The proposal of Cloud/Fog Automation introduces a new architecture for industrial automation, which breaks the boundaries between information technology, operational technology, and communication technology domains, facilitating information sharing and optimizing the system as a whole. This paper extends the vision of Cloud/Fog Automation to scalable robotics, which is deployed across the device-edge-cloud continuum using cloud-native technologies and calls for a co-design methodology that jointly considers communication, compute, and application characteristics. From an industrial practitioner’s viewpoint, we demonstrate its feasibility with a comprehensive approach to optimizing a mobile robot application. We leverage a dual-phase optimization strategy: static optimization pre-deployment and dynamic optimization post-deployment. In the static optimization phase, we employ a profiling-based method to minimize communication overhead while balancing computational load. The dynamic optimization phase utilizes a reinforcement learning-based approach to explore an optimal policy for computation offloading and network quality of service configuration to maximize edge server utilization and lower network usage costs while guaranteeing application performance. Experimental results, validated through a Simulation-to-Reality (Sim-to-Real) approach, demonstrate that our co-design method significantly enhances operational efficiency, reduces network costs, and improves overall system responsiveness.

Container orchestration platform is the foundation of modern cloud infrastructure. In recent years, container orchestration platform has been evolving to cross the boundary of device, edge, and cloud. More and more Internet of Things (IoT) applications such as robotics and eXtended Reality (XR) have been deployed across the device-cloud continuum through the container orchestration platform, e.g., the Kubernetes (K8s) framework. Meanwhile, the rapid expansion of advanced communication technologies like 5G has endorsed the revolution in IoT applications as more network resource is available for critical IoT use cases. This paper aims to integrate network configuration capabilities provided by a 5G Network Exposure Function (NEF) into the K8s framework which is used to simplify application deployment in an orchestration in the device-cloud continuum. Specifically, a Linux fwmark-based network Quality of Service (QoS) configuration method is proposed to expose the QoS information from an overlay network that is used by the container orchestration platform to the underlay network. A Container Networking Interface (CNI) plugin-based implementation is demonstrated to perform QoS configuration for the 5G network. The proposed solution is validated with an existing localization and mapping application to verify the feasibility. The proposed solution has the following benefits: (1) The solution is a Kubernetes-native approach which adopts the CNI plugin mechanism. (2) The solution can expose the QoS information from an overlay network to an underlay network in a non-intrusive manner. (3) No packet manipulation is required to greatly reduce the overhead for packet processing. (4) It extends the K8s bandwidth limit feature from on-node to the access network. (5) It is compatible with the 5G infrastructure without any alteration or adding extra complexity.

Establishing upon the connectivity layer provided by Internet of Things (IoT) platforms, modern industries are moving towards management and computation solutions which enable Artificial Intelligence (AI) services for data intensive applications. This raises two important challenges: first, the information carried by data should be refined and prepared for the various AI algorithms via data processing pipelines and; second, a distributed orchestration solution for data and AI computation resources featuring with migration capabilities is required to support the refining process. In order to address these challenges, this paper introduces ERAIA, an actor-based framework which provides a novel basis to build intelligence and data pipelines. ERAIA facilitates the deployment and migration of distributed AI computations for heterogeneous and dynamic IoT scenarios. An implementation description is accompanied by relevant performance evaluations to demonstrate the flexibility and scalability of the solution. ERAIA provides an interface to expand the scope of existing IoT systems as Application Enablement Platform (AEP), which hence accelerates the development of AI-based IoT solutions.