Autonomic workload execution control using throttling

Abstract

Database Management Systems (DBMSs) are often required to simultaneously process multiple diverse workloads while enforcing business policies that govern workload performance. Workload control mechanisms such as admission control, query scheduling, and workload execution control serve to ensure that such policies are enforced and that individual workload goals are met. Query throttling can be used as a workload execution control method whereby problematic queries are slowed down, thus freeing resources to allow the more important work to complete more rapidly. In a self-managed system, a controller would be used to determine the appropriate level of throttling necessary to allow the important workload to meet is goals. The throttling would be increased or decreased depending upon the current system performance. In this paper, we explore two techniques to maintain an appropriate level of query throttling. The first technique uses a simple controller based on a diminishing step function to determine the amount of throttling. The second technique adopts a control theory approach that uses a black-box modelling technique to model the system and to determine the appropriate throttle value given current performance. We present a set of experiments that illustrate the effectiveness of each controller, then propose and evaluate a hybrid controller that combines the two techniques.