Adapting Multi-objectivized Software Configuration Tuning to Overcome Local Optima and Improve Efficiency

Основные понятия

The authors propose an adaptive multi-objectivization (AdMMO) method to overcome the limitations of the fixed weight in the original multi-objectivization (MMO) approach for software configuration tuning. AdMMO dynamically adjusts the weight during the tuning to maintain an appropriate proportion of unique nondominated configurations, which serves as a better indicator of the ideal balance between exploitation and exploration. Additionally, AdMMO employs a partial duplicate retention mechanism to handle the issue of too many duplicate configurations without losing the rich information provided by the "good" duplicates.

Аннотация

The content discusses the challenges in software configuration tuning, where the presence of local optima and the expensive measurement of configurations make it difficult to find the optimal configurations efficiently. To address these issues, the authors propose an adaptive multi-objectivization (AdMMO) method.

Key highlights:

1. The original multi-objectivization (MMO) approach transforms the tuning problem into a meta-objective space, where one objective optimizes the target performance while the other diversifies the auxiliary performance. However, the weight parameter in MMO is crucial but difficult to set appropriately.
2. The authors explain the role of the weight in MMO through geometric transformation and empirical evidence. The weight determines the balance between exploitation (optimizing the target performance) and exploration (diversifying the configurations) during the tuning.
3. The authors propose AdMMO, which adaptively adjusts the weight during the tuning to maintain an appropriate proportion of unique nondominated configurations, which serves as a better indicator of the ideal balance between exploitation and exploration.
4. AdMMO also includes a progressive trigger to only enable weight adaptation when necessary, and a partial duplicate retention mechanism to handle the issue of too many duplicate configurations without losing the rich information provided by the "good" duplicates.
5. Extensive experiments on 14 system-objective pairs show that AdMMO outperforms state-of-the-art optimizers, including the improved MMO, in 71% of the cases, with the best normalized improvements between 17% and 100%. AdMMO also achieves considerable speedup over others, with the best from 2.2× to 20×.
6. The authors find that around 30% is generally the best proportion of nondominated configurations to maintain in AdMMO.

Статистика

The authors report that it takes 1,536 hours to explore merely 11 options of configuration for x264.

Цитаты

"Have you ever struggled in configuring your complex software system? There is no need to worry as we have automatic software configuration tuning."
"An unpleasant issue which an optimizer faces is that the tuning may easily be trapped at local optima—some configurations that perform better than all the neighbors but being undesirably sub-optimal."
"While this issue may well be inherited and common in Search-Based Software Engineering (SBSE), there are some unique characteristics in software configuration tuning that make it particularly hard to tackle: (1) the measurement of configurations is expensive. (2) The configuration landscape is rather sparse and rugged, i.e., the close configurations can also have radically different performance."

Ключевые выводы из

Adapting Multi-objectivized Software Configuration Tuning

by Tao Chen,Miq... в arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04744.pdf

Дополнительные вопросы

How can the proposed AdMMO method be extended to handle multi-objective software configuration tuning, where there are more than two performance objectives to optimize

To extend the AdMMO method for multi-objective software configuration tuning with more than two performance objectives, we can modify the weight adaptation mechanism to handle multiple objectives. Instead of a single weight parameter, we can introduce multiple weights, each corresponding to a different performance objective. These weights can be dynamically adjusted based on the proportion of nondominated configurations for each objective. The adaptation process would involve optimizing a set of meta-objectives that combine all the performance objectives, with each meta-objective having its own weight. By adapting these weights in a coordinated manner, we can maintain a balance between exploitation and exploration for each objective, thereby escaping local optima in a multi-objective setting.

What other techniques, beyond multi-objectivization, can be explored to effectively escape local optima in software configuration tuning

Beyond multi-objectivization, there are several techniques that can be explored to effectively escape local optima in software configuration tuning:

Ensemble Methods: Combining multiple optimization algorithms or strategies to leverage their strengths and mitigate their weaknesses. By using an ensemble of optimizers, we can improve the robustness of the tuning process and increase the chances of finding better configurations.
Constraint Handling: Incorporating constraints into the optimization process to guide the search towards feasible regions of the configuration space. By enforcing constraints related to the system requirements or limitations, we can avoid getting stuck in infeasible or suboptimal regions.
Dynamic Neighborhood Search: Adapting the neighborhood structure of the search space based on the current state of the optimization. By dynamically adjusting the exploration-exploitation balance and the search radius, we can navigate the configuration landscape more effectively and escape local optima.
Fitness Landscape Analysis: Analyzing the fitness landscape of the configuration space to identify regions of high and low fitness. By understanding the structure of the landscape, we can design better search strategies that are tailored to the specific characteristics of the problem, leading to more efficient optimization.

How can the insights from this work on software configuration tuning be applied to other domains that involve optimizing complex systems with expensive evaluations and rugged landscapes

The insights from this work on software configuration tuning can be applied to other domains that involve optimizing complex systems with expensive evaluations and rugged landscapes in the following ways:

Parameter Tuning in Machine Learning: The techniques developed for software configuration tuning can be adapted for hyperparameter optimization in machine learning models. By treating hyperparameters as configuration options and performance metrics as objectives, we can apply similar optimization strategies to find optimal hyperparameter settings.
Resource Allocation in Cloud Computing: Optimizing resource allocation in cloud computing environments involves configuring various parameters to achieve desired performance metrics. By applying adaptive weight mechanisms and partial duplicate retention strategies, we can improve the efficiency of resource allocation algorithms and escape local optima in the allocation process.
Supply Chain Optimization: In supply chain management, optimizing complex supply chain networks involves tuning various parameters to maximize efficiency and minimize costs. By leveraging multi-objective optimization techniques and dynamic weight adaptation, we can enhance decision-making processes in supply chain optimization and navigate rugged landscapes more effectively.