Enforcing deployment latency SLA in edge infrastructures through multi-objective genetic scheduler

Abstract

Edge Computing emerged as a solution to new applications, like augmented reality, natural language processing, and data aggregation that relies on requirements that the Cloud does not entirely fulfill. Given that necessity, the application deployment in Edge scenarios usually uses container-based virtualization. When deployed in a resource-constrained infrastructure, the deployment latency to instantiate a container can increase due to bandwidth limitation or bottlenecks, which can significantly impact scenarios where the edge applications have a short life period, high mobility, or interdependence between different microservices. To attack this problem, we propose a novel container scheduler based on a multi-objective genetic algorithm. This scheduler has the main objective of ensuring the Service Level Agreement set on each application that defines when the application is expected to be effectively active in the infrastructure. We also validated our proposal using simulation and evaluate it against two scheduler algorithms, showing a decrease in the number of applications that do not fulfill the SLA and the average time over the SLA to not fulfilled applications.