Causal inference for data-driven debugging and decision making in cloud computing
This work addresses specific technical and economic problems in cloud computing, such as system debugging and client bidding decisions, but appears incremental as it builds on existing causal inference methods.
The paper tackles two challenges in cloud computing: debugging and control for system optimization using sandbox experiments, and privacy-preserving cost prediction for spot resources to aid client decision-making, by formalizing these problems with causal inference concepts and demonstrating approaches through experiments on simulated and real data.
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.