Dynamic Adaptation of Policies Using Machine Learning

Managing large systems in order to guarantee certain behavior is a difficult problem due to their dynamic behavior and complex interactions. Policies have been shown to provide a very expressive and easy way to define such desired behaviors, mainly because they separate the definition of desired behavior from the enforcement mechanism, allowing either one to be changed fairly easily. Unfortunately, it is often difficult to define policies in terms of attributes that can be measured and/or directly controlled, or to set adaptable (i.e. non-static) parameters in order to account for rapidly changing system behavior. Dynamic policies are meant to solve these problems by allowing system administrators to define higher level parameters, which are more closely related to the business goals, while providing an automated mechanism to adapt them at a lower level, where attributes can be measured and/or controlled. Here, we present a way to define such policies, and a machine learning model that is able to dynamically apply lower level static policies by learning a hidden relationship between the high level business attribute space, and the low level monitoring space. We show that this relationship exists, and that we can learn it producing an error of at most 8.78% at least 96% of the time.