Discovering Indicators for Congestion in DBMSs

In today´s data server environments, multiple types of workloads can be present in a system simultaneously. Workloads may have different levels of business importance and unique performance goals. An autonomic workload management system controls the flow of the workloads to help the database management system (DBMS) meet the performance goals. A task of the autonomic workload management system is to prevent congestion in the DBMS, which can result in severe degradation in overall system performance. Autonomic workload management should detect that a system is becoming congested and then act to restore normal system operation. In this paper, we describe an approach to identify a set of database monitor metrics that can serve as indicators for potential congestion in a specific scenario. We present experiments to illustrate two cases of congestion in a DB2^® DBMS and use our approach to derive the indicators.