云计算中数据驱动调试和决策的因果推理
Causal inference for data-driven debugging and decision making in cloud computing
摘要：
云计算涉及复杂的技术和经济系统和交互。这带来了各种挑战，其中两个是：（1）在沙箱实验的帮助下，调试和控制以优化计算系统的性能，以及（2）对用于云客户端决策的“点”资源的成本的隐私保护预测。在本文中，我们正式调试反事实的概率和控制后（软）干预概率。我们证明了反事实可以近似地从“随机”图形因果模型中计算出来（而它们最初只定义为“确定性”功能因果模型），并基于这个草图，提出了一种数据驱动的方法来解决问题（1）。为了解决问题（2），我们通过后（软）干预概率形式化投标，并提出了一个简单的数学结果近似积分的“不完全”的条件概率分布。我们展示了云客户端如何使用这一点来权衡隐私与玩具场景中投标行为结果的可预测性。我们报告模拟和真实数据的实验。
英文摘要：
Cloud computing involves complex technical and economical systems and interactions. This brings about various challenges, two of which are: (1) debugging and control to optimize the performance of computing systems, with the help of sandbox experiments, and (2) privacy-preserving prediction of the cost of ``spot'' resources for decision making of cloud clients. In this paper, we formalize debugging by counterfactual probabilities and control by post-(soft-)interventional probabilities. We prove that counterfactuals can approximately be calculated from a ``stochastic'' graphical causal model (while they are originally defined only for ``deterministic'' functional causal models), and based on this sketch a data-driven approach to address problem (1). To address problem (2), we formalize bidding by post-(soft-)interventional probabilities and present a simple mathematical result on approximate integration of ``incomplete'' conditional probability distributions. We show how this can be used by cloud clients to trade off privacy against predictability of the outcome of their bidding actions in a toy scenario. We report experiments on simulated and real data.
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