A Reliability Optimization Framework for Public Cloud Services based on Markov Process and Hierarchical Correlation Modelling
Sa Meng, Liang Luo, Xiwei Qiu, Peng Sun · 2021
With the advancement of IoT and Smart City, public cloud computing systems are required to be powerful in data processing and be dependable as a service provider. Thus, reliability analysis of cloud computing systems has been widely investigated but far from being solved. Reliability of the public cloud computing system is indeed affected by many factors, such as service performance, system energy consumption. Researchers can analysis such important correlation to find correlation factors that can cause significant changes in the correlation, and further optimize those correlation factors dynamically and intelligently. This would be an effective approach to improve the reliability of the public cloud system. This paper tries to establish a Reliability analysis framework covering four levels, i.e., component, system, mission and data, by using of Markov process and hierarchical correlation modelling. Numerical results indicate that the proposed methods improve the reliability by reliability planning, optimizes energy utilization, and uses stand-by policies.