How Can We Trust an Autonomic System to Make the Best Decision?

Autonomic computing has gained widespread attention over the last few years for its vision of developing applications with autonomic or self-managing behaviors (Kephart and Chess, 2003). New approaches to the design and implementation of autonomic systems have emerged, including the use of goal policies (Kephart and Walsh,2004), utility functions, intelligent monitoring, data mining, reinforcement learning, and planning. Unfortunately, these new approaches do nothing to reduce administrators´ skepticism towards automation - how is an administrator to believe that an autonomic system will help his systems perform better? In this report, we describe an approach by which an autonomic system can win the trust of its users, and can continuously adjust itself to make better decisions based on the users´ preferences