Characterizing problems for realizing policies in self-adaptive and self-managing systems

Self-adaptive and self-managing systems optimize their own behaviour according to high-level objectives and constraints. One way for administrators to specify goals for such optimization problems effectively is using policies. Over the past decade, researchers produced various approaches, models and techniques for policy specification in different areas including distributed systems, communications networks, web services, autonomic computing, and cloud computing. Research challenges range from characterizing policies for ease of specification in particular application domains to categorizing policies for achieving solution qualities for particular algorithmic techniques. The contributions of this paper are threefold. Firstly, we give a mathematical formulation for each of the three policy types, action, goal and utility function policies, introduced in the policy framework by Kephart and Walsh. In particular, we introduce a first precise characterization of goal policies for optimization problems. Secondly, this paper introduces a mathematical framework that adds structure to the underlying optimization problem for different types of policies. Structure is added either to the objective function or the constraints of the optimization problem. These mathematical structures, imposed on the underlying problem, progressively increase the quality of the solutions obtained when using the greedy optimization technique. Thirdly, we show the applicability of our framework by analyzing several optimization problems encountered in self-adaptive and selfmanaging systems, such as resource allocation, quality of service management, and SLA profit optimization to provide quality guarantees for their solutions. Our approach is based on the algorithmic frameworks by Edmonds, Fisher et al., and Mestre, and the policy framework of Kephart and Walsh. Our characterization and approach will help designers of self-adaptive and self-managing systems formulate optimization problems, decide on algorithmic strategies based on policy requirements, and reason about solution qualities.