SCONE: Efficient PBS for High-load Chains

The SCONE project addresses the future challenges facing proposer-builder separation (PBS) in Ethereum. The project proposes the use of Optimal Stopping, a statistical method for determining the best time to make an early decision when faced with a sequence of options, to help relays/validators reduce the computational load required to check all block proposals within a block interval. This improves the efficiency and scalability of the relay, even under DoS attacks with high-bid invalid blocks.

The competition for profitable block proposals has become increasingly intense, so the MEV auction will become a computational challenge for relays. The increasing computational load on relays to check block proposals can threaten network security by missing valid and profitable blocks, especially in high-load situations; fast Layer-2 blockchains, or high computational auctions. Optimal Stopping, the statistical method, helps relays evaluate proposals and accept the most profitable ones, improving efficiency, scalability, and security.

Our project, SCONE, is built on a combination of research, data analysis, statistical modeling, and simulation development to address the scalability and security challenges of PBS in high-load chain environments. To the best of our knowledge, the SCONE project is the first initiative aimed at studying the scalability and security of the proposer-builder separation (PBS) scheme in a high-load chain environment.

Given that the MEV auction in PBS is poised to become a significant market, we developed the open-source "Optimal Stopping for PBS" simulator to share our findings and demonstrate the importance of our work to the blockchain community. Our mission is to provide a user-friendly simulation tool that can help researchers and relays/validators understand and experiment with the potential benefits of Optimal Stopping in block evaluation. Additionally, we offer two versions of our simulator implementation in Python and JavaScript, respectively, to accommodate users' language preferences.

Our project required extensive research and data analysis of maximal extractable value (MEV) and the scalability and security challenges of PBS. Although determining the meaningful risk potential from the data is currently challenging due to a lack of competition, we put considerable effort into utilizing real-world Ethereum data to refine our definitions and assumptions and explore potential DoS attack scenarios. In addition, we utilized statistical modeling and simulation tools to implement Optimal Stopping in PBS, experimenting with various models to ensure our solution was robust and effective in various scenarios.