Runtime Vertical Scaling of Virtualized Applications via Online Model Estimation

• Published in: International Conference on Self-Adaptive and Self-Organizing Systems (Print) pp. 157 - 166
• Main Authors: Spinner, Simon, Kounev, Samuel, Xiaoyun Zhu, Lei Lu, Uysal, Mustafa, Holler, Anne, Griffith, Rean
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2014
• Subjects: application performance management; auto-scaling; Delays; Estimation; resource demand estimation; Resource management; Runtime; Servers; Throughput; vertical scaling; Virtual machine monitors; virtualization
• DOI: 10.1109/SASO.2014.29
• ISSN: 1949-3673

Applications in virtualized data centers are often subject to Service Level Objectives (SLOs) regarding their performance (e.g., latency or throughput). In order to fulfill these SLOs, it is necessary to allocate sufficient resources of different types (CPU, memory, I/O, etc.) to an application. However, the relationship between the application performance and the resource allocation is complex and depends on multiple factors including application architecture, system configuration, and workload demands. In this paper, we present a model-based approach to ensure that the application performance meets the user-defined SLO efficiently by runtime "vertical scaling" (i.e., adding or removing resources) of individual virtual machines (VMs) running the application. A layered performance model describing the relationship between the resource allocation and the observed application performance is automatically extracted and updated online using resource demand estimation techniques. Such a model is then used in a feedback controller to dynamically adapt the number of virtual CPUs of individual VMs. We have implemented the controller on top of the VMware vSphere platform and evaluated it in a case study using a real-world email and groupware server. The experimental results show that our approach allows the managed application to achieve SLO satisfaction in spite of workload demand variation while avoiding oscillations commonly observed with state-of-the-art threshold-based controllers.