Eos: An Approach of Using Behavior Implications for Policy-Based Self-Management

Systems are becoming exceedingly complex to manage. As such, there is an increasing trend towards developing systems that are self-managing. Policy-based infrastructures have been used to provide a limited degree of automation, by associating actions to system-events. In the context of self-managing systems, the existing policy-specification model fails to capture the following: a) The impact of a rule on system behavior (behavior implications). This is required for automated decision-making. b) Learning mechanisms for refining the invocation heuristics by monitoring the impact of rules. This paper proposes Eos; An approach to enhance the existing policybased model with behavior implications. The paper gives details of the following aspects: • Expressing behavior implications. • Using behavior implications of a rule for learning and automated decision-making. • Enhancing existing policy-based infrastructures to support selfmanagement using Eos. The paper also describes an example of using Eos for self-management within a distributed file-system.