Towards Universal Atomic Composability: Application of the Formal Model

Table of Links

• Abstract and Introduction
• Formal Model - Rollups with Decentralized Common Pool (DCP)
• Atomic composability & ZK-proofs
• Incorporating Zk-proofs
• Application of the Formal Model
• Conclusion and References

5 Application of the Formal Model

As the Ethereum ecosystem evolves, diverse scaling solutions have emerged to address the challenges of throughput and latency. Among them, the concept of shared sequencers has gained significant traction. Shared sequencers act as centralized transaction ordering mechanisms, increasing throughput by temporarily assuming the role of transaction orderer before these transactions are batched and finalized on the main chain. While they introduce efficiencies, shared sequencers inherently operate differently from typical rollups, warranting an exploration of how our formal model for atomic composability can be applied to them.

The application of our formal model to shared sequencers and other existing solutions accentuates its versatility and universality. Whether it’s the centralized nature of sequencers or the diverse architectures of other rollups, the principles of atomic composability, as proposed in our model, remain consistent. This consistency is instrumental in creating a cohesive, interoperable, and scalable Ethereum ecosystem that can cater to the ever-growing demands of decentralized applications and services.

In this section, we extend our formal model to understand its implications on shared sequencers and juxtapose it with other prevailing solutions in the space. The goal is to provide a comparative analysis that not only elucidates the strengths and potential drawbacks of each approach but also offers a cohesive understanding of how atomic composability can be universally achieved irrespective of the underlying scaling solution.

5.1 Shared Sequencers

Shared sequencers, by design, centralize the transaction ordering mechanism without compromising the security guarantees of the main chain. Transactions are quickly processed off chain by the sequencer and then aggregated into larger batches to be submitted on-chain. This architecture provides the dual benefit of swift transaction times and reduced on-chain congestion.

5.1.1 Implications of the Formal Model:

1. Buffering & Dependency Handling: Given that shared sequencers operate in an off-chain environment before finalization, the buffering mechanism we proposed becomes even more critical. It allows for the temporary storage of transactions, especially when there are cross-rollup or cross-sequencer dependencies.

2. Concurrency Control: Shared sequencers inherently deal with a high volume of simultaneous transactions. Implementing our concurrency control mechanism ensures that interdependent transactions, even from different sources or rollups, can be processed in an atomic fashion.

3. Zero-Knowledge Proofs & Validation: The validation mechanism using zk-proofs becomes an asset here. It ensures that even in a semi-centralized environment, transaction validations remain private, swift, and secure. The zk-proofs also provide an added layer of trust to users who might be skeptical of the centralized nature of sequencers.

5.2 Alternative solutions:

While shared sequencers present a compelling case for scalability, other solutions like zk-rollups, optimistic rollups, and sidechains each have their unique architectures and merits.

1. zk-Rollups: These rely heavily on zk-proofs for batched transaction validation. Applying our formal model, zk-rollups can benefit from enhanced dependency handling and buffering mechanisms, ensuring transactions across rollups are consistently processed.

2. Optimistic Rollups: Here, transactions are assumed to be correct until proven otherwise. Our formal model introduces a systematic approach for handling disputes and reordering, ensuring atomic composability without extensive delays.

3. Sidechains: Being independent blockchains, sidechains can pose more significant challenges for atomic composability. Our model can act as a bridge, providing mechanisms like dependency resolution and buffering to ensure smooth inter-chain operations.