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Stab-FD: A cooperative and adaptive failure detector for wide area networks

JOURNAL OF parallel AND distributed COMPUTING 2024年 186卷

作者： Sens, Pierre Arantes, Luciana Rossetto, Anubis Graciela De Moraes Marin, Olivier Sorbonne Univ CNRS LIP6 4 Pl Jussieu F-75005 Paris France Fed Inst Sul rio grandense IFSUL Passo Fundo Brazil NYU Shanghai Comp Sci Shanghai Peoples R China

Failure detectors (FDs) are a fundamental abstraction that plays a central role in the design of distributed systems. FDs are distributed oracles that provide processes with unreliable information about process failures, often in the form of a list of trusted or suspected process identities. In this article, we propose a timer-based FD which assesses the quality of its input links, and exchanges its local estimations with other nodes. Nodes use this information to adjust their timers dynamically. Capturing the variations in the quality of each link reduces the number of false suspicions without degrading failure detection time. We present experiments on a dataset of real traces collected on PlanetLab, and compare our approach to well-known state-of-the-art algorithms. Our results show that our new algorithms yield a good trade-off in terms of failure detection speed and accuracy in real scenarios.

关键词： Failure detectors Quality of service Fault tolerance distributed algorithms Reliability

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Anomaly Detection in Industrial Networks using distributed Observation of Statistical Behavior

Anomaly Detection in Industrial Networks using Distributed O...

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workshop on Metrology for Industry 4.0 and IoT

作者： Paolo Ferrari Paolo Bellagente Alessandra Flammini Massimiliano Gaffurini Stefano Rinaldi Emiliano Sisinni Dennis Brandao Department of Information Engineering University of Brescia Brescia Italy

ISBN: (数字)9798350385823

ISBN: (纸本)9798350385830

Industrial network infrastructures are often exposed to possible issues related to accidental anomalous traffic or intentional cyber-attacks. Alteration and disruption of the real-time characteristics of the network may lead to loss of production or, worse, to danger situations. The prompt identification and mitigation of anomalies on the network traffic by means of early detection and classification is of main importance. However, cost and topology constrains of industrial network do not easy the deployment of devices for inspection of live traffic. Moreover, complex anomaly detection systems require personnel skills that are not common in industrial facilities. This paper presents an expert system relying only the collection of statistical data obtained in from industrial devices with standard protocol SNMP (Simple Network Management Protocol), realizing a de-facto distributed measurement system. The solution does not require addition hardware or excessive computation power. The proposed approach includes the definition of suitable performance indicators and the related clustering/classification methods. The use case of a real assembly machine is taken into account for the feasibility demonstration. With sampling time of ten minutes and after an initial training phase, the proposed system is able to highlight small modification of traffic behavior and guide the operator to identify the sources of the problem. Finally, current limitations and future works are discussed.

关键词： Training Protocols Automation Scalability distributed databases real-time systems Expert systems

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Proceedings of the 8th workshop on Principles and Practice of Consistency for distributed Data, PaPoC 2021

Proceedings of the 8th Workshop on Principles and Practice o...

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8th workshop on Principles and Practice of Consistency for distributed Data, PaPoC 2021

ISBN: (纸本)9781450383387

The proceedings contain 10 papers. The topics discussed include: read-write quorum systems made practical;Cambria: schema evolution in distributed systems with edit lenses;towards the synthesis of coherence/replication protocols from consistency models via real-time orderings;SCEW: programmable BFT-consensus with smart contracts for client-centric P2P web applications;totally-ordered prefix parallel snapshot isolation;convergent causal consistency for social media posts;improving the reactivity of pure operation-based CRDTs;advanced domain-driven design for consistency in distributed data-intensive systems;and access control conflict resolution in distributed file systems using CRDTs.

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Empowering Health Insurance with Personalization: A Comprehensive Architecture Leveraging Federated Learning and BlockChain

Empowering Health Insurance with Personalization: A Comprehe...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Jain, Anant Gupta, Gauranshi Johari, Rahul Vidyarthi, Deo Prakash Guru Gobind Singh Indraprastha University Delhi India School of Computer and Systems Sciences Parallel and Distributed System Lab JNU Delhi India

ISBN: (纸本)9789819795062

Historically, the health insurance domain has adhered to uniform premium pricing structures, often sidelining the distinct health nuances of each policyholder. Such broad-brush approaches can result in misaligned premiums, not truly reflective of individual health dynamics. Venturing into uncharted territories, this study proposes an innovative fusion of Federated Machine Learning (FML) and BlockChain methodologies to recalibrate health insurance pricing. FML, a cutting-edge evolution in the machine learning sphere, emphasizes decentralized data processing. By harnessing FML, timely insights can be gathered that mirror an individual’s health trajectory. This continuous insight generation facilitates agile adjustments to insurance premiums, ensuring they resonate with real-time health conditions. BlockChain’s decentralized ledger system acts as the bedrock of this model. Each premium modification, steered by FML-derived insights, is indelibly etched onto the BlockChain. This architecture not only masters transparency but also instills a heightened sense of trust, given the unalterable nature of BlockChain entries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks: Research Directions for Security and Optimal Control 4

Digital Twin Virtualization with Machine Learning for IoT an...

