Multilayered Cloud Applications Autoscaling Performance Estimation

A multilayered autoscaling gets an increasing attention both in research and business communities. Introduction of new virtualization layers such as containers, pods, and clusters has turned a deployment and a management of cloud applications into a simple routine. Each virtualization layer usually provides its own solution for scaling. However, synchronization and collaboration of these solutions on multiple layers of virtualization remains an open topic. In the scope of the paper, we consider a wide research problem of the autoscaling across several layers for cloud applications. A novel approach to multilayered autoscalers performance measurement is introduced in this paper. This approach is implemented in Autoscaling Performance Measurement Tool (APMT), which architecture and functionality are also discussed. Results of model experiments on different requests patterns are also provided in the paper.

I. Introduction

Cloud computing's success is based on the virtualization technology that considers hardware as a pool of resources to be provided to users in a form of virtual machines (VMs). While such an abstraction introduces an additional overhead for the indirect usage of hardware resources, it also helps businesses scale their applications in the cloud fast. Scalability of cloud applications is their major feature and the main reason behind the cloud computing wide spread. Most cloud services providers (CSPs) provide their users autoscaling services to both capture the flash crowd traffic and save money when additional machines are unneeded.