PBench: Workload Synthesizer with Real Statistics for Cloud Analytics Benchmarking

TL;DR

PBench is a workload synthesizer that generates synthetic cloud analytics workloads closely matching real execution statistics, using multi-objective optimization, timestamp refinement, and LLM-based component augmentation.

Contribution

This paper introduces PBench, a novel workload synthesis approach that incorporates real workload statistics and leverages large language models for improved fidelity.

Findings

Reduces approximation error by up to 6x compared to existing methods

Effectively captures performance metrics and operator distributions

Improves workload realism for benchmarking cloud analytics systems

Abstract

Cloud service providers commonly use standard benchmarks like TPC-H and TPC-DS to evaluate and optimize cloud data analytics systems. However, these benchmarks rely on fixed query patterns and fail to capture the real execution statistics of production cloud workloads. Although some cloud database vendors have recently released real workload traces, these traces alone do not qualify as benchmarks, as they typically lack essential components like the original SQL queries and their underlying databases. To overcome this limitation, this paper introduces a new problem of workload synthesis with real statistics, which aims to generate synthetic workloads that closely approximate real execution statistics, including key performance metrics and operator distributions, in real cloud workloads. To address this problem, we propose PBench, a novel workload synthesizer that constructs synthetic workloads by judiciously selecting and combining workload components (i.e., queries and databases) from existing benchmarks. This paper studies the key challenges in PBench. First, we address the challenge of balancing performance metrics and operator distributions by introducing a multi-objective optimization-based component selection method. Second, to capture the temporal dynamics of real workloads, we design a timestamp assignment method that progressively refines workload timestamps. Third, to handle the disparity between the original workload and the candidate workload, we propose a component augmentation approach that leverages large language models (LLMs) to generate additional workload components while maintaining statistical fidelity. We evaluate PBench on real cloud workload traces, demonstrating that it reduces approximation error by up to 6x compared to state-of-the-art methods.