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ACM workshop on Wireless Security and Machine Learning (WiseML)

作者： Jagannath, Jithin Ramezanpour, Keyvan Jagannath, Anu ANDRO Computat Solut LLC Marconi Rosenblatt AI ML Innovat Lab Rome NY 13440 USA

ISBN: (纸本)9781450392778

Digital twin (DT) technologies have emerged as a solution for realtime data-driven modeling of cyber physical systems (CPS) using the vast amount of data available by Internet of Things (IoT) networks. In this position paper, we elucidate unique characteristics and capabilities of a DT framework that enables realization of such promises as online learning of a physical environment, real-time monitoring of assets, Monte Carlo heuristic search for predictive prevention, on-policy, and off-policy reinforcement learning in real-time. We establish a conceptual layered architecture for a DT framework with decentralized implementation on cloud computing and enabled by artificial intelligence (AI) services for modeling and decision-making processes. The DT framework separates the control functions, deployed as a system of logically centralized process, from the physical devices under control, much like software-defined networking (SDN) in fifth generation (5G) wireless networks. To clarify the significance of DT in lowering the risk of development and deployment of innovative technologies on existing system, we discuss the application of implementing zero trust architecture (ZTA) as a necessary security framework in future data-driven communication networks.

关键词： 5G digital twin distributed control data-driven modeling intelligent controller real-time monitoring

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A parallel Novelty Search Metaheuristic Applied to a Wildfire Prediction System 36

A Parallel Novelty Search Metaheuristic Applied to a Wildfir...

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36th IEEE International parallel and distributed Processing Symposium (IEEE IPDPS)

作者： Strappa, Jan Caymes-Scutari, Paola Bianchini, German Consejo Nacl Invest Cient & Tecn CONICET Mendoza Argentina Univ Tecnol Nacl Fac Reg Mendoza Buenos Aires DF Argentina

ISBN: (纸本)9781665497473

Wildfires are a highly prevalent multi-causal environmental phenomenon. The impact of this phenomenon includes human losses, environmental damage and high economic costs. To mitigate these effects, several computer simulation systems have been developed in order to predict fire behavior based on a set of input parameters, also called a scenario (wind speed and direction;temperature;etc.). However, the results of a simulation usually have a high degree of error due to the uncertainty in the values of some variables, because they are not known, or because their measurement may be imprecise, erroneous, or impossible to perform in real time. Previous works have proposed the combination of multiple results in order to reduce this uncertainty. State-of-the-art methods are based on parallel optimization strategies that use a fitness function to guide the search among all possible scenarios. Although these methods have shown improvements in the quality of predictions, they have some limitations related to the algorithms used for the selection of scenarios. To overcome these limitations, in this work we propose to apply the Novelty Search paradigm, which replaces the objective function by a measure of the novelty of the solutions found, which allows the search to continuously generate solutions with behaviors that differ from one another. This approach avoids local optima and may be able to find useful solutions that would be difficult or impossible to find by other algorithms. As with existing methods, this proposal may also be adapted to other propagation models (floods, avalanches or landslides).

关键词： wildfire propagation prediction evolutionary algorithms novelty search uncertainty reduction

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Serialization-Oriented Data Layout for distributed and real-time Agent-Based Simulation

Serialization-Oriented Data Layout for Distributed and Real-...

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International workshop/Symposium on distributed Simulation and real-time Applications (DS-RT)

作者： Zhuoxiao Meng Mingyue Gao Stefano Bortoli Christoph Sommer Alois Knoll Department of Informatics Technical University of Munich Germany Intelligent Cloud Technologies Laboratory Huawei Munich Research Center Germany Faculty of Computer Science TU Dresden Germany

ISBN: (数字)9798331527211

ISBN: (纸本)9798331527228

Data transfer efficiency is a frequent bottleneck of distributed (co-)simulations and X-in-the-loop systems. One of the key reasons, particularly in Agent-Based Simulation (ABS), is related to the low serialization performance caused by non-optimal data layout in memory. To address these challenges, this paper explores the potentials of a Serialization-oriented Data Layout (SoDaLa) approach for ABS building on Data Oriented Design (DOD) principles. In this work we also introduce ABS_M, a model-based abstraction of memory access in ABS. Using this model, we evaluate the impact of SoDaLa. This is done also to promote the adoption of SoDaLa and to ease the assessment of data layout strategies prior to full implementation in an existing codebases. The results indicate that our proposed approach enhances serialization efficiency and highlights the trade-offs between serialization efficiency and simulation performance across different model specifics and hardware conditions.

