MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems
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DOI：
10.1145/3448016.3457276
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发表时间：
2021-06
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通讯作者：
Lin Ma;William Zhang;Jie Jiao;Wuwen Wang;Matthew Butrovich;Wan Shen Lim;Prashanth Menon;Andrew Pavlo
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作者：
Lin Ma;William Zhang;Jie Jiao;Wuwen Wang;Matthew Butrovich;Wan Shen Lim;Prashanth Menon;Andrew Pavlo
众所周知，数据库管理系统 (DBMS) 很难部署和管理。自动驾驶 DBMS 的目标是通过自动管理自身来消除这些障碍。然而，实现完全自治的一个关键问题是如何预测DBMS的运行时行为和资源消耗。这些预测指导自动驾驶 DBMS 的决策组件来调整和优化系统的各个方面。我们提出了 ModelBot2 端到端框架，用于在自动驾驶 DBMS 中使用机器学习 (ML) 构建和维护预测模型。我们的方法将 DBMS 的体系结构分解为细粒度的操作单元，从而可以更轻松地估计系统对于以前从未见过的配置的行为。然后，ModelBot2 提供离线执行环境来训练系统以生成用于训练其模型的训练数据。我们将 ModelBot2 集成到内存 DBMS 中，并测量其预测动态环境中运行的 OLTP 和 OLAP 工作负载性能的能力。我们还将 ModelBot2 与最先进的 ML 模型进行了比较，结果表明我们的模型在多种场景下的准确度提高了 25 倍。
Database management systems (DBMSs) are notoriously difficult to deploy and administer. The goal of a self-driving DBMS is to remove these impediments by managing itself automatically. However, a critical problem in achieving full autonomy is how to predict the DBMS's runtime behavior and resource consumption. These predictions guide a self-driving DBMS's decision-making components to tune and optimize all aspects of the system. We present the ModelBot2 end-to-end framework for constructing and maintaining prediction models using machine learning (ML) in self-driving DBMSs. Our approach decomposes a DBMS's architecture into fine-grained operating units that make it easier to estimate the system's behavior for configurations that it has never seen before. ModelBot2 then provides an offline execution environment to exercise the system to produce the training data used to train its models. We integrated ModelBot2 in an in-memory DBMS and measured its ability to predict its performance for OLTP and OLAP workloads running in dynamic environments. We also compare ModelBot2 against state-of-the-art ML models and show that our models are up to 25x more accurate in multiple scenarios.