ScaleX: Multi-Layered Cloud Applications Auto-Scaling Performance Analysis

More companies are shifting focus to adding more layers of virtualization for their cloud applications thus increasing the flexibility in development, deployment and management of applications. Increase in the number of layers can result in additional overhead during autoscaling and also in coordination issues while layers may use the same resources while managed by different software. In order to capture these multilayered autoscaling performance issues, an Autoscaling Performance Measurement Tool (APMT) was developed. This tool evaluates the performance of cloud autoscaling solutions and combinations thereof for varying types of load patterns. In the paper, we highlight the architecture of the tool and its configuration. An autoscaling behavior for major IaaS providers with Kubernetes pods as the second layer of virtualization is illustrated using the data collected by APMT.

This tool will automatically estimate and analyze the different configurations of existing cloud auto-scaling solutions in respect to performance and costs metrics, and presents the user with the best suited configuration for the deployment of application along with the pros and cons of other configurations


My research interests include cloud computing, specifically focussing on serverless computing for heterogeneous systems, edge computing, and AIOps.