关键词： Layout Memory management distributed databases Data transfer real-time systems Robustness Hardware Data models US Department of Defense Context modeling

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Sampling Policies for Near-Optimal Device Choice in parallel Simulations on CPU/GPU Platforms

Sampling Policies for Near-Optimal Device Choice in Parallel...

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International workshop/Symposium on distributed Simulation and real-time Applications (DS-RT)

作者： Philipp Andelfinger Alessandro Pellegrini Romolo Marotta University of Rostock Germany Tor Vergata University of Rome Italy

ISBN: (数字)9798331527211

ISBN: (纸本)9798331527228

Heterogeneous hardware platforms comprised of CPUs, GPUs, and other accelerators offer the opportunity to choose the best-suited device for executing a given scientific simulation in order to minimize execution time and energy consumption. To this end, the recently proposed "Follow the Leader" approach dynamically selects a suitable device based on runtime performance measurements during speculative discrete-event simulations. A currently active "leader" device is periodically challenged by a "follower" device in order to negotiate the new leader. The optimality of the device choices and the associated overhead depends critically on the challenge frequency and timing. Here, we explore policies to schedule challenges with the goal of attaining Pareto-optimal combinations of execution time and energy consumption. Several heuristics are first evaluated in an abstract fashion using a "meta-simulation" by mimicking the progress and energy consumption of an idealized co-execution. In this setting, we optimize the heuristics’ tuning parameters to assess their relative merits in near-optimal configurations when compared to challenge timings based on perfect knowledge. We find that under challenging stochastic workloads based on a class of mean-reverting random walks, the best heuristics can closely approximate the execution time and energy consumption achievable under an optimal device choice. Empirical support for this observation is given by measurements of a CPU/GPU co-execution of the time Warp algorithm on physical hardware.

关键词： Energy consumption Schedules Runtime Approximation algorithms Hardware Robustness real-time systems Timing Object recognition Tuning

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One-Shot distributed Node-Specific Signal Estimation with Non-Overlapping Latent Subspaces in Acoustic Sensor Networks

One-Shot Distributed Node-Specific Signal Estimation with No...

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International workshop on Acoustic Signal Enhancement (IWAENC)

作者： Paul Didier Pourya Behmandpoor Toon van Waterschoot Marc Moonen Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Belgium

ISBN: (数字)9798350361858

ISBN: (纸本)9798350361865

A one-shot algorithm called iterationless DANSE (iDANSE) is introduced to perform distributed adaptive node-specific signal estimation (DANSE) in a fully connected wireless acoustic sensor network (WASN) deployed in an environment with non-overlapping latent signal subspaces. The iDANSE algorithm matches the performance of a centralized algorithm in a single processing cycle while devices exchange fused versions of their multichannel local microphone signals. Key iterationless nature, which favors deployment in real-time applications, and the fact that devices can exchange fewer fused signals than the number of latent sources in the environment. The proposed method is validated in numerical simulations including a speech enhancement scenario.

关键词： Performance evaluation Wireless communication Wireless sensor networks Conferences Estimation Speech enhancement Acoustic sensors Numerical simulation real-time systems Microphones

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RESEARCH ON THE APPLICATION OF COLLABORATIVE CO-SIGNING INTELLIGENT VERIFICATION MODELS AND parallel distributed ALGORITHMS FOR SEAMLESS INTEGRATION OF INTERNAL AND EXTERNAL DOCUMENTS

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Scalable Computing 2024年第4期25卷 2711-2720页

作者： HU, XIGUO ZHENG, TAO HOU, DONGLIANG KANG, YUN School of Economics Zhongnan University of Economics and Law Hubei Wuhan430000 China School of computer science and technology Harbin Institute of Technology Heilongjiang Haerbin150000 China School of Economics Xiamen University Fujian Xiamen361000 China College of Modern Economics & Management Jiangxi University of Finance and Economics Jiangxi Jiujiang332000 China

In today’s complex and linked technology environments, attaining business efficiency and innovation is crucial. In this paper, an innovative architecture combining parallel distributed algorithms and collaboratively co-signing intelligent verification frameworks is introduced. By promoting safe and effective interaction among heterogeneous systems, this synergistic method seeks to improve the integration process. Diverse systems are proliferating in both internal and external domains, requiring sophisticated integration solutions. real-time data interchange, interoperability, and security are common issues with existing methods. By using parallel distributed algorithms and intelligent verification models, the suggested framework aims to overcome these difficulties. The framework presents a novel approach to confirming the integrity and validity of data transferred between systems by utilizing collaborative co-signing. Co-signing increases security and confidence in the integrated environment by having several parties jointly validate the information. Through the introduction of parallel distributed algorithms and collaborative co-signing smart verification models, this research advances a comprehensive strategy for smooth integration of systems. The results highlight how the framework can facilitate safe and effective data interchange between external and internal systems, opening the door for more developments in connected technology environments in the future. © (2024), SCPE.

关键词： Integration

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