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

TL;DR

This paper addresses the challenge of orchestrating smart-city services across an edge-cloud continuum by extending Kubernetes with a location-aware resource model (FogService), a real-time capable scheduler with plugins for dependency-aware and RT scheduling, and runtime extensions to support legacy applications and RT policies. It also introduces continuous cluster-state monitoring and a latency-aware load-balancing mechanism based on a Markov-chain model to optimize placement and traffic distribution. Through a Aveiro Living Lab-driven evaluation, the approach demonstrates improved real-time performance, dynamic re-scheduling, and lower end-to-end latency compared to traditional cloud-focused orchestration. The work advances practical MEC deployments by enabling cross-domain, low-latency service orchestration while highlighting gaps in network-layer integration and mobility support as avenues for future work.

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.

Figures (11)

• Figure 1: High-level overview of the structure of this work
• Figure 2: Overview of the proposed architecture
• Figure 3: Overview of the lifecycle of a deployment, and the role of the critical components of this implementation
• Figure 4: Workload Runtime Architecture
• Figure 5: Overview of the virtualised cluster used in the evaluation setup