Beelog: Online Log Compaction for Dependable Systems

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Luiz Gustavo C. Xavier;Cristina Meinhardt;Odorico Machado Mendizabal
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引用次数: 0

Abstract

Logs are a known abstraction used to develop dependable and secure distributed systems. By logging entries on a sequential global log, systems can synchronize updates over replicas and provide a consistent state recovery in the presence of faults. However, their usage incurs a non-negligible overhead on the application's performance. This article presents Beelog, an approach to reduce logging impact and accelerate recovery on log-based protocols by safely discarding entries from logs. The technique involves executing a log compaction during run-time concurrently with the persistence and execution of commands. Besides compacting logging information, the proposed technique splits the log file and incorporates strategies to reduce logging overhead, such as batching and parallel I/O. We evaluate the proposed approach by implementing it as a new feature of the etcd key-value store and comparing it against etcd's standard logging. Utilizing workloads from the YCSB benchmark and experimenting with different configurations for batch size and number of storage devices, our results indicate that Beelog can reduce application recovery time, especially in write-intensive workloads with a small number of keys and a probability favoring the most recent keys to be updated. In such scenarios, we observed up to a 50% compaction in the log file size and a 65% improvement in recovery time compared to etcd's standard recovery protocol. As a side effect, batching results in higher command execution latency, ranging from $ \text{100 ms}$ to $ \text{350 ms}$ with Beelog, compared to the default etcd's $ \text{90 ms}$. Except for the latency increase, the proposed technique does not impose other significant performance costs, making it a practical solution for systems where fast recovery and reduced storage are priorities.
日志是一种已知的抽象概念,用于开发可靠、安全的分布式系统。通过在顺序全局日志中记录条目,系统可以在副本上同步更新,并在出现故障时提供一致的状态恢复。然而,使用全局日志会给应用程序的性能带来不可忽略的开销。本文介绍的 Beelog 是一种通过安全地丢弃日志条目来减少日志影响并加速基于日志协议的恢复的方法。该技术包括在运行时与命令的持久化和执行同时执行日志压缩。除了压缩日志信息外,该技术还能分割日志文件,并采用批处理和并行 I/O 等策略来减少日志开销。我们将所提出的方法作为 etcd 键值存储的一项新功能加以实施,并与 etcd 的标准日志记录进行比较,从而对其进行评估。我们利用 YCSB 基准的工作负载,并尝试了不同的批处理大小和存储设备数量配置,结果表明 Beelog 可以缩短应用程序的恢复时间,尤其是在键数较少且可能偏向于更新最新键的写密集型工作负载中。在这种情况下,与 etcd 的标准恢复协议相比,我们观察到日志文件大小压缩了 50%,恢复时间缩短了 65%。作为副作用,批处理会导致更高的命令执行延迟,与 etcd 默认的 $ \text{90 ms}$ 相比,Beelog 的延迟从 $ \text{100 ms}$ 到 $ \text{350 ms}$ 不等。除了延迟增加外,所提出的技术不会带来其他显著的性能代价,因此对于优先考虑快速恢复和减少存储的系统来说,它是一种实用的解决方案。
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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