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This paper is an extension of work originally presented in SoftCOM 2019 [1]. The novelty of this work reside in its focused improvement of our scheduling algorithm towards its usage on a real 5G infrastructure. Industrial IoT applications are often designed to run in a distributed way on the devices and controller computers with strict service requirements for the nodes and the links between them. 5G, especially in concomitance with Edge Computing, will provide the desired level of connectivity for these setups and it will permit to host application run-time components in edge clouds. However, allocation of the edge cloud resources for Industrial IoT (IIoT) applications, is still commonly solved by rudimentary scheduling techniques (i.e. simple strategies based on CPU usage and device readiness, employing very few dynamic information). Orchestrators inherited from the cloud computing, like Kubernetes, are not satisfying to the requirements of the aforementioned applications and are not optimized for the diversity of devices which are often also limited in capacity. This design is especially slow in reacting to the environmental changes. In such circumstances, in order to provide a proper solution using these tools, we propose to take the physical, operational and network parameters (thus the full context of the IIoT application) into consideration, along with the software states and orchestrate the applications dynamically.

Edge architectures provide local, decentralized services, enabling balancing network traffic and distributing hardware resources. Later, many new use cases can be implemented by combining the advantages of the edge computing concept with the services of 5G systems. One of the biggest beneficiaries of this could be the Vehicle-to-Cloud (V2C) technology, where it is necessary to efficiently process large amounts of data resulting from Vehicle-to-Everything communication (V2X) services. In specific use cases, this makes it possible to process sensor data collectively, enhanced by fusion, which promotes a more effective virtual representation of the real world. The effective implementation of these technologies is a complex task. One of the most important steps before tests on actual infrastructures with real vehicles is evaluating and validating edge cloud systems. We present a solution for this problem, the Cloud-in-the-Loop (CiL) simulation framework. It can orchestrate a real-size, telco-grade level, Kubernetes-based edge cloud infrastructure based on information gathered from a traffic simulator and performing fine-grained benchmarking and data collection. In addition to the performance analysis of the edge system, it also enables an in-depth examination of cloud-native applications serving complex automotive use cases. In this paper, we focus on presenting the developed framework and its capabilities by utilizing the system with implemented test applications, and give an example of testing QoS and QoE aspects of the edge cloud-based V2C concept.

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