Abstract
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 computing systems with the help of sandbox experiments, and (2) 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 an 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.
Abstract (translated)
云计算涉及复杂的技术和经济系统以及交互。这带来了各种各样的挑战，其中两个挑战是：（1）借助沙盒实验调试和控制优化计算系统；（2）预测云客户端决策所需的“现货”资源成本。本文采用反事实概率形式化调试，采用后（软）干预概率形式化控制。我们证明反事实可以近似地从“随机”图形因果模型（虽然它们最初只是为“确定性”功能因果模型定义的）中计算出来，并在此基础上提出了一种解决问题的方法（1）。为了解决问题（2），我们采用后（软）介入概率形式化投标，并给出了一个简单的关于“不完全”条件概率分布近似积分的数学结果。我们展示了云客户如何利用这一点来权衡隐私权与在玩具场景中他们投标行为结果的可预测性。我们报告了模拟和真实数据的实验。
URL
https://arxiv.org/abs/1603.01581
