一句话：将 Resilient X10 从故障感知推进到故障恢复，把冗余与恢复负担转移给运行时与标准库，降低程序员负担。
云计算普及了大规模内存分布式计算，MapReduce 等专用模型提供透明容错却牺牲通用性；Resilient X10 虽在通用分布式语言中引入故障遏制与感知，但冗余与恢复仍须程序员自行实现，正确性难以保证。本文从理论与实践两方面推进 Resilient X10：严格区分可恢复故障与灾难性故障，重审 happens-before 不变性及其实现，并将冗余与恢复的主要负担转移至运行时系统与标准库；通过 resilient stores 保护关键数据，利用弹性（动态 place 创建）持久化数据及其空间分布。作者构建了多个代表性高性能内存并行应用内核与框架，代码规模为以往基准的 10–25 倍，平均容错运行时开销低于 7%；与 Spark 的对比表明，其更通用的编程模型能显著提升容错内存分布式计算的性能。
Cloud computing has made the resources needed to execute large-scale in-memory distributed computations widely available. Specialized programming models, e.g., MapReduce, have emerged to offer transparent fault tolerance and fault recovery for specific computational patterns, but they sacrifice generality. In contrast, the Resilient X10 programming language adds failure containment and failure awareness to a general purpose, distributed programming language. A Resilient X10 application spans over a number of places. Its formal semantics precisely specify how it continues executing after a place failure. Thanks to failure awareness, the X10 programmer can in principle build redundancy into an application to recover from failures. In practice, however, correctness is elusive, as redundancy and recovery are often complex programming tasks. This article further develops Resilient X10 to shift the focus from failure awareness to failure recovery, from both a theoretical and a practical standpoint. We rigorously define the distinction between recoverable and catastrophic failures. We revisit the happens-before invariance principle and its implementation. We shift most of the burden of redundancy and recovery from the programmer to the runtime system and standard library. We make it easy to protect critical data from failure using resilient stores and harness elasticity—dynamic place creation—to persist not just the data but also its spatial distribution. We demonstrate the flexibility and practical usefulness of Resilient X10 by building several representative high-performance in-memory parallel application kernels and frameworks. These codes are 10× to 25× larger than previous Resilient X10 benchmarks. For each application kernel, the average runtime overhead of resiliency is less than 7%. By comparing application kernels written in the Resilient X10 and Spark programming models, we demonstrate that Resilient X10’s more general programming model can enable significantly better application performance for resilient in-memory distributed computations.
DOI 原文 ·
@article{paperbot705,
title = {Failure Recovery in Resilient X10},
author = {David Grove and Sara S. Hamouda and Benjamin Herta and Arun Iyengar and Kiyokuni Kawachiya and Josh Milthorpe and Vijay Saraswat and Avraham Shinnar and Mikio Takeuchi and Olivier Tardieu},
journal = {ACM Transactions on Programming Languages and Systems},
volume = {41},
number = {3},
year = {2019},
doi = {10.1145/3332372}
}