Relay Mining: Incentivizing Full Non-Validating Nodes Servicing All RPC Types
Tags: machinelearning, ai, beginners, datascience
Published: 2024-05-01 13:00:40

*This is a Plain English Papers summary of a research paper called [Relay Mining: Incentivizing Full Non-Validating Nodes Servicing All RPC Types](https://aimodels.fyi/papers/arxiv/relay-mining-incentivizing-full-non-validating-nodes). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*

## Overview

- Relay Mining presents a scalable solution to estimate and prove the volume of Remote Procedure Calls (RPCs) made from a client to a server.
- This addresses a gap in distributed ledgers, which are designed to secure permissionless state transitions (writes) but lack incentives for servicing non-transactional (read) RPCs.
- The solution leverages digital signatures, commit-and-reveal schemes, and Sparse Merkle Sum Tries (SMSTs) to prove the amount of work done, introducing a novel ClosestMerkleProof proof-of-inclusion scheme.
- A native cryptocurrency and feedback loop control mechanism are used to rate limit applications and dynamically scale the difficulty of mining relay rewards.

## Plain English Explanation

Relay Mining tackles the challenge of incentivizing distributed networks to handle non-transactional "read" requests, in addition to the more commonly addressed "write" transactions. This is important because many applications rely on these read requests, but the current distributed ledger systems don't provide good incentives for handling them.

Relay Mining's solution uses a combination of cryptographic techniques, including digital signatures, commitments, and a novel "closest merkle proof" scheme, to allow multiple independent service providers to prove the amount of work they've done in servicing these read requests. This is enabled through a native cryptocurrency that is used to rate limit applications and dynamically adjust the difficulty of earning rewards for servicing requests, based on network usage.

By leveraging these crypto-economic incentives, Relay Mining aims to reduce the coordination overhead costs and introduce a more geopolitically and geographically distributed mechanism for providing these essential RPC services, compared to relying on altruistic or centralized providers.

## Technical Explanation

Relay Mining's solution employs probabilistic mechanisms, crypto-economic incentives, and new cryptographic primitives to address the challenge of incentivizing full non-validating nodes to service non-transactional (read) [Remote Procedure Calls (RPCs)](https://aimodels.fyi/papers/arxiv/notnets-accelerating-microservices-by-bypassing-network) in a distributed ledger system.

The key technical components of the Relay Mining approach include:

1. **Digital Signatures and Commit-and-Reveal Schemes**: These are used to prove the amount of work done by RPC providers in servicing client requests.
2. **Sparse Merkle Sum Tries (SMSTs)**: A novel data structure that enables efficient proofs of inclusion for the work performed.
3. **ClosestMerkleProof**: A new proof-of-inclusion scheme introduced by the authors to support the SMST-based proofs.
4. **Native Cryptocurrency and Feedback Loop Control**: A built-in cryptocurrency is used to rate limit applications and dynamically adjust the difficulty of mining relay rewards based on network usage, drawing from research on [token bucket algorithms and distributed rate-limiting penalty models](https://aimodels.fyi/papers/arxiv/proof-learning-incentive-security).

By leveraging these cryptographic primitives and crypto-economic incentives, Relay Mining aims to reduce the coordination overhead costs and introduce a more [geopolitically and geographically distributed](https://aimodels.fyi/papers/arxiv/saving-proof-work-by-hierarchical-block-structure) mechanism for providing essential RPC services, compared to relying on altruistic or centralized off-chain providers.

## Critical Analysis

The Relay Mining paper presents a compelling solution to a significant challenge in the distributed ledger ecosystem. By addressing the incentive gap for servicing non-transactional read requests, the authors have the potential to improve the overall usability and robustness of Web3 applications.

However, the paper does not extensively discuss potential limitations or caveats of the proposed approach. For example, it would be valuable to understand the performance characteristics of the SMST data structure and the ClosestMerkleProof scheme, especially as the network scales and the number of RPC requests grows. Additionally, the authors could provide more insight into the potential security implications of the crypto-economic incentive model and how it might be vulnerable to manipulation or abuse.

Furthermore, the paper could benefit from a more in-depth comparison to related work, such as [proof-of-useful-computation](https://aimodels.fyi/papers/arxiv/replacing-cryptopuzzles-useful-computation-blockchain-proof-work) approaches and [self-custody non-ledger-based](https://aimodels.fyi/papers/arxiv/benchmarking-performance-self-custody-non-ledger-based) solutions, to better contextualize the novelty and trade-offs of the Relay Mining approach.

Overall, the Relay Mining paper presents a promising solution to a significant challenge in the distributed ledger ecosystem. However, further research and analysis would be valuable to fully understand the practical implications and potential limitations of the proposed approach.

## Conclusion

Relay Mining introduces a scalable solution to address the incentive gap for servicing non-transactional (read) Remote Procedure Calls (RPCs) in distributed ledger systems. By leveraging cryptographic techniques, crypto-economic incentives, and a novel ClosestMerkleProof scheme, the authors have developed a mechanism to enable multiple independent RPC providers to service requests from applications in a permissionless network.

The key innovation of Relay Mining is its ability to prove the amount of work done by RPC providers and dynamically adjust the difficulty of mining relay rewards based on network usage. This approach has the potential to reduce coordination overhead costs and introduce a more geopolitically and geographically distributed RPC service ecosystem, compared to relying on altruistic or centralized providers.

While the paper presents a compelling solution, further research is needed to fully understand the practical implications, performance characteristics, and potential security considerations of the Relay Mining approach. Nonetheless, this work represents an important step forward in addressing a significant challenge in the distributed ledger landscape and enabling more robust and scalable Web3 applications.

**If you enjoyed this summary, consider subscribing to the [AImodels.fyi newsletter](https://aimodels.substack.com) or following me on [Twitter](https://twitter.com/mikeyoung44) for more AI and machine learning content.**