Metrics-Based Auto Scaling Module for Amazon Web Services Cloud Platform

Conference paper

DOI: 10.1007/978-3-319-58274-0_4

Part of the Communications in Computer and Information Science book series (CCIS, volume 716)
Cite this paper as:
Augustyn D.R., Warchal L. (2017) Metrics-Based Auto Scaling Module for Amazon Web Services Cloud Platform. In: Kozielski S., Mrozek D., Kasprowski P., Małysiak-Mrozek B., Kostrzewa D. (eds) Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation. BDAS 2017. Communications in Computer and Information Science, vol 716. Springer, Cham

Abstract

One of the key benefits of moving an application to the cloud is the ability to easy scale horizontally when the workload increases. Many cloud providers offer a mechanism of auto scaling which dynamically adjusts the number of virtual server instances, on which given system is running, according to some basic resource-based metrics like CPU utilization. In this work, we propose a model of auto scaling which is based on timing statistics: a high order quantile and a mean value, which are calculated from custom metrics, like execution time of a user request, gathered on application level. Inputs to the model are user defined values of those custom metrics. We developed software module that controls a number of virtual server instances according to both auto scaling models and conducted experiments that show our model based on custom metrics can perform better, while it uses less instances and still maintains assumed time constraints.

Keywords

Cloud computing Scalability Auto scaling Custom metrics Load balancing 

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

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