A Control Theory Foundation for Self-Managing Computing Systems

The high cost of operating large computing installations has motivated a broad interest in reducing the need for human intervention by making systems self-managing. This paper explores the extent to which control theory can provide an architectural and analytic foundation for building self-managing systems. Control theory provides a rich set of methodologies for building automated self-diagnosis and self-repairing systems with properties such as stability, short settling times, and accurate regulation. However, there are challenges in applying control theory to computing systems, such as developing effective resource models, handling sensor delays, and addressing lead times in effector actions. We propose a deployable testbed for autonomic computing (DTAC) that we believe will reduce the barriers to addressing research problems in applying control theory to computing systems. The initial DTAC architecture is described along with several problems that it can be used to investigate

Discovering Indicators for Congestion in DBMSs

Abstract-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

Managing the Performance Impact of Administrative Utilities

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