GROOT: General-Purpose Automatic Parameter Tuning Across Layers, Domains, and Use Cases

2025-11-22

AI Summary

Modern software systems suffer performance degradation and increased operational costs due to suboptimal parameter configurations across multi-layer runtime stacks—including virtualization, storage, and trusted execution environments (TEEs). Existing tuning tools are typically domain-specific, single-layer, or constrained to fixed optimization objectives, rendering them ill-suited for startups and innovative ventures (SIVs) with resource constraints, customized technology stacks, and limited expert expertise. This paper introduces the first general-purpose, cross-layer, cross-domain, multi-objective parameter tuning framework. It requires no prior knowledge, supports black-box evaluation and incremental optimization, and synergistically integrates Bayesian optimization, multi-objective evolutionary algorithms, and meta-learning to dynamically model parameter–performance relationships in heterogeneous environments. Evaluated on real-world deployments and standard benchmarks, the framework consistently improves performance, reduces resource consumption, and demonstrates strong generalizability and deployment robustness.

Technology Category

• Machine Learning: Auto ML and Hyperparameter Tuning
• Search and Optimization: Metareasoning and Metaheuristics
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Modern software systems are executed on a runtime stack with layers (virtualization, storage, trusted execution, etc.) each incurring an execution and/or monetary cost, which may be mitigated by finding suitable parameter configurations. While specialized parameter tuners exist, they are tied to a particular domain or use case, fixed in type and number of optimization goals, or focused on a specific layer or technology. These limitations pose significant adoption hurdles for specialized and innovative ventures (SIVs) that address a variety of domains and use cases, operate under strict cost-performance constraints requiring tradeoffs, and rely on self-hosted servers with custom technology stacks while having little data or expertise to set up and operate specialized tuners. In this paper, we present Groot - a general-purpose configuration tuner designed to a) be explicitly agnostic of a particular domain or use case, b) balance multiple potentially competing optimization goals, c) support different custom technology setups, and d) make minimal assumptions about parameter types, ranges, or suitable values. Our evaluation on both real-world use cases and benchmarks shows that Groot reliably improves performance and reduces resource consumption in scenarios representative for SIVs.

Problem

• Automatically tunes parameters across software stack layers
• Balances multiple competing optimization goals simultaneously
• Supports diverse technology setups without domain restrictions

Innovation

• General-purpose configuration tuner for multiple domains
• Balances multiple competing optimization goals
• Supports diverse custom technology setups