Performance is arguably the most crucial attribute that reflects the quality of a configurable
software system. However, given the increasing scale and complexity of modern software …

This paper presents MLOS (ML Optimized Systems), a flexible framework that bridges the
gap between benchmarking, experimentation, and optimization of software systems. It …

Table Question Answering (TQA) presents a substantial challenge at the intersection of
natural language processing and data analytics. This task involves answering natural …

Retrieval-augmented generation (RAG) is an umbrella of different components, design
decisions, and domain-specific adaptations to enhance the capabilities of large language …

The last few years have been marked by several breakthroughs in the domain of generative
AI. Large language models such as GPT-4 are able to solve a plethora of tasks, ranging from …

In this paper we explore the ability of Large Language Models (LLMs) in analyzing and
comparing query plans, and resolving query performance regressions. We present DBG-PT …

We demonstrate λ-Tune, a tool that leverages Large Language Models (LLMs) for
automated, workload-adaptive database system tuning. λ-Tune harnesses the ability of …

Data analysis focuses on harnessing advanced statistics, programming, and machine
learning techniques to extract valuable insights from vast datasets. An increasing volume …

Deep learning recommendation models (DLRM) rely on large embedding tables to manage
categorical sparse features. Expanding such embedding tables can significantly enhance …

A service-level objective (SLO) is a target performance metric of service that cloud vendors
aim to ensure. Delivering optimized SLOs can enhance user satisfaction and improve the …