Can we run our Ethereum nodes at home?
- URL: http://arxiv.org/abs/2311.05252v1
- Date: Thu, 9 Nov 2023 10:20:09 GMT
- Title: Can we run our Ethereum nodes at home?
- Authors: Mikel Cortes-Goicoechea, Tarun Mohandas-Daryanani, Jose L. Muñoz-Tapia, Leonardo Bautista-Gomez,
- Abstract summary: Scalability is a common issue among the most used permissionless blockchains.
achieved a major protocol improvement, including a change in the consensus mechanism towards Proof of Stake.
This work analyzes the resource usage behavior of different clients running as consensus nodes.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Scalability is a common issue among the most used permissionless blockchains, and several approaches have been proposed to solve this issue. Tackling scalability while preserving the security and decentralization of the network is a significant challenge. To deliver effective scaling solutions, Ethereum achieved a major protocol improvement, including a change in the consensus mechanism towards Proof of Stake. This improvement aimed a vast reduction of the hardware requirements to run a node, leading to significant sustainability benefits with a lower network energy consumption. This work analyzes the resource usage behavior of different clients running as Ethereum consensus nodes, comparing their performance under different configurations and analyzing their differences. Our results show higher requirements than claimed initially and how different clients react to network perturbations. Furthermore, we discuss the differences between the consensus clients, including their strong points and limitations.
Related papers
- Mitigating Challenges in Ethereum's Proof-of-Stake Consensus: Evaluating the Impact of EigenLayer and Lido [4.606106768645647]
The transition from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism introduces a transformative approach to blockchain validation.
This paper explores two innovative solutions: EigenLayer and Lido.
We conclude with an evaluation of the combined potential of EigenLayer and Lido to foster a more resilient and inclusive ecosystem.
arXiv Detail & Related papers (2024-10-30T19:58:46Z) - SoK: Public Blockchain Sharding [19.82054462793622]
This study provides a systemization of knowledge of public blockchain sharding.
It includes the core components of sharding systems, challenges, limitations, and mechanisms of the latest sharding protocols.
arXiv Detail & Related papers (2024-05-30T22:38:40Z) - Larger-scale Nakamoto-style Blockchains Don't Necessarily Offer Better Security [1.2644625435032817]
Research on Nakamoto-style consensus protocols has shown that network delays degrade the security of these protocols.
This contradicts the very foundation of blockchains, namely that decentralization improves security.
We take a closer look at how the network scale affects security of Nakamoto-style blockchains.
arXiv Detail & Related papers (2024-04-15T16:09:41Z) - Graph Attention Network-based Block Propagation with Optimal AoI and Reputation in Web 3.0 [59.94605620983965]
We design a Graph Attention Network (GAT)-based reliable block propagation optimization framework for blockchain-enabled Web 3.0.
To achieve the reliability of block propagation, we introduce a reputation mechanism based on the subjective logic model.
Considering that the GAT possesses the excellent ability to process graph-structured data, we utilize the GAT with reinforcement learning to obtain the optimal block propagation trajectory.
arXiv Detail & Related papers (2024-03-20T01:58:38Z) - Generative AI-enabled Blockchain Networks: Fundamentals, Applications,
and Case Study [73.87110604150315]
Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology.
In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
arXiv Detail & Related papers (2024-01-28T10:46:17Z) - Analysis of Information Propagation in Ethereum Network Using Combined
Graph Attention Network and Reinforcement Learning to Optimize Network
Efficiency and Scalability [2.795656498870966]
We develop a Graph Attention Network (GAT) and Reinforcement Learning (RL) model to optimize the network efficiency and scalability.
In the experimental evaluation, we analyze the performance of our model on a large-scale dataset.
The results indicate that our designed GAT-RL model achieves superior results compared to other GCN models in terms of performance.
arXiv Detail & Related papers (2023-11-02T17:19:45Z) - Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection.
A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes.
Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
arXiv Detail & Related papers (2023-07-11T07:48:39Z) - Highly Available Blockchain Nodes With N-Version Design [13.131269677617286]
This paper introduces the concept of N-version blockchain nodes.
N-version nodes rely on simultaneous execution of different implementations of the same blockchain protocol.
We show that N-ETH is able to mitigate the effects of unstable execution environments and significantly enhance availability under environment faults.
arXiv Detail & Related papers (2023-03-25T11:16:17Z) - Energy Regularized RNNs for Solving Non-Stationary Bandit Problems [97.72614340294547]
We present an energy term that prevents the neural network from becoming too confident in support of a certain action.
We demonstrate that our method is at least as effective as methods suggested to solve the sub-problem of Rotting Bandits.
arXiv Detail & Related papers (2023-03-12T03:32:43Z) - HANT: Hardware-Aware Network Transformation [82.54824188745887]
We propose hardware-aware network transformation (HANT)
HANT replaces inefficient operations with more efficient alternatives using a neural architecture search like approach.
Our results on accelerating the EfficientNet family show that HANT can accelerate them by up to 3.6x with 0.4% drop in the top-1 accuracy on the ImageNet dataset.
arXiv Detail & Related papers (2021-07-12T18:46:34Z) - ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep
Neural Network and Transfer Learning [80.85273827468063]
Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable.
We propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for smart contracts.
We show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract.
arXiv Detail & Related papers (2021-03-23T15:04:44Z)
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.