Practical Considerations for Implementing State Machine Replication in the Cloud

State Machine Replication (SMR) protocols form the backbone of many distributed systems. Enterprises and startups increasingly build their distributed systems on the cloud due to its many advantages, such as scalability and cost-effectiveness. Due to their prevalence in systems, the practical aspects of SMR algorithms, such as efficiency and performance, become hugely important. These practical considerations can impact capacity planning, deployment strategies, and, ultimately, the cost of running stateful systems in the cloud. In this paper, we consider various practical choices that impact the performance and efficiency of state machine replication in the cloud. To that order, we design a language-agnostic Multi-Paxos-based state machine architecture and implement it in several languages popular for cloud deployment. In the process, we investigate the impact of threading, communication, and memory management models on replicated state machines’ performance and resource efficiency. We also examine the high-level implications of virtualization on the performance of SMR implementations in various languages. We present our findings as a collection of practical lessons backed by our experimental data and analysis.