Cost Risk Analysis for Instance Recommendation in a Sustainable Cloud-CPS Framework

   @article{https://doi.org/10.1002/spe.2919,
       author = {Jiang, Wenjing and Xu, Zichen and Gao, Cuiying and Gu, Jingyun and Wang, Yuhao},
       title = {Cost risk analysis for instance recommendation in a sustainable Cloud-cyber-physical system framework},
       journal = {Software: Practice and Experience},
       volume = {51},
       number = {11},
       pages = {2317-2336},
       keywords = {IaaS in CPS, online optimization, performance modeling, risk inference},
       doi = {https://doi.org/10.1002/spe.2919},
       url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/spe.2919},
       eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/spe.2919},
       abstract = {Abstract 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.},
       year = {2021}
   }