PANDAS: Peer-to-peer, Adaptive Networking for Data Availability Sampling within Ethereum Consensus Timebounds

TL;DR

PANDAS introduces a practical method for integrating data availability sampling into Ethereum's Danksharding, enabling scalable layer-2 data dissemination and sampling within strict time constraints without altering core protocols.

Contribution

It presents a novel, lightweight approach for disseminating and sampling layer-2 data in Ethereum that meets tight timing requirements without protocol modifications.

Findings

Supports data dissemination within 4 seconds at planetary-scale latencies.

Enables sampling of layer-2 data in large-scale Ethereum networks.

Demonstrates scalability up to 20,000 peers in simulations.

Abstract

Layer-2 protocols can assist Ethereum's limited throughput, but globally broadcasting layer-2 data limits their scalability. The Danksharding evolution of Ethereum aims to support the selective distribution of layer-2 data, whose availability in the network is verified using randomized data availability sampling (DAS). Integrating DAS into Ethereum's consensus process is challenging, as pieces of layer-2 data must be disseminated and sampled within four seconds of the beginning of each consensus slot. No existing solution can support dissemination and sampling under such strict time bounds. We propose PANDAS, a practical approach to integrate DAS with Ethereum under Danksharding's requirements without modifying its protocols for consensus and node discovery. PANDAS disseminates layer-2 data and samples its availability using lightweight, direct exchanges. Its design accounts for message loss, node failures, and unresponsive participants while anticipating the need to scale out the Ethereum network. Our evaluation of PANDAS's prototype in a 1,000-node cluster and simulations for up to 20,000 peers shows that it allows layer-2 data dissemination and sampling under planetary-scale latencies within the 4-second deadline.