论文标题
朝着基于云的软件系统的自我改进的混合弹性控制
Towards Self-Improving Hybrid Elasticity Control of Cloud-based Software Systems
论文作者
论文摘要
弹性是基于云的软件系统中自适应性的一种形式，通常仅限于基础架构层，并通过自动缩放实现。但是，当分别使用以及一起使用时，基础设施自动缩放的反应性和主动形式都有局限性。为了解决这些局限性，我们提出了一种结合了（a）基础架构和软件弹性的自我改进混合弹性控制的方法，以及（b）积极主动，反应性和响应式决策。在基础架构层，根据观察到的工作负载变化，基于一步的工作负载预测和反应性地将资源主动提供。在软件层，由于与预测的工作负载的短暂偏差，激活或停用了功能。提出的方法可以在基于云的软件系统中提供更好的性能感知和具有成本效益的资源管理。我们通过使用现实世界数据集的部分实现和模拟来验证我们的方法。
Elasticity is a form of self-adaptivity in cloud-based software systems that is typically restricted to the infrastructure layer and realized through auto-scaling. However, both reactive and proactive forms of infrastructure auto-scaling have limitations, when used separately as well as together. To address these limitations, we propose an approach for self-improving hybrid elasticity control that combines (a) infrastructure and software elasticity, and (b) proactive, reactive and responsive decision-making. At the infrastructure layer, resources are provisioned proactively based on one-step-ahead workload forecasts, and reactively, based on observed workload variations. At the software layer, features are activated or deactivated in response to transient, minor deviations from the predicted workload. The proposed approach can lead to better performance-aware and cost-effective resource management in cloud-based software systems. We validate our approach via a partial realization and simulation with real-world datasets.
