Policy-based tuning in autonomic database management systems using economic models

A key advantage of Autonomic Computing Systems will be the ability to manage according to business policies. Implementing this ability is not a trivial problem as there is little similarity in the metrics used for measuring database performance and business performance. These translations can be simplified, however, by having a configuration model that reflects the business policies. Economic models allow for a system that mirrors the types of policies used to define performance in a business.;In this dissertation, we utilize an economic model to address the buffer pool sizing problem in DBMSs. We use this context to implement importance as a parameter for resource allocation. We investigate a number of meanings for importance and identify how this additional information can best be used in the allocation of main memory.;One such business policy comes from using Value Based Management [31], in which a manager is able to define the business units that are most important when it comes to allocation of capital resources. This concept can be applied to a Database Management System (DBMS) running multiple workloads corresponding to different business units. Importance information can be utilized in making resource allocation decisions, such as allocating buffer space.