The Cost of Garbage Collection for State Machine Replication (2405.11182v1)

Abstract: 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. One of the first technical questions companies face when building a system on the cloud is which programming language to use. Among many factors that go into this decision is whether to use a language with garbage collection (GC), such as Java or Go, or a language with manual memory management, such as C++ or Rust. Today, companies predominantly prefer languages with GC, like Go, Kotlin, or even Python, due to ease of development; however, there is no free lunch: GC costs resources (memory and CPU) and performance (long tail latencies due to GC pauses). While there have been anecdotal reports of reduced cloud cost and improved tail latencies when switching from a language with GC to a language with manual memory management, so far, there has not been a systematic study of the GC overhead of running an SMR-based cloud system. This paper studies the overhead of running an SMR-based cloud system written in a language with GC. To this end, we design from scratch a canonical SMR system -- a MultiPaxos-based replicated in-memory key-value store -- and we implement it in C++, Java, Rust, and Go. We compare the performance and resource usage of these implementations when running on the cloud under different workloads and resource constraints and report our results. Our findings have implications for the design of cloud systems.

• The paper presents a systematic evaluation of garbage collection's impact on state machine replication, comparing manual and automatic memory management in cloud environments.
• Experiments on AWS with the Replicant key-value store reveal up to 9x higher throughput for C++ over Java under memory constraints.
• The study highlights how language choice and virtualization influence resource efficiency, offering actionable insights for optimizing cloud deployments.

Analyzing the Impact of Garbage Collection in State Machine Replication Systems

The paper "The Cost of Garbage Collection for State Machine Replication" presents a systematic evaluation of the performance implications of garbage collection (GC) in state machine replication (SMR) systems, particularly in cloud environments. By creating a canonical MultiPaxos-based replicated in-memory key-value store called Replicant, implemented in multiple programming languages each with different memory management techniques, the authors analyze the trade-offs between ease of development and runtime efficiency, providing insights into the cloud cost implications of language choice.

Key Contributions and Methodology

This paper makes significant contributions by implementing Replicant in four programming languages: C++, Java, Rust, and Go, to compare manual versus automatic memory management in cloud-based SMR systems. Through a series of controlled experiments on Amazon Web Services (AWS), the researchers evaluate the throughput and resource usage under various workloads (update-heavy and read-heavy) and resource constraints (memory and CPU variations).

The authors employ a consistent set of tools and approaches across implementations, using gRPC for inter-peer communication in C++ and Java, while Rust and Go versions initially suffered performance bottlenecks due to the H2 library, leading to a subsequent redesign utilizing TCP. The metrics used include maximum throughput while maintaining a target 99th percentile latency to capture the tail latency implications of GC-induced overheads.

Significant Findings

1. Throughput and CPU Utilization: The results demonstrate that significant differences in throughput exist between languages with GC and those with manual memory management, particularly under constrained memory conditions. For update-heavy workloads and ample memory, C++ outperforms Java by approximately 1.7 times due to fewer GC-related pauses. Under more severe memory constraints, these differences become even more pronounced, with C++ maintaining throughput over nine times higher than Java.
2. Memory and CPU Constraints: The paper reveals that as memory constraints tighten, languages with GC, such as Java and Go, experience substantial declines in throughput because of increased GC activity, leading to processor utilization being squandered on memory management. In extreme cases, Go struggles to maintain target tail latencies, highlighting the profound impact of memory allocation and GC frequency on system performance.
3. Virtualization Overhead: Interestingly, the paper finds that virtualization overhead affects languages differently; C++ experiences a more pronounced drop in throughput under virtualized conditions compared to Go and Rust, suggesting that language choice and threading models may interact with modern virtualized cloud infrastructure in complex, non-intuitive ways.
4. Resource Efficiency: Rust exhibits notable efficiency, combining manual memory management with advanced compilation checks for memory safety without a GC. This enables higher throughput and resource efficiency, even when contrasting with Go’s user-mode threading advantages.

Practical and Theoretical Implications

From a practical standpoint, this paper provides quantifiable evidence that the choice of programming language, specifically regarding memory management paradigms, can significantly impact long-term operational costs in cloud deployments. For businesses anticipating substantial growth, investing in systems developed in languages with manual memory management could lead to significant cost savings due to reduced resource allocations for achieving similar or higher performance levels as languages with automatic memory management.

Theoretically, the findings underscore the importance of aligning language features and runtime strategies with application performance goals within cloud environments. The discussion reinforces considerations around memory management and GC's intrinsic trade-offs between developer productivity and runtime efficiency, emphasizing the need for nuanced decision-making in language selection for system-intensive applications.

Future Directions

The insights from this work suggest several avenues for future research. Further examination of the interplay between modern serverless or function-as-a-service environments, where ephemeral compute resources amplify GC's effect on system latency and cost, could be invaluable. Additionally, exploring hybrid approaches that balance automation with manual optimization might yield strategies that leverage the benefits of both paradigms, minimizing their respective drawbacks.

In conclusion, this work contributes substantially to the understanding of language-driven performance variances in cloud-based SMR systems, offering a robust framework for evaluating the cost and performance trade-offs that accompany GC in distributed software architectures.