A Timely Journey Through the Cloud

This thesis treats the intersection between two of the largest transformations we are seeing within our society today; the cloud and the Internet-of-Things (IoT). The aim of this thesis is to investigate different ways to model and control a network of cloud services so that timing-critical IoT applications can make use of them. Examples of such applications can be autonomous and mobile robots, smart production plants, or massive multi-player augmented-reality games. The main motivational use-case, however, comes from the industrial side, and their digitalization, the drive towards industrial IoT (IIoT). We wish to enable smart robots to offload some of their computations to the cloud in order to allow for better and smarter control and collaboration. For instance, using the cloud, it would become possible for them to collaborate and make use of smarter analytics, artificial intelligence, and machine learning, in order to improve efficiency and safety. To address this problem the thesis combines concepts and theory from different fields, most notably from control theory, real-time systems, and network calculus. Examples are: modeling of dynamic systems and the use of feedback and feedforward control from control theory, the goal of ensuring that end-to-end deadlines are met, from real-time systems, and finally the principles of modeling traffic from network calculus. The thesis begins with an introduction to provide some background on cloud, IIoT, and to set the scope of the thesis. Following this, we begin by treating the problem of controlling a single cloud service with the goal of ensuring that the traffic flowing through the node is guaranteed to meet a deadline. Following this, we study a chain of connected cloud nodes, investigating how to provide end-to-end deadline guarantees for the traffic flowing through the chain. The chain is finally generalized to a network of cloud nodes, with multiple flows traversing it. For this problem we study how to ensure that the end-to-end deadline of every single flow in the network is guaranteed. We also provide a set of protocols controlling how cloud nodes and flows are allowed to dynamically join and leave the network, such that no end-to-end deadline is violated

Achieving predictable and low end-to-end latency for a network of smart services

To remain competitive in the field of manufacturing today, companies must constantly improve the automation loops within their production plants. This can be done by augmenting the automation applications with "smart services" such as supervisory-control applications or machine-learning inference algorithms. The downside is that these smart services are often hosted in a cloud infrastructure and the automation applications require a low and predictable end-to-end latency. However, with the 5G technology it will become possible to establish a low-latency connection to the cloud infrastructure and with proper control of the capacity of the smart services, it will become possible to achieve a low and predictable end-to-end latency for the augmented automation applications.In this work we address the challenge of controlling the capacity of the smart services in a way that achieves a low and predictable end-to-end latency. We do this by deriving a mathematical framework that models a network of smart services that is hosting several automation applications. We propose a generalized AutoSAC (automatic service- and admission controller) that builds on previous work by the authors. In the previous work the system was only capable of handling a single set of smart services, with a single application hosted on top of it. With the contributions of this paper it becomes possible to host multiple applications on top of a larger, more general network of smart services