分布式系统中的自愈困境：故障校正与容错
Self-healing Dilemmas in Distributed Systems: Fault Correction vs. Fault Tolerance
摘要：
大规模分散的自主智能体通过异步通信交互的系统通常会遇到以下自愈困境：故障检测继承网络的不确定性，使远程故障过程无法区分从一个缓慢的过程。在没有故障的缓慢过程的情况下，错误校正是不期望的，因为它可能触发新的故障，而这些新的故障可以通过作为更主动的系统维护的容错来防止。但在实际故障过程的情况下，仅容错而不最终纠正持续性故障会使系统表现不佳。衡量、理解和解决这种自我修复的困境是一个及时的挑战和关键要求，因为分布式账本、边缘计算、物联网在能源、交通和健康等领域的应用正在兴起。本文贡献了一种新的和通用的建模故障场景在系统运行时。它们被用来准确地测量和预测由错误校正和容错的不良结果所产生的不一致性，作为在设计阶段改善大规模分散系统的自愈的手段。一个严格的实验方法的设计，评估696个实验设置不同的故障规模，故障配置文件和故障检测阈值在一个原型的分散网络的3000个节点。在一个网络中收集了近900万个不一致的测量值，其中每个节点都监控另一个节点的健康状态，而两者都可以缺陷。模型化的故障场景的预测性能进行了验证，在一个具有挑战性的应用场景中的分散和动态的网络内数据聚合，使用真实世界的数据从智能电网试点项目。调查结果证实了在设计阶段的不一致的起源。
英文摘要：
Large-scale decentralized systems of autonomous agents interacting via asynchronous communication often experience the following self-healing dilemma: fault detection inherits network uncertainties making a remote faulty process indistinguishable from a slow process. In the case of a slow process without fault, fault correction is undesirable as it can trigger new faults that could be prevented with fault tolerance that is a more proactive system maintenance. But in the case of an actual faulty process, fault tolerance alone without eventually correcting persistent faults can make systems underperforming. Measuring, understanding and resolving such self-healing dilemmas is a timely challenge and critical requirement given the rise of distributed ledgers, edge computing, the Internet of Things in several energy, transport and health applications. This paper contributes a novel and general-purpose modeling of fault scenarios during system runtime. They are used to accurately measure and predict inconsistencies generated by the undesirable outcomes of fault correction and fault tolerance as the means to improve self-healing of large-scale decentralized systems at the design phase. A rigorous experimental methodology is designed that evaluates 696 experimental settings of different fault scales, fault profiles and fault detection thresholds in a prototyped decentralized network of 3000 nodes. Almost 9 million measurements of inconsistencies were collected in a network, where each node monitors the health status of another node, while both can defect. The prediction performance of the modeled fault scenarios is validated in a challenging application scenario of decentralized and dynamic in-network data aggregation using real-world data from a Smart Grid pilot project. Findings confirm the origin of inconsistencies at design phase.
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