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arXiv · 1601.08221

WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases

Ryan Marcus·Olga Papaemmanouil
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Abstract

Workload management for cloud databases must deal with the tasks of resource provisioning, query placement and query scheduling in a manner that meets the application's performance goals while minimizing the cost of using cloud resources. Existing solutions have approached these three challenges in isolation, and with only a particular type of performance goal in mind. In this paper, we introduce WiSeDB, a learning-based framework for generating holistic workload management solutions customized to application-defined performance metrics and workload characteristics. Our approach relies on supervised learning to train cost-effective decision tree models for guiding query placement, scheduling, and resource provisioning decisions. Applications can use these models for both batch and online scheduling of incoming workloads. A unique feature of our system is that it can adapt its offline model to stricter/looser performance goals with minimal re-training. This allows us to present alternative workload management strategies that address the typical performance vs. cost trade-off of cloud services. Experimental results show that our approach has very low training overhead while offering low cost strategies for a variety of performance goals and workload characteristics.

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Ryan Marcus, Olga Papaemmanouil. 2016-04-30. WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases. https://doi.org/10.14778/2977797.2977804

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