatibility with existing infrastructures that support MQTT technology. This is critical as MQTT is a standard protocol for IoT communications. © 2024 IEEE.

关键词： Data transfer

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