Recent advances in modern containerized execution environments have resulted in substantial benefits in terms of elasticity and more efficient utilization of computing resources. Although existing schedulers strive to optimize performance metrics like task execution times and resource utilization, they provide limited transparency into their decision-making processes or the specific actions developers must take to meet Service Level Objectives (SLOs). In this work, we propose X-Sched, a middleware that uses explainability techniques to generate actionable guidance on resource configurations that makes task execution in containerized environments feasible, under resource and time constraints. X-Sched addresses this gap by integrating counterfactual explanations with advanced machine learning models, such as Random Forests, to efficiently identify optimal configurations. This approach not only ensures that tasks are executed in line with performance goals but also gives users clear, actionable insights into the rationale behind scheduling decisions. Our experimental results validated with data from real-world execution environments, illustrate the efficiency, benefits and practicality of our approach.

翻译：现代容器化执行环境的最新进展在弹性与计算资源利用效率方面带来了显著优势。尽管现有调度器致力于优化任务执行时间与资源利用率等性能指标，但其决策过程或开发者为满足服务等级目标所需采取的具体行动缺乏透明度。本研究提出X-Sched——一种通过可解释性技术生成资源配置可操作指导的中间件，使资源与时间约束下的容器化环境任务执行成为可能。X-Sched通过将反事实解释与随机森林等先进机器学习模型相结合，有效识别最优配置，从而弥补这一不足。该方法不仅确保任务执行符合性能目标，还为用户提供关于调度决策背后逻辑的清晰、可操作的见解。基于真实执行环境数据的实验结果验证了我们方法在效率、优势及实用性方面的表现。