Breaking the Scalability Barrier of Causal Broadcast for Large and Dynamic Systems

TL;DR

This paper introduces a scalable, non-blocking causal broadcast protocol for large, dynamic distributed systems, significantly improving message size, execution time, and space complexity, enabling practical causal broadcast at scale.

Contribution

It presents a novel causal broadcast protocol that is non-blocking, scalable, and suitable for dynamic systems, with constant-size control information piggybacked on messages.

Findings

Outperforms existing protocols in message size and complexity

Proven effectiveness in both static and dynamic systems

Enables causal broadcast in large, evolving distributed environments

Abstract

Many distributed protocols and applications rely on causal broadcast to ensure consistency criteria. However, none of causality tracking state-of-the-art approaches scale in large and dynamic systems. This paper presents a new non-blocking causal broadcast protocol suited for dynamic systems. The proposed protocol outperforms state-of-the-art in size of messages, execution time complexity, and local space complexity. Most importantly, messages piggyback control information the size of which is constant. We prove that for both static and dynamic systems. Consequently, large and dynamic systems can finally afford causal broadcast.