ammBoost: State Growth Control for AMMs
- URL: http://arxiv.org/abs/2406.17094v2
- Date: Mon, 1 Jul 2024 14:10:56 GMT
- Title: ammBoost: State Growth Control for AMMs
- Authors: Nicholas Michel, Mohamed E. Najd, Ghada Almashaqbeh,
- Abstract summary: Automated market makers (AMMs) are a prime example of Decentralized Finance (DeFi) applications.
Their popularity and high trading activity have resulted in millions of on-chain transactions leading to serious scalability issues.
In this paper, we address the on-chain storage overhead problem of AMMs by utilizing a new sidechain architecture as a layer 2 solution.
- Score: 0.6383640665055312
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Automated market makers (AMMs) are a form of decentralized cryptocurrency exchanges and considered a prime example of Decentralized Finance (DeFi) applications. Their popularity and high trading activity have resulted in millions of on-chain transactions leading to serious scalability issues. In this paper, we address the on-chain storage overhead problem of AMMs by utilizing a new sidechain architecture as a layer 2 solution, building a system called ammBoost. Our system reduces the amount of on-chain transactions, boosts throughput, and supports blockchain pruning. We devise several techniques to enable layer 2 processing for AMMs while preserving correctness and security of the underlying AMM. We also build a proof-of-concept of ammBoost for a Uniswap-inspired use case to empirically evaluate its performance. Our experiments show that ammBoost decreases the gas cost by 94.53% and the chain growth by at least 80%, and that it can support up to 500x of the daily traffic volume observed for Uniswap in practice.
Related papers
- SAMM: Sharded Automated Market Makers [2.6831773062745863]
We present emphSAMM, an AMM comprising multiple independent emphshards.
We show that all Subgame-Perfect Nash Equilibria (SPNE) fit the desired behavior: Liquidity providers balance the liquidity among all pools, so the system converges to the state where trades are evenly distributed.
Evaluation in the Sui blockchain shows that SAMM's throughput is over fivefold that of traditional AMMs, approaching the system's limit.
arXiv Detail & Related papers (2024-06-08T20:19:35Z) - Decentralization of Ethereum's Builder Market [13.369643484986593]
We study one of the least decentralized parts of the most used blockchain system in practice.
To avoid centralization caused by Maximal Extractable Value (MEV), adopts a novel mechanism that produces blocks through a builder market.
Our findings help identify directions for improving the decentralization of builder markets.
arXiv Detail & Related papers (2024-05-02T14:32:21Z) - chainBoost: A Secure Performance Booster for Blockchain-based Resource Markets [0.6383640665055312]
We propose chainBoost, a secure performance booster for decentralized resource markets.
It expedites service related operations, reduces the blockchain size, and supports flexible service-payment exchange modalities at low overhead.
We implement a proof-of-concept prototype for a distributed file storage market as a use case.
arXiv Detail & Related papers (2024-02-25T14:19:41Z) - 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) - A Scale-out Decentralized Blockchain Ledger System for Web3.0 [5.327844605578174]
This paper proposes EZchain -- a novel decentralized scale-out" ledger system designed for web3.0.
Without compromising security and decentralization, EZchain successfully accomplishes the following milestones.
arXiv Detail & Related papers (2023-12-01T01:34:48Z) - Unpacking How Decentralized Autonomous Organizations (DAOs) Work in
Practice [54.47385318258732]
Decentralized Autonomous Organizations (DAOs) have emerged as a novel way to coordinate a group of entities towards a shared vision.
In just a few years, over 4,000 DAOs have been launched in various domains, such as investment, education, health, and research.
Despite such rapid growth and diversity, it is unclear how theses actually work in practice and to what extent they are effective in achieving their goals.
arXiv Detail & Related papers (2023-04-17T01:30:03Z) - Uniswap Liquidity Provision: An Online Learning Approach [49.145538162253594]
Decentralized Exchanges (DEXs) are new types of marketplaces leveraging technology.
One such DEX, Uniswap v3, allows liquidity providers to allocate funds more efficiently by specifying an active price interval for their funds.
This introduces the problem of finding an optimal strategy for choosing price intervals.
We formalize this problem as an online learning problem with non-stochastic rewards.
arXiv Detail & Related papers (2023-02-01T17:21:40Z) - QLAMMP: A Q-Learning Agent for Optimizing Fees on Automated Market
Making Protocols [5.672898304129217]
We develop a Q-Learning Agent for Market Making Protocols (QLAMMP) that learns the optimal fee rates and leverage coefficients for a given AMM protocol.
We show that QLAMMP is consistently able to outperform its static counterparts under all the simulated test conditions.
arXiv Detail & Related papers (2022-11-28T00:30:45Z) - Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in
Edge Industrial IoT [106.83952081124195]
Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes.
We propose an adaptive ADMM (asI-ADMM) algorithm and apply it to decentralized RL with edge-computing-empowered IIoT networks.
Experiment results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability, and can well adapt to complex IoT environments.
arXiv Detail & Related papers (2021-06-30T16:49:07Z) - 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) - Resource Management for Blockchain-enabled Federated Learning: A Deep
Reinforcement Learning Approach [54.29213445674221]
Federated Learning (BFL) enables mobile devices to collaboratively train neural network models required by a Machine Learning Model Owner (MLMO)
The issue of BFL is that the mobile devices have energy and CPU constraints that may reduce the system lifetime and training efficiency.
We propose to use the Deep Reinforcement Learning (DRL) to derive the optimal decisions for theO.
arXiv Detail & Related papers (2020-04-08T16:29:19Z)
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.