Predictive Auto-Scaling of Multi-Tier Applications Using Performance Varying Cloud Resources
Predictive Auto-Scaling of Multi-Tier Applications Using Performance Varying Cloud Resources
复制标题
使用性能变化的云资源预测性自动缩放多层应用程序
DOI：
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复制
发表时间：
2022
影响因子：
6.5
通讯作者：
A. Mahmood
中科院分区：
文献类型：
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作者：
Waheed Iqbal;A. Erradi;Muhammad Abdullah;A. Mahmood
相同类型的云资源（例如虚拟机（VM））的性能随时间变化，主要是由于硬件异构性，位于同一位置的VM之间的资源争用以及虚拟化开销。性能变化可能很大，这给学习特定于工作负载的资源配置策略带来了挑战，从而自动扩展云托管应用程序，以保持所需的响应时间。此外，使用最少的资源自动扩展多层应用程序更具挑战性，因为瓶颈可能同时出现在多个层上。在本文中，我们解决的问题，使用性能不同的虚拟机优雅地自动扩展多层应用程序，使用最少的资源来处理动态增加的工作负载，并满足响应时间的要求。该系统使用监督学习方法，以确定适当的资源供应多层应用程序的基础上的应用程序响应时间和请求到达率的预测。监督学习方法学习状态转换配置图，该图对不随底层虚拟机性能变化而变化的资源分配状态进行编码。此配置映射有助于在预测自动缩放方法中使用性能变化资源。我们使用托管在公共云上的真实多层Web应用程序进行的实验评估显示，与传统的预测自动扩展方法相比，使用最少的资源提高了应用程序的性能。
The performance of the same type of cloud resources, such as virtual machines (VMs), varies over time mainly due to hardware heterogeneity, resource contention among co-located VMs, and virtualization overhead. The performance variation can be significant, introducing challenges to learn workload-specific resource provisioning policies to automatically scale the cloud-hosted applications to maintain the desired response time. Moreover, auto-scaling multi-tier applications using minimal resources is even more challenging because bottlenecks may occur on multiple tiers concurrently. In this paper, we address the problem of using performance varying VMs for gracefully auto-scaling a multi-tier application using minimal resources to handle dynamically increasing workloads and satisfy the response time requirements. The proposed system uses a supervised learning method to identify the appropriate resources provisioning for multi-tier applications based on the prediction of the application response time and the request arrival rate. The supervised learning method learns a state transition configuration map which encodes a resource allocation states invariant to the underlying VMs performance variations. This configuration map helps to use performance varying resources in predictive autoscaling method. Our experimental evaluation using a real-world multi-tier web application hosted on a public cloud shows an improved application performance with minimal resources compared to conventional predictive auto-scaling methods.