Cloud markets advocate powerful instances to take computation over from the cyber-physical system (CPS). Combining the Cloud and CPS layer, the whole Cloud–CPS framework is designed to achieve both accurate data sensing and fast data analysis. While most researchers trust the computation side, and focus on the actuator in the physical space to ensure the service-level objectives, SLO, that is, deadline misses, cloud can be a threat to the service sustainability as instance may fail, especially when one tries to make a cost-effective design. Specifically, users must bear the risk of instance failure. These risks can cause the entire cyber-physical system to collapse. Our work tackles the cloud aspect of the sustainability challenge from the cloud side in a cloud–CPS framework. We have studied the instance selection problem for the CPS systems, and propose a Cost-Risk Analysis for Instance Recommendation, or CRAIR, to support a sustainable Cloud–CPS framework. We have adopted the classic risk analysis process from the portfolio management in hedge financial market, combining with the system modeling for the CPS instance selection, as an optimization problem. To solve this problem, we formulate it as a multi-armed bandit problem and solve it with our upper confidence bound bandit algorithm together, our CRAIR can provide an online risk analysis to maximize the profit with a comparative ratio of O(1+). We have evaluated CRAIR based on simulations using real-world Google and Alibaba workloads and cloud market numbers. The results show that, compared to traditional approaches, our approach provides the best tradeoff between SLOs and costs. All users achieve their SLOs goals while minimizing their average expenses by 34.6%. By using CRAIR for instance selection, the CPS service can maximize its benefit under a controlled risk.

中文翻译：

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可持续云-网-物理系统框架中实例推荐的成本风险分析

云市场提倡强大的实例来接管网络物理系统 (CPS) 的计算。结合 Cloud 和 CPS 层，整个 Cloud-CPS 框架旨在实现准确的数据感知和快速的数据分析。虽然大多数研究人员信任计算方面，并专注于物理空间中的执行器以确保服务级别目标，SLO，即最后期限错过，但云可能对服务可持续性构成威胁，因为实例可能会失败，尤其是当一个试图做出具有成本效益的设计。具体来说，用户必须承担实例失败的风险。这些风险可能导致整个网络物理系统崩溃。我们的工作在云-CPS 框架中从云方面解决了可持续性挑战的云方面问题。我们研究了 CPS 系统的实例选择问题，并提出实例推荐的成本风险分析或 CRAIR，以支持可持续的 Cloud-CPS 框架。我们采用了对冲金融市场投资组合管理中的经典风险分析过程，结合系统建模对CPS实例的选择，作为一个优化问题。为了解决这个问题，我们将其表述为多臂老虎机问题，并与我们的上置信边界老虎机算法一起解决，我们的 CRAIR 可以提供在线风险分析，以 O(1+ 作为优化问题。为了解决这个问题，我们将其表述为多臂老虎机问题，并与我们的上置信边界老虎机算法一起解决，我们的 CRAIR 可以提供在线风险分析，以 O(1+ 作为优化问题。为了解决这个问题，我们将其表述为多臂老虎机问题，并与我们的上置信边界老虎机算法一起解决，我们的 CRAIR 可以提供在线风险分析，以 O(1+）。我们已经使用真实世界的谷歌和阿里巴巴工作负载以及云市场数据基于模拟对 CRAIR 进行了评估。结果表明，与传统方法相比，我们的方法提供了 SLO 和成本之间的最佳权衡。所有用户都实现了他们的 SLO 目标，同时将他们的平均费用降低了 34.6%。通过使用 CRAIR 进行实例选择，CPS 服务可以在受控风险下最大化其收益。