TL;DR
L3 zkRollup architecture serves as Oraclizer’s core technological foundation, with its adoption primarily driven by overwhelming advantages in scalability and economic efficiency. In particular, the revolutionary cost efficiency of over 93% gas reduction serves as a decisive differentiator in systems requiring continuous transactions like state synchronization. Additionally, the L3 layer design opens new technological horizons that existing oracle solutions haven’t reached in terms of data compression efficiency, processing pipeline optimization, and modular scalability. This isn’t merely a technical choice, but an essential architectural decision to realize the innovative paradigm of next-generation oracle state machines.
Our Concerns About Scalability Limitations
The greatest technical challenge our team encountered while designing Oraclizer was implementing state synchronization between on-chain and off-chain systems at an economically viable level. Existing oracle solutions haven’t evolved beyond simple data point transmission to complete state synchronization, primarily due to scalability limitations and resulting economic inefficiencies.
State synchronization inherently demands continuous transaction processing. Each state change requires verification and synchronization, generating numerous sub-transactions that incur enormous gas costs in traditional L1 or L2 structures. According to our calculations, implementing a state synchronization oracle with these structures creates a paradoxical situation where the costs users must pay significantly exceed the expected benefits obtainable through state synchronization.
Why L3 zkRollup Architecture?
After thoroughly examining various architectures, we confirmed that L3 zkRollup forms the ideal foundation for the oracle state machine we’re pursuing. Let’s analyze the scientific basis and superiority of this decision.
1. Economic Innovation Through Multi-layered Compression
The most significant technical advantage of L3 zkRollup architecture is its multi-layered data compression mechanism, realized through the following process:
- Primary compression (L3 → L2): Initial compression of oracle state change data
- Secondary compression (L2 → L1): Further compression of already compressed data
- Incremental proof optimization: Only proving the differences in state changes (state diffs)
This multi-layered compression dramatically reduces data volume, with efficiency increasing cumulatively in oracle environments where continuous state changes occur. This operates on a principle similar to how Git efficiently manages only changes rather than entire codebases[1].
2. Revolutionary Reduction in ZK Proof Verification Costs
To further maximize the efficiency of our L3 structure, we decided to integrate with Horizen Labs’ zkVerify technology. zkVerify is a specialized modular blockchain for ZK proof verification that can reduce verification costs by up to 91%[2]. This serves as a crucial element in achieving our architecture’s target of over 93% gas fee reduction.
While ZK proofs are essential for ensuring state transition validity, their verification process is computationally intensive and costly. By optimizing this process at L3 through integration with zkVerify, we’ve secured a level of economic efficiency incomparable to existing oracle solutions.
3. Strategic Benefits of Leveraging the Base L2 Ecosystem
Oraclizer is built on the Base L2 ecosystem, which represents a strategic choice beyond mere technical decision. Developed by Coinbase, Base offers the following advantages:
- Institutional-grade infrastructure: Access to Coinbase’s enterprise-level infrastructure
- OP Stack foundation: High scalability and interoperability through an open-source framework
- Liquidity accessibility: An ecosystem with concentrated large user base and capital
An L3 structure based on Base L2 provides an ideal framework that leverages the strengths of existing infrastructure while maximizing performance through its own optimizations[3]. In particular, the OP Stack messaging system enables efficient communication between L3s, delivering exceptional performance for state synchronization in cross-chain environments.
Core Mechanisms of Technical Implementation
Our L3 zkRollup architecture is implemented through the following core mechanisms:
1. State Compression and Proof Generation Process
State synchronization in L3 zkRollup occurs through the following steps:
- State capture: Detecting off-chain state changes
- State compression: Efficient state representation based on SMT (Sparse Merkle Tree)
- Proof generation: Creating zk proofs for the validity of state changes
- L2 submission: Submitting compressed data and proofs to L2
- L1 finalization: Finalizing the data, compressed again from L2, to L1
In this process, we defined two state transition functions, φ₃₂ and φ₂₁:
φ₃₂: StateL3 → StateL2 φ₃₂(s) = (h(s), π₃₂, metadata)
φ₂₁: StateL2 → StateL1 φ₂₁(s) = (h(s), π₂₁, metadata)
Φ = φ₂₁ ∘ φ₃₂
This mathematical definition ensures that the state transition process from L3 to L1 is fully verifiable and secure[4].
2. D-quencer: Decentralized Sequencer Algorithm
Sequencer centralization in L3 structures can pose security risks. To address this, we developed our proprietary decentralized sequencer algorithm called D-quencer.
D-quencer features the following characteristics:
- BLS signature-based VRF: Fair and unpredictable sequencer election
- Equal staking POS: Minimizing weight calculation overhead
- Multisig consensus: Guaranteeing BFT (Byzantine Fault Tolerance)
This enables us to maintain decentralization while securing scalability even in a rollup structure[5].
