Chain Simulations
Transaction Throughput Simulation
To evaluate the robustness and scalability of the foundational Layer 0 network, we performed a simulation of transaction throughput for several Layer 1 projects running concurrently. The primary aim was to observe the network’s ability to manage and distribute its transaction processing capacity among the projects.
Simulation Parameters
The simulation was conducted under the following assumptions:
Layer 0 Capacity: The maximum transactions per second (tps) capacity was set at 31,000, reflecting a high-throughput blockchain infrastructure.
Even Distribution: The Layer 0 network’s tps capacity was evenly divided among the Layer 1 projects, emulating a fair and balanced load-sharing protocol.
Temporal Scope: The simulation covered a 100-second timeframe, providing a snapshot of network activity in a high-velocity environment.
Throughput Simulation Results
The throughput simulation [18] results (Figure 5) showcased the transactions processed by each Layer 1 project over time. The graphical representation illustrated that despite the fluctuations typically observed in network conditions, each project maintained a consistent level of activity, indicating a resilient and well-dimensioned network infrastructure.
Gas Fee Simulation
Complementary to the throughput analysis, a simulation of gas fees was executed [19], capturing the computational and storage demands of various dApp types. This simulation aimed to offer insight into the costs associated with on-chain activity, from simple transactions to complex smart contract interactions.
Assumptions for Gas Fee Simulation
The following assumptions were integral to the gas fee simulation:
Computational Complexity: Each dApp type exhibited a distinct pattern of transaction complexity, informed by common use case scenarios.
Storage Requirements: dApps with storage needs were attributed higher gas fees, proportional to the size of the data being managed.
Network Congestion: A sinusoidal model was applied to simulate network congestion, affecting the gas fees across all dApp types.
Phron Zero Gas Fee Simulation Results
Since Phron Zero is the foundation on which multiple Layer ones will run, we need to understand the holistic impact of each layer one chain on layer zero. Let’s first take a look at the assumed types of each layer one.
Simulated Gaming Focused Layer One
A gaming-focused blockchain is a specialized blockchain network designed specifically to cater to the needs and requirements of the gaming industry. Such a blockchain leverages the unique characteristics of blockchain technology to offer various features and functionalities tailored to gamers, game developers, and other stakeholders within the gaming ecosystem. The expected gas fees would be an order of magnitude higher for the gaming Dapps rather than the DEX or Storage.
Simulated DEX Focused Layer One
A decentralized exchange (DEX) focused blockchain is a specialized blockchain network specifically designed to facilitate decentralized trading of digital assets, such as cryptocurrencies, tokens, and other blockchain-based assets. This type of blockchain prioritizes features and functionalities that enhance the performance, security, and user experience of decentralized exchange platforms. Naturally, such a chain would generate more swap related gas fees.
Simulated Storage Focused Layer One
A storage-focused blockchain typically prioritizes the efficient and secure storage of data on the blockchain network. Gas fees, which represent the cost of performing transactions or executing smart contracts on the blockchain, play a crucial role in incentivizing network participants and maintaining the security and integrity of the system. In a storage-focused blockchain, gas fees may be structured in a way that reflects the costs associated with storing and accessing data on the blockchain. Gas fees in a storage-focused blockchain are designed to reflect the costs of storing and accessing data on the blockchain while incentivizing efficient resource usage and maintaining network security and performance. By implementing a dynamic and transparent fee structure, the blockchain ensures that gas fees remain competitive, responsive, and aligned with the needs of network participants.
Phron Zero Simulated Gas Fee Consumption
The results, as visualized in Figure 9, depicted the variability of gas fees over time for gaming, DEX, and storage dApps. The simulation reflected that storage-intensive dApps may incur higher fees during peak data operations, whereas gaming and DEX dApps showed variable fees correlated with their interactive and market-driven activities.
Conclusion
The simulations confirm the Layer 0 network’s capacity to support a multi-faceted blockchain ecosystem, managing both high-velocity transactions and complex dApp interactions efficiently. By mirroring realistic operational conditions, the simulations validate the network’s design philosophy, highlighting its ability to adaptively balance performance and cost for diverse Layer 1 projects.
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