Cost-efficient Auto-scaling of Container-based Elastic Processes

TL;DR

This paper presents a cost-efficient auto-scaling approach for container-based elastic processes, optimizing resource allocation to reduce costs while maintaining service levels using multi-objective optimization.

Contribution

It introduces a novel four-fold auto-scaling method for containers, formulated as a multi-objective optimization problem with MILP, enhancing resource utilization and cost savings.

Findings

Significant cost reductions achieved in process enactments.

Effective resource allocation with maintained service levels.

Validation through thorough evaluation of the optimization approach.

Abstract

In business process landscapes, a common challenge is to provide the necessary computational resources to enact the single process steps. One well-known approach to solve this issue in a cost-efficient way is to use the notion of elasticity, i.e., to provide cloud-based computational resources in a rapid fashion and to enact the single process steps on these resources. Existing approaches to provide elastic processes are mostly based on Virtual Machines (VMs). Utilizing container technologies could enable a more fine-grained allocation of process steps to computational resources, leading to a better resource utilization and improved cost efficiency. In this paper, we propose an approach to optimize resource allocation for elastic processes by applying a four-fold auto-scaling approach. The main goal is to minimize the cost of process enactments by using containers. To this end, we formulate and implement a multi-objective optimization problem applying Mixed-Integer Linear Programming and use a transformation step to allocate software services to containers. We thoroughly evaluate the optimization problem and show that it can lead to significant cost savings while maintaining Service Lev