3. Data Availability Optimization
Another core component of L3 zkRollup architecture is the optimization of the data availability (DA) layer. We adopted the Validium approach to maximize off-chain data storage efficiency, providing the following benefits:
- Reduced data storage costs: Not storing all data on-chain
- Increased throughput: Eliminating DA layer bottlenecks
- Enhanced scalability: Freedom from data size constraints
The Validium approach effectively balances data availability and verifiability, maximizing scalability while maintaining security[6].
Measurable Performance Advantages of L3 zkRollup
The technical advantages provided by L3 zkRollup architecture can be measured in concrete figures. The table below compares key performance indicators between existing oracle solutions and Oraclizer’s L3 structure:
Performance Metric | Existing Oracle | Oraclizer L3 | Improvement |
---|---|---|---|
Proof Verification Cost | Baseline | -40% | 40% |
State Transition Throughput | 100/sec | 1,000+/sec | 10x |
Regulatory Verification Latency | 5-10 sec | 1-2 sec | 75% |
State Changes per Single Call | 10 | 100+ | 10x |
Such dramatic improvements in performance metrics provide an incomparable advantage over existing oracle solutions, especially in environments requiring continuous transaction processing like state synchronization.
Technical Advantages in Real Application Cases
The technical advantages of L3 zkRollup architecture are clearly evident in various application cases. For example, let’s compare tracking price changes of tokenized bonds:
Existing Oracle Approach:
- Requires new, independent oracle calls for each price change
- Gas fees paid for each call, costs increase linearly
- 10x cost incurred for 10 updates over an hour
L3 zkRollup Approach:
- Incrementally processes state changes after initial state registration
- Efficient verification through compressed data and zk proofs
- Only about 0.7x cost for the same period, approximately 93% savings
The difference in cost structure grows larger as the number of transactions increases, demonstrating why L3 zkRollup architecture is essential for state synchronization oracles[7].
Technical Challenges and Solutions in Adopting L3 zkRollup
We faced several technical challenges in adopting L3 zkRollup architecture. Resolving these challenges strengthened the robustness of our architecture.
1. Balancing Decentralization and Scalability
In rollup-based structures, maintaining decentralization while securing scalability was a fundamental challenge. There was a dilemma: centralizing sequencers increases scalability but enlarges security risks, while full decentralization degrades performance.
To resolve this, we adopted the Stage 1 rollup model and developed the D-quencer algorithm. This allowed us to secure sufficient decentralization while eliminating unnecessary consensus overhead.
2. Minimizing Verification Latency
Generating and verifying ZK proofs is a computationally intensive process that could increase state synchronization latency. This was a significant challenge, especially in situations where state synchronization demanded near real-time performance.
To solve this problem, we decided to integrate with Horizen Labs’ zkVerify. zkVerify’s verification network provides specialized infrastructure for ZK proof verification, significantly reducing verification time.
3. Ensuring Cross-chain Consistency
Ensuring state consistency in cross-chain environments with L3 structures was a highly complex issue. State synchronization between different chains required consideration of various edge cases, including network delays, temporary failures, and retry logic.
For this, we designed atomic state transitions and rollback mechanisms. By implementing safeguards that ensure cross-chain state consistency in any situation, we enabled reliable state synchronization even in cross-chain environments.
Conclusion
Adopting L3 zkRollup architecture wasn’t merely a technical choice, but an essential decision to realize Oraclizer’s core value of state synchronization. This architecture fundamentally resolves the limitations of existing oracle solutions by integrating various technical innovations, including multi-layered compression, ZK proof optimization, and Validium-style data availability.
In particular, gas fee reductions exceeding 93% and throughput increases of over 10x have become decisive factors in realizing the new paradigm of state synchronization oracles. We are now continuously optimizing and expanding our architecture to establish a foundation for efficiently synchronizing a broader range of data and states.
L3 zkRollup architecture will become an important milestone in the evolution of oracle technology and establish itself as a key element in realizing true scalability and practicality of blockchain.
References
[1] Chainalysis. (2024). Zero Knowledge Rollups & Optimistic Rollups: An Overview. https://www.chainalysis.com/blog/zero-knowledge-rollups-optimistic-rollups-overview/
[2] Horizen Labs. (2024). The Launch of zkVerify. https://blog.zkverify.io/posts/the-launch-of-zkverify
[3] Coinbase. (2023). Introducing Base. https://www.coinbase.com/blog/introducing-base
[4] Matter Labs. (2023). zkSync: Scaling and privacy engine for Ethereum based on ZK Rollup. https://zksync.io/zksync.pdf
[5] Ethereum Foundation. (2024). Zero-knowledge rollups. https://ethereum.org/en/developers/docs/scaling/zk-rollups/
[6] Hacken. (2024). ZK-Rollups: The Next Step In Blockchain Scalability. https://hacken.io/discover/zk-rollups-explained/
[7] Biconomy. (2024). Reducing gas fees on L2 roll-ups. https://www.biconomy.io/post/reducing-gas-fees-on-l2-roll-ups
Further Reading
To learn more about how Oraclizer implements state synchronization using our L3 architecture, check out our documentation at https://docs.oraclizer.io/
For more insights on the theoretical foundations of oracle state machines, explore our research article “Oracle State Machine: Beginning of a New Paradigm“