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

TL;DR

WiSeDB is a learning-based framework that optimizes workload management in cloud databases by generating adaptable, cost-effective decision models for query placement, scheduling, and resource provisioning tailored to specific performance goals.

Contribution

It introduces a holistic, learning-based approach that integrates workload management tasks and adapts to different performance goals with minimal retraining.

Findings

Low training overhead demonstrated in experiments

Effective cost reduction across various performance goals

Flexible adaptation to different workload characteristics

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.