Difference between revisions of "BaseFeeChangeDenominator"
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(Created page with " Divides the difference between actual and target utilization to determine how much to increase/decrease the base fee. A larger denominator indicates a slower changing, sticki...") |
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| + | The optimal value of BaseFeeChangeDenominator depends on a variety of factors, including the current state of the network, the expected transaction volume, and the desired level of responsiveness to changes in fees. Typically, a value between 100 and 1000 is recommended, although the exact value may need to be adjusted over time based on network conditions. | ||
| − | {{avax}} | + | Ultimately, the best approach is to experiment with different values of BaseFeeChangeDenominator and monitor the network's performance to determine the optimal setting for your specific use case. It is also important to consider other parameters and factors that can affect the overall performance of the network, such as block size and transaction throughput. |
| + | |||
| + | Ref: [[ChatGPT]] | ||
| + | |||
| + | == See also == | ||
| + | * {{avax}} | ||
| + | |||
| + | [[Category:Avalanche]] | ||
Revision as of 09:08, 21 April 2023
Divides the difference between actual and target utilization to determine how much to increase/decrease the base fee. A larger denominator indicates a slower changing, stickier base fee, while a lower denominator allows the base fee to adjust more quickly.
The optimal value of BaseFeeChangeDenominator depends on a variety of factors, including the current state of the network, the expected transaction volume, and the desired level of responsiveness to changes in fees. Typically, a value between 100 and 1000 is recommended, although the exact value may need to be adjusted over time based on network conditions.
Ultimately, the best approach is to experiment with different values of BaseFeeChangeDenominator and monitor the network's performance to determine the optimal setting for your specific use case. It is also important to consider other parameters and factors that can affect the overall performance of the network, such as block size and transaction throughput.
Ref: ChatGPT
See also
- Avalanche, AVAX token, Ava Labs, Avalanche subnets, SING token,
avalanche-cli, subnet-cli, avalanchego, AvalancheJS, Avalanche Network Runner, Fuji, 9650, 9651,/ext/, Exchange Chain (X-Chain), Contract Chain (C-Chain), Platform Chain (P-Chain), Avalanche Wallet, Explorers, Avalanche Cortina, Subnet EVM, apm, HyperSDK, Multiverse, Blockpay.mx, Core.app, BitNote
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