Modeling Deployment Decisions for Elastic Services with ABS

TL;DR

This paper presents ABS, a formal model-based approach for making early deployment decisions for elastic cloud services, optimizing resource use and meeting service-level requirements.

Contribution

It introduces a formal modeling framework that integrates service design with deployment decision-making, enabling comparison of scaling strategies based on traffic patterns.

Findings

Effective auto-scaling strategies modeled with ABS

Comparison of deployment decisions under peak load conditions

Demonstration of early design-time deployment optimization

Abstract

The use of cloud technology can offer significant savings for the deployment of services, provided that the service is able to make efficient use of the available virtual resources to meet service-level requirements. To avoid software designs that scale poorly, it is important to make deployment decisions for the service at design time, early in the development of the service itself. ABS offers a formal, model-based approach which integrates the design of services with the modeling of deployment decisions. In this paper, we illustrate the main concepts of this approach by modeling a scalable pool of workers with an auto-scaling strategy and by using the model to compare deployment decisions with respect to client traffic with peak loads.