On the Design of Ethereum Data Availability Sampling: A Comprehensive Simulation Study

• URL: http://arxiv.org/abs/2407.18085v1
• Date: Thu, 25 Jul 2024 14:47:41 GMT
• Title: On the Design of Ethereum Data Availability Sampling: A Comprehensive Simulation Study
• Authors: Arunima Chaudhuri, Sudipta Basak, Csaba Kiraly, Dmitriy Ryajov, Leonardo Bautista-Gomez,
• Abstract summary: This paper presents an in-depth exploration of Data Availability Sampling (DAS) and sharding mechanisms within decentralized systems through simulation-based analysis. DAS, a pivotal concept in blockchain technology and decentralized networks, is thoroughly examined to unravel its intricacies and assess its impact on system performance. A series of experiments are conducted within the simulated environment to validate theoretical formulations and dissect the interplay of DAS parameters.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents an in-depth exploration of Data Availability Sampling (DAS) and sharding mechanisms within decentralized systems through simulation-based analysis. DAS, a pivotal concept in blockchain technology and decentralized networks, is thoroughly examined to unravel its intricacies and assess its impact on system performance. Through the development of a simulator tailored explicitly for DAS, we embark on a comprehensive investigation into the parameters that influence system behavior and efficiency. A series of experiments are conducted within the simulated environment to validate theoretical formulations and dissect the interplay of DAS parameters. This includes an exploration of approaches such as custody by row, variations in validators per node, and malicious nodes. The outcomes of these experiments furnish insights into the efficacy of DAS protocols and pave the way for the formulation of optimization strategies geared towards enhancing decentralized network performance. Moreover, the findings serve as guidelines for future research endeavors, offering a nuanced understanding of the complexities inherent in decentralized systems. This study not only contributes to the theoretical understanding of DAS but also offers practical implications for the design, implementation, and optimization of decentralized systems.

Related papers

• The Significance of Latent Data Divergence in Predicting System Degradation [1.2058600649065616]
  Condition-Based Maintenance is pivotal in enabling the early detection of potential failures in engineering systems. We introduce a novel methodology grounded in the analysis of statistical similarity within latent data from system components. We infer the similarity between systems by evaluating the divergence of these priors, offering a nuanced understanding of individual system behaviors.
  arXiv  Detail & Related papers  (2024-06-13T11:41:20Z)
• Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis [0.1558630944877332]
  This work introduces a comprehensive approach to elucidate the deposition process regimes in Chemical Vapor Deposition (CVD) reactors. Our methodology relies on process outcomes, derived by a detailed CFD model, to identify clusters of "outcomes" corresponding to distinct process regimes. This phenomenon is experimentally validated through Arrhenius plot analysis, affirming the efficacy of our approach.
  arXiv  Detail & Related papers  (2024-05-24T14:10:22Z)
• A Gap in Time: The Challenge of Processing Heterogeneous IoT Point Data in Buildings [15.06538531625261]
  The need for sustainable energy solutions has driven the integration of digitalized buildings into the power grid. incorporating IoT point data within deep-learning frameworks for energy management presents a complex challenge. This paper comprehensively analyzes the multifaceted heterogeneity present in real-world building IoT data streams.
  arXiv  Detail & Related papers  (2024-05-23T07:45:48Z)
• Multilayer Environment and Toolchain for Holistic NetwOrk Design and Analysis [2.7763199324745966]
  This work analyses in detail the requirements for distributed systems assessment. Our approach emphasizes setting up and assessing a broader spectrum of distributed systems. We demonstrate the framework's capabilities to provide valuable insights across various use cases.
  arXiv  Detail & Related papers  (2023-10-24T21:18:25Z)
• Deep Learning-based Analysis of Basins of Attraction [49.812879456944984]
  This research addresses the challenge of characterizing the complexity and unpredictability of basins within various dynamical systems. The main focus is on demonstrating the efficiency of convolutional neural networks (CNNs) in this field.
  arXiv  Detail & Related papers  (2023-09-27T15:41:12Z)
• Physics Inspired Hybrid Attention for SAR Target Recognition [61.01086031364307]
  We propose a physics inspired hybrid attention (PIHA) mechanism and the once-for-all (OFA) evaluation protocol to address the issues. PIHA leverages the high-level semantics of physical information to activate and guide the feature group aware of local semantics of target. Our method outperforms other state-of-the-art approaches in 12 test scenarios with same ASC parameters.
  arXiv  Detail & Related papers  (2023-09-27T14:39:41Z)
• Federated Deep Learning for Intrusion Detection in IoT Networks [1.3097853961043058]
  A common approach to implementing AI-based Intrusion Detection systems (IDSs) in distributed IoT systems is in a centralised manner. This approach may violate data privacy and prohibit IDS scalability. We design an experiment representative of the real world and evaluate the performance of an FL-based IDS.
  arXiv  Detail & Related papers  (2023-06-05T09:08:24Z)
• Latent Variable Representation for Reinforcement Learning [131.03944557979725]
  It remains unclear theoretically and empirically how latent variable models may facilitate learning, planning, and exploration to improve the sample efficiency of model-based reinforcement learning. We provide a representation view of the latent variable models for state-action value functions, which allows both tractable variational learning algorithm and effective implementation of the optimism/pessimism principle. In particular, we propose a computationally efficient planning algorithm with UCB exploration by incorporating kernel embeddings of latent variable models.
  arXiv  Detail & Related papers  (2022-12-17T00:26:31Z)
• Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review [62.997667081978825]
  This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
  arXiv  Detail & Related papers  (2022-04-29T08:06:05Z)
• CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
  We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE) At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-03-03T05:58:49Z)
• A Field Guide to Federated Optimization [161.3779046812383]
  Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms.
  arXiv  Detail & Related papers  (2021-07-14T18:09:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.