Modern configurable systems provide tremendous opportunities for engineering future intelligent software systems. A key difficulty thereof is how to effectively self-adapt the configuration of a running system such that its performance (e.g., runtime and throughput) can be optimized under time-varying workloads. This unfortunately remains unaddressed in existing approaches as they either overlook the available past knowledge or rely on static exploitation of past knowledge without reasoning the usefulness of information when planning for self-adaptation. In this paper, we tackle this challenging problem by proposing DLiSA, a framework that self-adapts configurable systems. DLiSA comes with two properties: firstly, it supports lifelong planning, and thereby the planning process runs continuously throughout the lifetime of the system, allowing dynamic exploitation of the accumulated knowledge for rapid adaptation. Secondly, the planning for a newly emerged workload is boosted via distilled knowledge seeding, in which the knowledge is dynamically purified such that only useful past configurations are seeded when necessary, mitigating misleading information.

Extensive experiments suggest that the proposed DLiSA significantly outperforms state-of-the-art approaches, demonstrating a performance improvement of up to 229% and a resource acceleration of up to 2.22x on generating promising adaptation configurations.

This repository contains the key codes, full data used, and raw experiment results for the paper.

• dataset: configuration datasets of 9 subject systems as specified in the paper.
• order_files: Sequence of workloads in 100 independent runs for 9 systems.
• results: contains the raw experiment results for all the research questions.
• statistical tool: contains statistical indicators for all research questions and corresponding statistical results.
• supplementary file: contains the specific workloads for 9 subjected systems and detailed experimental results for RQ2 and RQ3.
• main.py: the main program for using DLiSA, which automatically reads data from csv files, implements Knowledge Distillation and Evolutionary Planning, and saves the corresponding results and data.
• Adaptation_Optimizer.py the program for running Knowledge Distillation.
• Genetic_Algorithm.py the program for running Evolutionary Planning
• requirements.txt the required packages for running main.py.

1. Download all the files into the same folder/clone the repository.

2. Install the specified version of Python: the codes have been tested with Python 3.7 - 3.9, Other versions might cause errors.

3. Using the command line: cd to the folder with the codes, and install all the required packages by running:

    pip install -r requirements.txt
   

• Command line: cd to the folder with the codes, input the command below, and the rest of the process will fully automated.

  python main.py
  

• Python IDE (e.g., Pycharm): Open the main.py file on the IDE, and simply click 'Run'.

The main.py script is structured with several key components:

• Parameters Settings: Configure essential parameters like the number of independent runs, maximum generations for GA, and population size.
• Systems: Define the list of systems to be optimized.
• Compared Algorithms: Specify which algorithms to compared for system performance tuning.
• Optimization Goal: Set the optimization objective for each (e.g., maximize throughput or minimize runtime).

Modify these parameters in main.py to suit your optimization needs:

• run: Number of independent optimization runs (e.g., run = 100).
• max_generation: Maximum number of generations for the evolutionary process (e.g., max_generation = 3).
• pop_size: Population size for the Genetic Algorithm (e.g., pop_size = 20).
• mutation_rate: Probability of mutation in the evolutionary process (e.g., mutation_rate = 0.1).
• crossover_rate: Probability of crossover (e.g., crossover_rate = 0.9).
• systems: List of systems for optimization (e.g., systems = ['batlik', 'kanzi']).
• compared_algorithm: Algorithms for comparison (e.g.,compared_algorithm = ['FEMOSAA', 'Seed-EA', 'D-SOGA', 'DLiSA-0.3'] )