Automated Configuration for Agile Software Environments
Automated Configuration for Agile Software Environments
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DOI：
10.1109/cloud55607.2022.00074
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Negar Mohammadi Koushki;Sanjeev Sondur;K. Kant
中科院分区：
文献类型：
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作者：
Negar Mohammadi Koushki;Sanjeev Sondur;K. Kant
DevOps范式在软件系统中的使用越来越多，大大增加了配置参数设置更改的频率。由于复杂的相互依赖性，确保这些设置的正确性通常是一个非常具有挑战性的问题，并且需要一种既可以快速运行又可以提供准确设置的自动化机制。在本文中，我们提出了一个有效的离散组合优化技术，使两个独特的贡献：（a）一个改进和扩展的元启发式，利用应用领域的知识快速收敛，和（B）的发展和量化的离散版本的经典隧道机制，以提高解决方案的准确性。我们使用包括配置信息的可用工作负载跟踪进行的广泛评估表明，与传统的元启发式算法相比，所提出的技术可以提供更低成本的解决方案（约60%），收敛速度更快（约48%）。此外，我们的解决方案成功地找到一个可行的解决方案，在大约30%以上的情况下比基线算法。
The increasing use of the DevOps paradigm in software systems has substantially increased the frequency of configuration parameter setting changes. Ensuring the correctness of such settings is generally a very challenging problem due to the complex interdependencies, and calls for an automated mechanism that can both run quickly and provide accurate settings. In this paper, we propose an efficient discrete combinatorial optimization technique that makes two unique contributions: (a) an improved and extended metaheuristic that exploits the application domain knowledge for fast convergence, and (b) the development and quantification of a discrete version of the classical tunneling mechanism to improve the accuracy of the solution. Our extensive evaluation using available workload traces that do include configuration information shows that the proposed technique can provide a lower-cost solution (by ~60%) with faster convergence (by ~48%) as compared to the traditional metaheuristic algorithms. Also, our solution succeeds in finding a feasible solution in approximately 30% more cases than the baseline algorithm.