RACS-SADL: Robust and Understandable Randomized Consensus in the Cloud

Abstract

Widely deployed consensus protocols in the cloud are often leader-based and optimized for low latency under synchronous network conditions. However, cloud networks can experience disruptions such as network partitions, high-loss links, and configuration errors. These disruptions interfere with the operation of leader-based protocols, as their view change mechanisms interrupt the normal case replication and cause the system to stall. We propose RACS, a novel randomized consensus protocol that ensures robustness against adversarial network conditions. RACS achieves optimal one-round trip latency under synchronous network conditions while remaining resilient to adversarial network conditions. RACS follows a simple design inspired by Raft, the most widely used consensus protocol in the cloud, and therefore enables seamless integration with the existing cloud software stack. Experiments with a prototype running on Amazon EC2 show that RACS achieves 28k cmd/sec throughput, ninefold higher than Raft under adversarial cloud network conditions. Under synchronous network conditions, RACS matches the performance of Multi-Paxos and Raft, achieving a throughput of 200k cmd/sec with a median latency of 300ms, confirming that RACS introduces no unnecessary overhead. Finally, SADL-RACS, a throughput-optimized version of RACS, achieves a throughput of 500k cmd/sec, delivering 150% higher throughput than Raft.