XPlain帮助运维人员理解启发式算法失效的时机与原因。
Towards Safer Heuristics With XPlain
- 通过分析输入实例，定位启发式算法失效的完整集合。
- 揭示启发式算法在特定场景下性能下降的根本原因。
- 适合需要优化系统性能的运维工程师使用。
许多云运维问题计算成本高昂，运维人员常采用更快、可扩展性更好的启发式算法来高效解决。现有启发式分析工具能发现导致启发式算法性能下降的单一输入实例，但无法提供足够细节以实际缓解问题：既未揭示全部导致失效的输入集合，也未能解释失效原因。本文提出 XPlain，一种扩展现有分析工具的方法，帮助运维人员理解启发式算法在何时以及为何会表现不佳。初步结果显示该方法具有可行性。
原文摘要 · Abstract (English)
Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them more efficiently. Heuristic analyzers enable operators to find when and by how much their heuristics underperform. However, these tools do not provide enough detail for operators to mitigate the heuristic's impact in practice: they only discover a single input instance that causes the heuristic to underperform (and not the full set), and they do not explain why. We propose XPlain, a tool that extends these analyzers and helps operators understand when and why their heuristics underperform. We present promising initial results that show such an extension is viable.
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