Abstract
Automatically tuning software configuration for optimizing a single performance attribute (e.g., minimizing latency) is not trivial, due to the nature of the configuration systems (e.g., complex landscape and expensive measurement). To deal with the problem, existing work has been focusing on developing various effective optimizers. However, a prominent issue that all these optimizers need to take care of is how to avoid the search being trapped in local optima — a hard nut to crack for software configuration tuning due to its rugged and sparse landscape, and neighboring configurations tending to behave very differently. Overcoming such in an expensive measurement setting is even more challenging. In this paper, we take a different perspective to tackle this issue. Instead of focusing on improving the optimizer, we work on the level of optimization model. We do this by proposing a meta multi-objectivization model (MMO) that considers an auxiliary performance objective (e.g., throughput in addition to latency). What makes this model unique is that we do not optimize the auxiliary performance objective, but rather use it to make similarly-performing while different configurations less comparable (i.e. Pareto nondominated to each other), thus preventing the search from being trapped in local optima.
Experiments on eight real-world software systems/environments with diverse performance attributes reveal that our MMO model is statistically more effective than state-of-the-art single-objective counterparts in overcoming local optima (up to 42% gain), while using as low as 24% of their measurements to achieve the same (or better) performance result.
Experiments on eight real-world software systems/environments with diverse performance attributes reveal that our MMO model is statistically more effective than state-of-the-art single-objective counterparts in overcoming local optima (up to 42% gain), while using as low as 24% of their measurements to achieve the same (or better) performance result.
Original language | English |
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Title of host publication | ESEC/FSE 2021 - Proceedings of the 29th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering |
Subtitle of host publication | Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering |
Editors | Diomidis Spinellis, Georgios Gousios, Marsha Chechik, Massimiliano Di Penta |
Publisher | Association for Computing Machinery (ACM) |
Pages | 453–465 |
Number of pages | 13 |
ISBN (Electronic) | 9781450385626 |
ISBN (Print) | 9781450385626 |
DOIs | |
Publication status | Published - 20 Aug 2021 |
Event | ESEC/FSE 2021: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering - Athens, Greece Duration: 23 Aug 2021 → 28 Aug 2021 |
Publication series
Name | ACM proceedings |
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Publisher | Association for Computing Machinery (ACM) |
ISSN (Print) | 2168-4081 |
Conference
Conference | ESEC/FSE 2021 |
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Abbreviated title | ESEC/FSE 2021 |
Country/Territory | Greece |
City | Athens |
Period | 23/08/21 → 28/08/21 |
Bibliographical note
Publisher Copyright:© 2021 ACM.
Keywords
- Configuration tuning
- multi-objectivization
- performance optimization
- search-based software engineering
ASJC Scopus subject areas
- Software
- Artificial Intelligence