Scaling Replicated State Machines with Compartmentalization
Michael J. Whittaker, Ailidani Ailijiang, Aleksey Charapko, Murat Demirbas, Neil Giridharan, Joseph M. Hellerstein, Heidi Howard, Ion Stoica, Adriana Szekeres
Abstract
State machine replication protocols, like MultiPaxos and Raft, are a critical component of many distributed systems and databases. However, these protocols offer relatively low throughput due to several bottlenecked components. Numerous existing protocols fix different bottlenecks in isolation but fall short of a complete solution. When you fix one bottleneck, another arises. In this paper, we introduce compartmentalization, the first comprehensive technique to eliminate state machine replication bottlenecks. Compartmentalization involves decoupling individual bottlenecks into distinct components and scaling these components independently. Compartmentalization has two key strengths. First, compartmentalization leads to strong performance. In this paper, we demonstrate how to compartmentalize MultiPaxos to increase its throughput by 6× on a write-only workload and 16× on a mixed read-write workload. Unlike other approaches, we achieve this performance without the need for specialized hardware. Second, compartmentalization is a technique, not a protocol. Industry practitioners can apply compartmentalization to their protocols incrementally without having to adopt a completely new protocol.
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Cited by top-tier papers2
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Builds on4
- Harmonia: Near-Linear Scalability for Replicated Storage with In-Network Conflict DetectionHang Zhu, Zhihao Bai, Jialin Li, Ellis Michael et al.VLDB 2020 · 58 citations
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- Scalog: Seamless Reconfiguration and Total Order in a Scalable Shared LogCong Ding, David Chu, Evan Zhao, Xiang Li et al.NSDI 2020 · 52 citations
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