VDTuner: a machine learning-based automatic performance tuning system for vector data management systems (VDMS)

Big language models (BLMs) have been the key to the extraordinary growth of artificial intelligence (AI) technologies. To solve problems such as conversational hallucinations, these models are increasingly being used in a variety of situations where unstructured multimedia data is converted into embedding vectors. Vector Data Management Systems (VDMS) are specifically designed to efficiently manage these vectors. Platforms such as Qdrant and Milvus have created significant user bases and lively communities serving as the backbone of the LLM era.

LLM and other machine learning and information retrieval systems rely heavily on vector data management systems. These systems rely on efficient similarity search, which is made possible by VDMSs that provide users with the ability to define a variety of customizable indexes and system parameters. Nevertheless, the intrinsic complexity of VDMSs poses significant obstacles to automatic performance optimization that existing methods find it difficult to sufficiently address.

As a solution to these problems, the research team presented VDTuner, a learning-based automatic performance tuning system designed specifically for VDMS. Without requiring users to have prior knowledge, VDTuner efficiently navigates the complex multidimensional parameter space of VDMS using multi-object Bayesian optimization. It also strikes a fine balance between memorization speed and search speed, creating an ideal configuration that improves overall performance.

The team shared that various evaluations have shown the effectiveness of VDTuner. Compared to the default settings, it significantly improves the performance of VDMS by increasing the search speed by 14.12% and the memorization rate by 186.38%. VDTuner achieves 3.57 times higher tuning efficiency compared to the latest base versions. It provides scalability to meet individual user preferences and optimize budget goals.

The team articulated their main contribution as follows.

1. To identify the main difficulties in fine-tuning vector data management systems, an extensive study was conducted. The team studied the shortcomings of existing VDMS customization options, which allowed to get a complete picture of the current state of affairs in this area.
2. Introduced VDTuner, a unique performance tuning framework designed for VDMS. Using multi-objective Bayesian optimization, VDTuner efficiently explores the complex VDMS parameter space to determine the ideal tuning. This strategy addresses an important requirement in VDMS optimization by optimizing the search rate and the memoization rate simultaneously.
3. To confirm the effectiveness of VDTuner, thorough research has been conducted to show that VDTuner performs much better than all existing base versions. In-depth research has also been conducted to understand the elements that influence its effectiveness and offer opinions on its exceptional performance.

In conclusion, VDTuner is a great step forward in automatic VDMS performance tuning and a powerful tool to improve the efficiency and performance of your systems.