分布式自稳定程序中故障表征的高效启发式与机器学习方法
Efficient Heuristics and Machine Learning Approach for Fault Characterization in Distributed Self-Stabilizing Programs
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中文总结 AI 辅助
本文针对分布式自稳定程序中的一致性违反故障，提出基于冲突边的状态空间探索与机器学习两种互补方法，以高效表征故障发生位置，提升系统正确性与计算效率。
中文摘要 AI 辅助
现代大规模系统依赖分布式协议来最大化效率，同时保持单进程执行的正确性保证。然而，设计此类协议并非易事：向系统添加资源本质上会增加其复杂性，这反过来又引入了必须在设计阶段解决的故障。在采用分布式共享内存（例如，复制数据库）的系统中出现的一类此类故障是一致性违反故障（cvf），即访问共享内存的进程读取到先前由另一进程写入的陈旧数据的故障。cvf 是那些优先考虑可用性而非严格一致性的系统所固有的。先前的工作表明，只要此类故障保持在可容忍的界限内，自稳定程序在高可用性下仍然保持正确。因此，表征自稳定程序在存在 cvf 时的行为可以帮助系统设计者构建既正确又高效的系统。在本文中，我们研究在 cvf 下的自稳定程序，以理解此类故障何时何地发生，从而启用能够提高计算效率的运行时措施。由于穷举状态空间探索不可扩展，它遭受状态空间爆炸问题；我们的研究调查了两种互补的方法：一种用于单调稳定 $({\Delta} + 1)$-图着色程序的基于冲突的状态空间探索技术，以及一种用于最大独立集程序的基于机器学习的方法。我们提出了一种基于冲突边的状态空间探索算法，以及一个经过严格验证的 ML 模型，该模型在分布外数据集上通过针对已知图不变量的结构一致性检查进行评估。
英文摘要
Modern large-scale systems rely on distributed protocols to maximize efficiency while preserving the correctness guarantees of single-process execution. However, designing such protocols is non-trivial: adding resources to a system inherently increases its complexity, which in turn introduces faults that must be addressed during design. One such fault class, arising in systems that use distributed shared memory (e.g., replicated databases), is consistency violating fault (cvf), a fault in which a process accessing shared memory reads stale data previously written by another process. Cvfs are inherent to systems that prioritize availability over strict consistency. Prior work has shown that self-stabilizing programs remain correct under high availability, provided such faults stay within a tolerable bound. Characterizing the behavior of self-stabilizing programs in the presence of cvfs can therefore help system designers build systems that are both correct and efficient. In this paper, we study self-stabilizing programs under cvfs to understand when and where such faults occur, enabling runtime measures that improve computational efficiency. Since exhaustive state-space exploration does not scale, it suffers from state-space explosion; our study investigates two complementary approaches: a conflict-based state-space exploration technique for monotonically stabilizing $(Δ + 1)$-Graph Coloring programs, and a machine-learning-based approach for a Maximal Independent Set program. We propose a conflicting-edges-based state-space exploration algorithm alongside a rigorously validated ML model, evaluated on an out-of-distribution dataset through structural consistency checks against known graph invariants.
发表机构
- University of Wyoming（怀俄明大学）
机构由 AI 辅助整理，请以论文原文为准。
