Optimized Container-Based Process Execution in the Cloud

A key challenge for elastic business processes is the resource-efficient scheduling of cloud resources in such a way that Quality-of-Service levels are met. So far, this has been difficult, since existing approaches use a coarse-granular resource allocation based on virtual machines.

In this paper, we present a technique that provides fine-granular resource scheduling for elastic processes based on containers. In order to address the increased complexity of the respective scheduling problem, we develop a novel technique called GeCo based on genetic algorithms. Our evaluation demonstrates that in comparison to a baseline that follows an ad hoc approach a cost saving between 32.90% and 47.45% is achieved by GeCo while considering a high service level.