Modern distributed applications increasingly span an edge-cloud continuum, where services may need to dynamically migrate between far-edge, near-edge, and cloud environments to meet latency, resource, or policy constraints. Ensuring seamless service continuity during such migrations remains a significant challenge, particularly due to delays in DNS resolution and inconsistent client routing. This paper presents a modular DNS-driven orchestration framework designed for Kubernetes-based infrastructures. Our approach integrates service orchestration logic with a shared DNS component to enable fast and autonomous service redirection without relying on public DNS providers or complex service meshes. By coupling orchestrator-triggered migrations with DNS record updates, the system ensures immediate service reachability after migration. An optional analytics and decision engine complements the framework by triggering orchestrator actions based on monitoring insights. We evaluate the DNS reconfiguration performance of our solution compared to traditional ExternalDNS-based architectures, demonstrating significantly lower propagation latency and higher determinism during service migrations across the edge–cloud continuum.

As industries increasingly embrace digital and intelligent transformation, enterprises face significant challenges in the containerization upgrades for their Artificial Intelligence(AI) applications and dynamic service migration and management in Cloud-Edge continuum(CEC). This paper presents CloudSkin, an innovative platform designed to realize streamlined, seamless and intelligent service migration in Cloud-Edge Continuum by integrating advanced containerization techniques and AI-driven orchestration capabilities. Our approach, leveraging intelligent algorithms for service migration, can seamlessly transit services between cloud and edge environments, ensuring optimised resource allocation and reducing service latency to assure quality of service(QoS). CloudSkin has been enabled in a Mobility Usecase, empowering Cellnex businesses undergoing digital transformation to achieve higher operational efficiency. The experimental results show that compared to traditional reactive service migration, using intelligent proactive service migration can provide better migration detection, improving F1-Score up to 23.5%, and reducing 28.9% the service running time where the service latency violates SLA.

The sixth generation (6G) of mobile networks seeks to leverage Integrated Terrestrial and Non-Terrestrial Networks (ITNTN) to support next-generation applications. However, despite increased network densification, meeting the stringent latency requirements—particularly for delay-sensitive applications—remains a significant challenge due to the high latency overhead introduced by non-terrestrial nodes. Content caching offers a potential solution to reduce content access delays and backhaul bandwidth costs by pre-storing content closer to the end users. However, the heterogenity of nodes, the varying content popularity and the distinct Quality of Service (QoS) requirements introduced by network slicing complicate caching decisions in ITNTNs. For instance, prioritizing content related to critical services (e.g., emergency services) over popular content (e.g., social media files) may better address QoS needs, albeit with lower hit ratios, an aspect not explored in existing works. This paper introduces SaCCA-RL (Reinforcement Learning-based Slice-aware Content Caching Algorithm) for selecting which content to cache and its placement under constrained ITNTN node capacities, considering different and varying content popularity and priority. Simulations show that SaCCA-RL results in more than 10% improvement in terms of cache gain compared to baseline algorithms, while guaranteeing 100% hit-ratio for critical content.

The virtualization and softwarization of 5G/6G mobile networks have enabled the deployment and orchestration of cloud-native network and application functions. The deployment of these functions is crucial, as the placement of data plane elements (i.e., User Plane Function (UPF)) and vertical services can significantly impact the overall user latency. However, in multi-slice edge scenarios, characterized by users with distinct levels of criticality, the problem of UPF and application placement is becoming increasingly complex due to i) the various costs involved and ii) the limited computational resources at the edge. In this paper, the problem of joint UPF and application placement for a multi-slice user scenario is studied, taking into account multiple cost components that influence the placement decision, including service migration, traffic forwarding, server activation and processing costs. To tackle this problem, we introduce a Joint UPF and Application Reinforcement Learning-based (JUAP-RL) algorithm, which decides the UPF and application deployment location and coordinates the placement stages. Extensive experiments have shown that JUAP-RL demonstrates up to 17% gain in terms of user acceptance ratio and up to 23.4% reduction in provisioning cost compared to baseline schemes.