Automatic Parameter Configuration for Cloud Infrastructures by Design Pattern Extraction

Large-scale cloud data centers have a great number of configuration parameters, and as such it is difficult for administrators to configure these parameters correctly. Proposals have been made for approaches to automatic parameter configuration focusing mainly on the identification of common configurations in existing infrastructures as "common design patterns" and applying these patterns to the development of new cloud infrastructure. However, these methods still require manual configuration for the areas which do not have common design patterns, and manual configuration must be reduced as far as possible because misconfiguration is one of the most dominant causes of service failures. Taking this background into account, here we propose an automatic configuration method which reduces manual configuration by identifying "incremental design patterns" which are linear relationships between generations of existing infrastructures (the time the infrastructures were constructed) and the values assigned to the parameters. Furthermore, we construct a parameter configuration procedure by ordering the application of design patterns. For the design patterns that are interdependent, we resolve them by recursively applying decision tree analysis. By using these design patterns and application procedure, we can reduce the manual configuration that is necessary. We evaluated our method in actual cloud infrastructures and confirmed that the proposed approach could configure 91.3% of parameters in new infrastructure automatically.