Computing systems continue to evolve, resulting in increased performance when processing workloads in large data centers due to the virtualization benefits. This technology is the key factor that allows multiple applications to share resources, thereby enhancing the overall hardware utilization of cloud computing environments. However, multiple cloud-services contending for shared resources are susceptible to cross-application interference, which can lead to significant performance degradation and, consequently, an increase in Service Level Agreements violations. Nevertheless, state-of-the-art resource scheduling still relies mainly on resource capacity, adopting heuristics such as bin-packing and overlooking this source of overhead. But in recent years, interference-aware scheduling has gained traction, with the investigation of ways to classify applications regarding their interference levels and the proposal of static interference models and policies for scheduling co-hosted cloud applications. The preliminary results already show a considerable improvement in resource utilization and can be considered as the first steps toward a dynamic scheduling strategy. In this scenario, this paper proposes a machine learning-driven classification scheme for dynamic interference-aware resource scheduling in cloud computing environments. The main goal is to present how a classification approach, that better represents the workload variations, affects resource scheduling. In the first place, we analyze how hardware resources react to different applications with dynamic workloads. Then, we explore distinct interference classification formats and evaluate their efficiency, taking the dynamic nature of cloud workloads into account. Lastly, we present an interference-aware application classifier based on machine learning techniques and compare it with related work, adopting a variety of workload patterns. Preliminary results revealed an improvement in resource utilization efficiency by 27%, on average, when applying our classification approach in cloud infrastructures.

由于虚拟化的优势，计算系统继续发展，从而在处理大型数据中心的工作负载时提高了性能。这项技术是允许多个应用程序共享资源的关键因素，从而提高了云计算环境的整体硬件利用率。但是，争用共享资源的多个云服务容易受到跨应用程序的干扰，这可能导致性能显着下降，并因此导致违反服务水平协议的情况增加。尽管如此，最新的资源调度仍主要依赖于资源容量，采用了诸如装箱装箱等启发式方法，并忽略了这种开销来源。但是，近年来，意识到干扰的调度已受到关注，研究如何根据应用程序的干扰级别对应用程序进行分类，并提出了用于计划协同托管云应用程序的静态干扰模型和策略的建议。初步结果已经显示出资源利用率的显着提高，可以视为迈向动态调度策略的第一步。在这种情况下，本文提出了一种机器学习驱动的分类方案，用于在云计算环境中动态感知干扰的资源调度。主要目标是介绍更好地表示工作负载变化的分类方法如何影响资源调度。首先，我们分析硬件资源如何通过动态工作负载对不同的应用程序做出反应。然后，我们考虑了云工作负载的动态性质，探索了不同的干扰分类格式并评估了它们的效率。最后，我们提出了一种基于机器学习技术的可感知干扰的应用分类器，并将其与相关工作进行了比较，并采用了多种工作负载模式。初步结果显示，将我们的分类方法应用于云基础架构中，资源利用效率平均提高了27％。