https://www.mdu.se/

Container-based virtualization is a promising deployment model in fog and edge computing applications, because it allows a seamless co-existence of virtualized applications in a heterogeneous environment without introducing significant overhead. Certain application domains (e.g., industrial automation, automotive, or aerospace) mandate that applications exhibit a certain degree of temporal predictability. Container-based virtualization cannot be easily used for such applications, since the technology is not designed to support real-time properties and handle temporal disturbances. This article proposes a framework consisting of a static offline and a dynamic online phase for resource allocation and adaptive re-dimensioning of real-time containers. In the offline phase, the optimal initial deployment and dimensioning of containers are decided based on ideal system models. Additionally, to adapt to dynamic variations caused by changing workloads or interferences, the online phase adapts the CPU usage and limits of real-time containers at runtime to improve the real-time behavior of the real-time containerized applications while optimizing resource usage. We implement the framework in a real Linux-based system and showthrough a series of experiments that the proposed framework is able to adjust and re-distribute computing resources between containers to improve the real-time behavior of containerized applications in the presence of temporal disturbances while optimizing resource usage.

Container-based virtualization has become a promising deployment model for industrial applications mainly due to its benefits, such as providing support for co-located applications in heterogeneous environments. However, such facilitation brings challenges, including full temporal isolation among real-time applications and support for time-critical applications. In this paper, we tackle such challenges, in particular when the applications are time-sensitive Control Applications. The literature suggests that flexible timing constraints for Control Applications are beneficial in responding to disturbances and minimizing response deviation. Therefore, we propose a mechanism to support such a runtime adaptation in container-based virtualization. To show the performance of the proposed mechanism, we implement our approach on a Linux-based hierarchical scheduling platform, and we evaluate it for a Control application.

In recent years, autonomous systems have become an important research area and application domain, with a significant impact on modern society. Such systems are characterized by different levels of autonomy and complex communication infrastructures that allow for collective decision-making strategies. There exist several publications that tackle ethical aspects in such systems, but mostly from the perspective of a single agent. In this paper we go one step further and discuss these ethical challenges from the perspective of an aggregate of autonomous systems capable of collective decision-making. In particular, in this paper, we propose the Caesar approach through which we model the collective ethical decision-making process of a group of actors—agents and humans, as well as define the building blocks for the agents participating in such a process, namely Caesar agents. Factors such as trust, security, safety, and privacy, which affect the degree to which a collective decision is ethical, are explicitly captured in Caesar. Finally, we argue that modeling the collective decision-making in Caesar provides support for accountability. 

Fog computing is a distributed computing paradigm that brings data processing from remote cloud data centers into the vicinity of the edge of the network. The computation is performed closer to the source of the data, and thus it decreases the time unpredictability of cloud computing that stems from (i) the computation in shared multi-tenant remote data centers, and (ii) long distance data transfers between the source of the data and the data centers. The computation in fog computing provides fast response times and enables latency sensitive applications. However, industrial systems require time-bounded response times, also denoted as RT. The correctness of such systems depends not only on the logical results of the computations but also on the physical time instant at which these results are produced. Time-bounded responses in fog computing are attributed to two main aspects: computation and communication.   

In this thesis, we explore both aspects targeting soft RT applications in fog computing in which the usefulness of the produced computational results degrades with real-time requirements violations. With regards to the computation, we provide a systematic literature survey on a novel lightweight RT container-based virtualization that ensures spatial and temporal isolation of co-located applications. Subsequently, we utilize a mechanism enabling RT container-based virtualization and propose a solution for orchestrating RT containers in a distributed environment. Concerning the communication aspect, we propose a solution for a dynamic bandwidth distribution in virtualized networks.

Fog and edge computing offer the flexibility and decentralized architecture benefits of cloud computing without suffering from the latency issues inherent in the cloud. This makes fog computing very attractive in real-time and safety-critical applications, especially if combined with container-based technologies. Whereas different orchestration systems are available to manage the container placement based on their resource demand, no orchestration system is considering real-time requirements for containerized applications. In this paper, we present the architecture and design of a real-time container orchestrator based on Kubernetes. Moreover, this paper defines metrics for the performance evaluation of real-time containers and describes an initial model for allocating a mixture of real-time and non-real-time containers. We present an initial implementation of our real-time container extension and evaluate its feasibility on Linux-based systems.

Low latency and on demand resource availability enable fog computing to host industrial applications in a cloud like manner. One industrial domain which stands to benefit from the advantages of fog computing is robotics. However, the challenges in developing and implementing a fog-based robotic system are manifold. To illustrate this, in this paper we discuss a system involving robots and robot cells at a factory level, and then highlight the main building blocks necessary for achieving such functionality in a fog-based system. Further, we elaborate on the challenges in implementing such an architecture, with emphasis on resource virtualization, memory interference management, real-time communication and the system scalability, dependability and safety. We then discuss the challenges from a system perspective where all these aspects are interrelated.

Container-based virtualization has gained a significant importance in a deployment of software applications in cloud-based environments. The technology fully relies on operating system features and does not require a virtualization layer (hypervisor) that introduces a performance degradation. Container-based virtualization allows to co-locate multiple isolated containers on a single computation node as well as to decompose an application into multiple containers distributed among several hosts (e.g., in fog computing layer). Such a technology seems very promising in other domains as well, e.g., in industrial automation, automotive, and aviation industry where mixed criticality containerized applications from various vendors can be co-located on shared resources. However, such industrial domains often require real-time behavior (i.e, a capability to meet predefined deadlines). These capabilities are not fully supported by the container-based virtualization yet. In this work, we provide a systematic literature survey study that summarizes the effort of the research community on bringing real-time properties in container-based virtualization. We categorize existing work into main research areas and identify possible immature points of the technology. © Václav Struhár, Moris Behnam, Mohammad Ashjaei, and Alessandro V. Papadopoulos; licensed under Creative Commons License CC-BY 2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020).

In this paper we address a network architecture that uses a combination of network virtualization and software defined networking in order to reduce complexity of network management and at the same time support high quality of service. Within this network architecture, we propose a framework to be able to dynamically distribute the network bandwidth to various services such that the network resources are utilized efficiently. In many industrial domains, multiple services may use the same hardware platform for the sake of a better resource utilization. Therefore, bandwidth distribution among the services should be done in an efficient way during runtime. We also develop an admission control in this framework which dynamically coordinates the bandwidth distributions based on requested quality of services. We show the applicability of the proposed framework by implementing it on a common SDN controller. Moreover, we conduct a set of experiments to show the performance of the proposed framework.

To meet the demands of future automation systems, the architecture of traditional control systems such as the industrial robotic systems needs to evolve and new architectural paradigms need to be investigated. While cloud-based platforms provide services such as computational resources on demand, they do not address the requirements of real-time performance expected by control applications. Fog computing is a promising new architectural paradigm that complements the cloud-based platform by addressing its limitations. In this paper, we analyse the existing robot system architecture and propose a fog-based solution for industrial robotic systems that addresses the needs of future automation systems. We also propose the use of Time-Sensitive Networking (TSN) services for real-time communication and OPC-UA for information modelling within this architecture. Additionally, we discuss the main research challenges associated with the proposed architecture.