Edge-Cloud Continuum Orchestration of Critical Services: A Smart-City Approach

TL;DR

This paper presents an extension of Kubernetes for smart-city edge-cloud orchestration, enabling real-time, resource-aware management of critical and non-critical services in a distributed environment.

Contribution

It introduces new orchestration components tailored for MEC environments, including location-specific resource definitions, a custom scheduler, and enhanced load balancing.

Findings

Extended Kubernetes supports real-time edge services.

Improved workload isolation and timing constraints handling.

Prioritized load balancing for faster response times.

Abstract

Smart-city services are typically developed as closed systems within each city's vertical, communicating and interacting with cloud services while remaining isolated within each provider's domain. With the emergence of 5G private domains and the introduction of new M2M services focusing on autonomous systems, there is a shift from the cloud-based approach to a distributed edge computing paradigm, in a \textit{continuum} orchestration. However, an essential component is missing. Current orchestration tools, designed for cloud-based deployments, lack robust workload isolation, fail to meet timing constraints, and are not tailored to the resource-constrained nature of edge devices. Therefore, new orchestration methods are needed to support MEC environments. The work presented in this paper addresses this gap. Based on the real needs of a smart-city testbed - the Aveiro Living Lab-, we developed a set of orchestration components to facilitate the seamless orchestration of both cloud and edge-based services, encompassing both critical and non-critical services. This work extends the current Kubernetes orchestration platform to include a novel location-specific resource definition, a custom scheduler to accommodate real-time and legacy services, continuous service monitoring to detect sub-optimal states, and a refined load balancing mechanism that prioritizes the fastest response times.