Gas Algorithm Update v1 (RAM-Intensive Focus)

[stephenpdeos]

Gas Algorithm Update v1 (RAM-Intensive Focus)

Problem

Opportunity: What are the needs of our target user groups?

Developers and users within the EOS EVM ecosystem face challenges with a one-size-fits-all gas fee structure that doesn't account for the varying computational resources required by transactions, particularly those that are RAM-intensive. Addressing this can significantly improve cost efficiency and fairness, encouraging continued use of the network by other applications even when RAM-intensive applications have been deployed.

Strategic alignment: How does this problem align with our core strategic pillars?

Focusing on RAM-intensive transactions for gas fee adjustments aligns with our commitment to "Ease of Use" and "Fair Pricing". By optimizing gas fees for transactions based on their actual resource consumption, we support a more equitable and sustainable ecosystem for developers and users alike.

Context

Competitors: Who are our top competitors and why? How do they solve this problem today?

Competitors like Ethereum, with EIP-1559, introduce dynamic fee structures but do not specifically target RAM-intensive transaction costs because it is not part of their resource model. Our approach directly addresses the computational resource allocation, setting EOS EVM apart by optimizing network efficiency and user costs in a targeted manner.

Product differentiation: What would make our solution different?

Our solution stands out by precisely targeting RAM-intensive transactions, ensuring that gas fees more accurately reflect the actual computational resources used. This nuanced approach promotes a more balanced and fair usage of network resources that avoids punishing all operations on the network.

Solution

Solution name: RAM-Focused Gas Fee Algorithm (RFGFA)

Purpose: Define the product’s purpose briefly

RFGFA aims to recalibrate the gas fee structure within EOS EVM, specifically addressing the cost associated with RAM-intensive transactions to ensure fairness and encourage efficient network resource utilization.

Success definition: What are the top metrics for the product to define success?

1. Reduction in average gas fees for non-RAM-intensive transactions.
2. Broad adoption and positive reception from the EOS EVM community.

Assumptions

• Transactions varying in RAM usage represent a significant portion of network activity.
• A more equitable gas fee structure will drive increased adoption and innovation on EOS EVM.

Risks: What risks should be considered?

• Potential challenges in accurately measuring and categorizing RAM usage for transactions.
• Risk of unintended consequences on dApp development and usage patterns.

Business Objectives/Functionality

1. Implement an algorithm within EOS EVM that dynamically adjusts gas fees based on RAM usage.

User stories

• As a developer, I want to optimize my dApp for efficiency, knowing that the gas fees will fairly reflect the RAM resources used.
• As a user, I appreciate fairer gas fees that encourage a wider variety of dApps on the EOS EVM platform, enhancing my overall experience.

Open questions

• TBD

Tasks

Beta Give feedback

1. https://github.com/eosnetworkfoundation/eos-evm-internal/issues/132
2. Emit configchange event eos-evm-contract#679
   👍 lgtm +1
   
3. Store minimum gas price and 5 gas fee parameters in state associated to each block silkworm#121
   👍 lgtm +1
   
4. Implement updated gas fee algorithm evmone#4
   👍 lgtm +1
   
5. Process configchange event to record minimum gas price and gas parameters eos-evm-node#186
   👍 lgtm +1
   
6. Pass gas parameters into ExecutionState silkworm#122
   👍 lgtm +1
   
7. Use active gas parameters in ExecutionState eos-evm-contract#680
   👍 lgtm +1
   
8. Use saved gas parameters for new gas algorithm eos-evm-node#187
   👍 lgtm +1
   
9. Implement updated gas fee algorithm (evmone #4) silkworm#126
   👍 lgtm +1
   

Possible future tasks

• Continuously monitor and adjust the algorithm parameters to align with network growth and technological advancements.