A Framework for Dynamically Configurable Embedded Controllers

[abstract missing

A Matlab Toolbox for Real-Time and Control Systems Co-Design

The paper presents a Matlab toolbox for simulation of real-time control systems. The basic idea is to simulate a real-time kernel in parallel with continuous plant dynamics. The toolbox allows the user to explore the timely behavior of control algorithms, and to study the interaction between the control tasks and the scheduler. From a research perspective, it also becomes possible to experiment with more flexible approaches to real-time control systems, such as feedback scheduling. The importance of a more unified approach for the design of real-time control systems is discussed. The implementation is described in some detail and a number of examples are given

The Control Server Model for Co-Design of Real-Time Control Systems

The paper presents the control server, a real-time scheduling mechanism tailored to control and signal processing applications. A control server creates the abstraction of a control task with a specified period and a fixed input-output latency shorter than the period. Individual tasks can be combined into more complex components without loss of their individual guaranteed fixed-latency properties. I/O occurs at fixed predefined points in time, at which inputs are read or controller outputs become visible. The control server model is especially suited for codesign of real-time control systems. The single parameter linking the scheduling design and the controller design is the task utilization factor. The proposed server is an extension of the constant bandwidth server, which is based on the earliest-deadline-first scheduling algorithm. The server has been implemented in a real-time kernel and has also been validated in control experiments on a ball and beam process

Dynamic control of NFV forwarding graphs with end-to-end deadline constraints

There is a strong industrial drive to use cloud computing technologies and concepts for providing timing sensitive services in the networking domain since it would provide the means to share the physical resources among multiple users and thus increase the elasticity and reduce the costs. In this work, we develop a mathematical model for user-stateless virtual network functions forming a forwarding graph. The model captures uncertainties of the performance of these virtual resources as well as the time-overhead needed to instantiate them. The model is used to derive a service controller for horizontal scaling of the virtual resources as well as an admission controller that guarantees that packets exiting the forwarding graph meet their end-to-end deadline. The Automatic Service and Admission Controller (AutoSAC) developed in this work uses feedback and feedforward making it robust against uncertainties of the underlying infrastructure. Also, it has a fast reaction time to changes in the input

End-To-End Deadlines over Dynamic Topologies

Despite the creativity of the scientific community and the funding agencies, the underlying model of computation behind IoT, WSN, cloud, edge, fog, and mist is fundamentally the same; Computational nodes which are dynamically interconnected to form a system in where both processing capacity and connectivity may vary over time. On top of such a system, we consider applications that need packets to flow along a path and adhere to end-to-end deadlines. This application model is motivated by both control and automation systems, as well as telecom systems. The challenge is to guarantee end-to-end deadlines when allowing nodes and applications to join or leave. The mainstream, and to some extent natural, approach to this is to relax the stringency of the constraint (e.g. use probabilistic guarantees, soft deadlines). In this paper we take a different approach and keep the end-to-end deadlines as hard constraints and instead partially limit the freedom of how nodes and applications are allowed to leave and join. We present a theoretical framework for modeling such systems along with proofs that deadlines are always honored

Feedback for increased robustness of forwarding graphs in the cloud

Cloud computing technology provides the means to share physical resources among multiple users and data center tenants by exposing them as virtual resources. There is a strong industrial drive to use similar technology and concepts to provide timing sensitive services. One such domain is a chain of connected virtual network functions. This allows the capacity of each function to be scaled up and down by adding or removing virtual resources. In this work, we develop a model of such service chain and pose the dynamic allocation of resources as an optimization problem. We design and present a set of strategies to allow virtual network nodes to be controlled in an optimal fashion subject to latency and buffer constraints. Furthermore, we derive a feedback-law for dynamically adjusting the amount of resources given to each functions in order to ensure that the system remains in the desired state even if there are modeling errors or for a stochastic input

Cloud-Based Model Predictive Control with Variable Horizon

A novel method using the cloud to implement a variable horizon model predictive controller is presented. In case of sudden long delays and downtime, a graceful degradation is used. Robust, best effort strategies allow industrial grade use of the powerful, efficient, and quickly improving cloud ecosystems. The variable horizon strategy finds use in, for example, non-linear control problems, and the proposed method can be generalized to implement robust and scalable controllers that benefit from cloud technology. We show results from two horizon selection strategies, service degradation and connectivity issues

An Introduction to Control and Scheduling Co-Design

The paper presents the emerging field of integrated control and CPU-time scheduling, where more general scheduling models and methods that better suit the needs of control systems are developed. This creates possibilities for dynamic and flexible integrated control and scheduling frameworks, where the control design methodology takes the availability of computing resources into account during design and allows on-line trade-offs between control performance and computing resource utilization

Towards a Holistic Controller: Reinforcement Learning for Data Center Control

The increased use of cloud and other large scale datacenter IT services and the associated power usage has put the spotlight on more energy-efficient datacenter management. In this paper, a simple model was developed to represent the heat rejection system and energy usage in a small DC setup. The model was then controlled by a reinforcement learning agent that handles both the load balancing of the IT workload, as well as cooling system setpoints.The main contribution is the holistic approach to datacenter control where both facility metrics, IT hardware metric and cloud service logs are used as inputs. The application of reinforcement learning in the proposed holistic setup is feasible and achieves results that outperform standard algorithms. The paper presents both the simplified DC model and the reinforcement learning agent in detail and discusses how this work can be extended towards a richer datacenter model