POLUS: A self-evolving model-based approach for automating the observe-analyze-act loop

Computer systems today are managed by human administrators who are required to continuously observe the system, analyze its behavior, and activate corrective actions (generally referred to as the Observe-Analyze-Act loop). Automating the OAA loop within real-world systems is a non-trivial problem, but the growing economic incentive associated with making systems self-managing; and significant increase in the computation bandwidth have made OAA automation a promising area of research. The existing choices for OAA automation can be characterized as one of the following: policy-based, feedback-based, empirical or learning based, and model-based---the available solutions suffer from complexity, brittleness, slow convergence, and have been useful to automate only trivial management scenarios.; This thesis proposes POLUS: a methodology for OAA automation using a model-based approach with integrated learning and feedback . POLUS uses models of system behavior for deciding the corrective action to be invoked---it continuously refines models using monitor data, exhaustively searches for an optimal corrective action using constrained optimization, and executes the selected action using a variably aggressive feedback loop. The core architecture of POLUS closely resembles that of an Expert System: A Knowledge-base of models for components, workloads, actions, and a Reasoning engine that selects and executes a "feasible" action at run-time.; The details of the POLUS methodology consist of: Representation of domain-specific details as models; creation and evolution of these models in an automated fashion; decision-making for the corrective action(s) to be invoked at run-time; handling divergent system behavior during action execution. POLUS is the first-of-a-kind in using a model-based approach for OAA automation; by applying the following operational principles. P OLUS addresses challenges related to model inaccuracies in real-world systems, and the computational complexity of decision-making: (1) Models don't need to be perfectly accurate---they only need to be accurate enough to maintain the relative ordering during action selection; (2) The objective of action selection is not to find the most optimal one, but rather to avoid the worst ones; (3) Creation of models is not a one-time activity---it is a continuous process over the lifetime of the system. (Abstract shortened by UMI.)...