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Search-Based Scheduling of Experiments in Continuous Deployment

机译：基于搜索的连续部署实验调度

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Continuous experimentation involves practices for testing new functionality on a small fraction of the user base in production environments. Running multiple experiments in parallel requires handling user assignments (i.e., which users are part of which experiments) carefully as experiments might overlap and influence each other. Furthermore, experiments are prone to change, get canceled, or are adjusted and restarted, and new ones are added regularly. We formulate this as an optimization problem, fostering the parallel execution of experiments and making sure that enough data is collected for every experiment avoiding overlapping experiments. We propose a genetic algorithm that is capable of (re-)scheduling experiments and compare with other search-based approaches (random sampling, local search, and simulated annealing). Our evaluation shows that our genetic implementation outperforms the other approaches by up to 19% regarding the fitness of the solutions identified and up to a factor three in execution time in our evaluation scenarios.
机译：连续实验涉及在生产环境中对用户基数的小数部分测试新功能的实践。并行运行多个实验需要处理用户分配（即，哪些用户是哪个实验的一部分）作为实验可能重叠并相互影响。此外，实验易于改变，取消或调整和重新启动，并定期添加新的。我们将其作为优化问题制定，促进实验的并行执行，并确保为每个实验收集足够的数据，避免重叠的实验。我们提出了一种能够（重新）调度实验的遗传算法，并与其他基于搜索的方法（随机采样，本地搜索和模拟退火）进行比较。我们的评价表明，我们的遗传实施胜过了其他方法，最多高达19％的方法，关于所确定的解决方案的适应性以及在我们的评估方案中的执行时间中的一个因素三。

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