English

AWARE: Adaptive Wide-Area Replication for Fast and Resilient Byzantine Consensus

Distributed, Parallel, and Cluster Computing 2020-11-04 v1

Abstract

With upcoming blockchain infrastructures, world-spanning Byzantine consensus is getting practical and necessary. In geographically distributed systems, the pace at which consensus is achieved is limited by the heterogenous latencies of connections between replicas. If deployed on a wide-area network, consensus-based systems benefit from weighted replication, an approach that utilizes extra replicas and assigns higher voting power to well connected replicas. This enables more choice in quorum formation and replicas can leverage proportionally smaller quorums to advance, thus decreasing consensus latency. However, the system needs a solution to autonomously adjust to its environment if network conditions change or faults occur. We present Adaptive Wide-Area REplication (AWARE), a mechanism which improves the geographical scalability of consensus with nodes being widely spread across the world. Essentially, AWARE is an automated and dynamic voting weight tuning and leader positioning scheme, which supports the emergence of fast quorums in the system. It employs a reliable self-monitoring process and provides a prediction model seeking to minimize the system's consensus latency. In experiments using several AWS EC2 regions, AWARE dynamically optimizes consensus latency by self-reliantly finding a fast weight configuration yielding latency gains observed by clients located across the globe.

Keywords

Cite

@article{arxiv.2011.01671,
  title  = {AWARE: Adaptive Wide-Area Replication for Fast and Resilient Byzantine Consensus},
  author = {Christian Berger and Hans P. Reiser and João Sousa and Alysson Bessani},
  journal= {arXiv preprint arXiv:2011.01671},
  year   = {2020}
}

Comments

This paper consists of 16 pages in total. This paper is the accepted version to be published in IEEE Transactions on Dependable and Secure Computing (2020). For the published version refer to DOI https://doi.org/10.1109/TDSC.2020.3030605

R2 v1 2026-06-23T19:53:01.869Z