Practical Rateless Set Reconciliation (2402.02668v3)

Abstract: Set reconciliation, where two parties hold fixed-length bit strings and run a protocol to learn the strings they are missing from each other, is a fundamental task in many distributed systems. We present Rateless Invertible Bloom Lookup Tables (Rateless IBLT), the first set reconciliation protocol, to the best of our knowledge, that achieves low computation cost and near-optimal communication cost across a wide range of scenarios: set differences of one to millions, bit strings of a few bytes to megabytes, and workloads injected by potential adversaries. Rateless IBLT is based on a novel encoder that incrementally encodes the set difference into an infinite stream of coded symbols, resembling rateless error-correcting codes. We compare Rateless IBLT with state-of-the-art set reconciliation schemes and demonstrate significant improvements. Rateless IBLT achieves 3--4x lower communication cost than non-rateless schemes with similar computation cost, and 2--2000x lower computation cost than schemes with similar communication cost. We show the real-world benefits of Rateless IBLT by applying it to synchronize the state of the Ethereum blockchain, and demonstrate 5.6x lower end-to-end completion time and 4.4x lower communication cost compared to the system used in production.

• The paper introduces Rateless IBLT that incrementally encodes set differences, enabling rateless erasure correction without estimating set sizes.
• It achieves a communication overhead of 1.35d to 1.72d, reducing costs 3–4× and cutting computation by up to 2000× compared to traditional methods.
• It demonstrates real-world impact by processing differences at 120 MB/s on a single core and lowering blockchain sync time by 5.6× and costs by 4.4×.

Practical Rateless Set Reconciliation: An Academic Overview

The paper "Practical Rateless Set Reconciliation" introduces Rateless Invertible Bloom Lookup Tables (Rateless IBLT), a novel protocol for efficient set reconciliation. Set reconciliation is a critical task within distributed systems where two parties need to determine the set differences between their respective datasets. Rateless IBLT distinguishes itself by achieving low computational and near-optimal communication costs across varied scenarios, including datasets with disparities ranging from one to millions of entries.

Technical Contributions

1. Rateless IBLT Protocol Design: Rateless IBLT derives its efficacy from a novel encoder that incrementally encodes the set difference into an endless sequence of coded symbols. This approach enables rateless erasure correction without requiring size estimations of set differences.
2. Communication and Computation Efficiency: The protocol demonstrates $1.35d$ to $1.72d$ communication overhead relative to the number of differences $d$ for large values of $d$. It significantly outperforms traditional schemes in both communication cost and computational demand, achieving $3$--$4\times$ lower communication cost and $2$--$2000\times$ lower computational cost than existing solutions.
3. Real-World Application and Implementation: The Rateless IBLT implementation, capable of processing differences at $120$ MB/s using a single CPU core, was tested within the Ethereum blockchain context, showcasing a reduction in end-to-end synchronization time and communication cost by factors of $5.6\times$ and $4.4\times$, respectively.

Implications and Speculations

Rateless IBLT has significant implications for distributed systems efficiency, particularly those that rely on the exchange and synchronization of large state replicas, such as blockchain networks, social networks, and distributed file systems. The ratelessness of the protocol eliminates the necessity for real-time difference size estimation, enabling streamlined and effective communication, making it an ideal choice for use cases where the set difference is difficult to pre-estimate.

The theoretical underpinning of Rateless IBLT provides new insights into set reconciliation frameworks, suggesting the potential for further research into using rateless coding techniques within other data synchronization contexts.

Future Developments in AI and Distributed Systems

Looking forward, Rateless IBLT could inspire AI-driven approaches that leverage its rateless encoding properties to improve data recovery mechanisms, potentially optimizing real-time data streams and backups. As distributed systems grow in complexity and scale, Rateless IBLT's ability to maintain low overheads in communication and computation could be pivotal in applications ranging from decentralized data storage solutions to IoT networks where resource constraints are prevalent.

The paper's provision of a robust theoretical analysis, coupled with extensive empirical evaluation, lays a solid foundation for exploring adaptive, rateless approaches within redundant and fault-tolerant network designs. Future work could expand on creating more generalized rateless frameworks that cater to dynamically-changing environments, thus propelling further advancements at the intersection of coding theory and distributed systems.