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Outlier Detection for Fine-grained Load Balancing in Database Clusters

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4 Author(s)
Jin Chen ; Department of Computer Science, University of Toronto. jinchen@cs.toronto.edu ; Gokul Soundararajan ; Madalin Mihailescu ; Cristiana Amza

Recent industry trends towards reducing the costs of ownership in large data centers emphasize the need for database system techniques for both automatic performance tuning and efficient resource usage. The goal is to host several database applications on a shared server farm, including scheduling multiple applications on the same physical server or even within a single database engine, while meeting each application's service level agreement. Automatic provisioning of database servers to applications and virtualization techniques, such as, live virtual machine migration have been proposed as useful tools to address this problem. In this paper we argue that by allocating entire server-boxes and migrating entire application stacks in cases of server overload, these solutions are too coarse-grained for many overload situations. Hence, they may result in resource usage inefficiency, performance penalties, or both. We introduce an outlier detection algorithm which zooms in to the fine-grained query contexts which are most affected by an environment change and/or where a perceived overload problem is likely to originate from. We show that isolating these query contexts through either memory quota enforcements or fine-grained load balancing across different database replicas of their respective applications allows us to alleviate resource interference in many cases of overload.

Published in:

Data Engineering Workshop, 2007 IEEE 23rd International Conference on

Date of Conference:

17-20 April 2007