Improving Performance of Feedback-Based Real-Time Networks using Model Checking and Reinforcement Learning

Traditionally, automatic control techniques arose due to need for automation in mechanical systems. These techniques rely on robust mathematical modelling of physical systems with the goal to drive their behaviour to desired set-points. Decades of research have successfully automated, optimized, and ensured safety of a wide variety of mechanical systems. Recent advancement in digital technology has made computers pervasive into every facet of life. As such, there have been many recent attempts to incorporate control techniques into digital technology. This thesis investigates the intersection and co-application of control theory and computer science to evaluate and improve performance of time-critical systems. The thesis applies two different research areas, namely, model checking and reinforcement learning to design and evaluate two unique real-time networks in conjunction with control technologies. The first is a camera surveillance system with the goal of constrained resource allocation to self-adaptive cameras. The second is a dual-delay real-time communication network with the goal of safe packet routing with minimal delays.The camera surveillance system consists of self-adaptive cameras and a centralized manager, in which the cameras capture a stream of images and transmit them to a central manager over a shared constrained communication channel. The event-based manager allocates fractions of the shared bandwidth to all cameras in the network. The thesis provides guarantees on the behaviour of the camera surveillance network through model checking. Disturbances that arise during image capture due to variations in capture scenes are modelled using probabilistic and non-deterministic Markov Decision Processes (MDPs). The different properties of the camera network such as the number of frame drops and bandwidth reallocations are evaluated through formal verification.The second part of the thesis explores packet routing for real-time networks constructed with nodes and directed edges. Each edge in the network consists of two different delays, a worst-case delay that captures high load characteristics, and a typical delay that captures the current network load. Each node in the network takes safe routing decisions by considering delays already encountered and the amount of remaining time. The thesis applies reinforcement learning to route packets through the network with minimal delays while ensuring the total path delay from source to destination does not exceed the pre-determined deadline of the packet. The reinforcement learning algorithm explores new edges to find optimal routing paths while ensuring safety through a simple pre-processing algorithm. The thesis shows that it is possible to apply powerful reinforcement learning techniques to time-critical systems with expert knowledge about the system

Ethernet - a survey on its fields of application

During the last decades, Ethernet progressively became the most widely used local area networking (LAN) technology. Apart from LAN installations, Ethernet became also attractive for many other fields of application, ranging from industry to avionics, telecommunication, and multimedia. The expanded application of this technology is mainly due to its significant assets like reduced cost, backward-compatibility, flexibility, and expandability. However, this new trend raises some problems concerning the services of the protocol and the requirements for each application. Therefore, specific adaptations prove essential to integrate this communication technology in each field of application. Our primary objective is to show how Ethernet has been enhanced to comply with the specific requirements of several application fields, particularly in transport, embedded and multimedia contexts. The paper first describes the common Ethernet LAN technology and highlights its main features. It reviews the most important specific Ethernet versions with respect to each application field’s requirements. Finally, we compare these different fields of application and we particularly focus on the fundamental concepts and the quality of service capabilities of each proposal

Adaptive Quality of Service Control in Distributed Real-Time Embedded Systems

An increasing number of distributed real-time embedded systems face the critical challenge of providing Quality of Service (QoS) guarantees in open and unpredictable environments. For example, such systems often need to enforce CPU utilization bounds on multiple processors in order to avoid overload and meet end-to-end dead-lines, even when task execution times deviate significantly from their estimated values or change dynamically at run-time. This dissertation presents an adaptive QoS control framework which includes a set of control design methodologies to provide robust QoS assurance for systems at different scales. To demonstrate its effectiveness, we have applied the framework to the end-to-end CPU utilization control problem for a common class of distributed real-time embedded systems with end-to-end tasks. We formulate the utilization control problem as a constrained multi-input-multi-output control model. We then present a centralized control algorithm for small or medium size systems, and a decentralized control algorithm for large-scale systems. Both algorithms are designed systematically based on model predictive control theory to dynamically enforce desired utilizations. We also introduce novel task allocation algorithms to ensure that the system is controllable and feasible for utilization control. Furthermore, we integrate our control algorithms with fault-tolerance mechanisms as an effective way to develop robust middleware systems, which maintain both system reliability and real-time performance even when the system is in face of malicious external resource contentions and permanent processor failures. Both control analysis and extensive experiments demonstrate that our control algorithms and middleware systems can achieve robust utilization guarantees. The control framework has also been successfully applied to other distributed real-time applications such as end-to-end delay control in real-time image transmission. Our results show that adaptive QoS control middleware is a step towards self-managing, self-healing and self-tuning distributed computing platform

Resource Management in Distributed Camera Systems

The aim of this work is to investigate different methods to solve the problem of allocating the correct amount of resources (network bandwidth and storage space) to video camera systems. Here we explore the intersection between two research areas: automatic control and game theory. Camera systems are a good example of the emergence of the Internet of Things (IoT) and its impact on our daily lives and the environment. We aim to improve today’s systems, shift from resources over-provisioning to allocate dynamically resources where they are needed the most. We optimize the storage and bandwidth allocation of camera systems to limit the impact on the environment as well as provide the best visual quality attainable with the resource limitations. This thesis is written as a collection of papers. It begins by introducing the problem with today’s camera systems, and continues with background information about resource allocation, automatic control and game theory. The third chapter de- scribes the models of the considered systems, their limitations and challenges. It then continues by providing more background on the automatic control and game theory techniques used in the proposed solutions. Finally, the proposed solutions are provided in five papers.Paper I proposes an approach to estimate the amount of data needed by surveillance cameras given camera and scenario parameters. This model is used for calculating the quasi Worst-Case Transmission Times of videos over a network. Papers II and III apply control concepts to camera network storage and bandwidth assignment. They provide simple, yet elegant solutions to the allocation of these resources in distributed camera systems. Paper IV com- bines pricing theory with control techniques to force the video quality of cam- era systems to converge to a common value based solely on the compression parameter of the provided videos. Paper V uses the VCG auction mechanism to solve the storage space allocation problem in competitive camera systems. It allows for a better system-wide visual quality than a simple split allocation given the limited system knowledge, trust and resource constraints

Computation-Communication Trade-offs and Sensor Selection in Real-time Estimation for Processing Networks

Recent advances in electronics are enabling substantial processing to be performed at each node (robots, sensors) of a networked system. Local processing enables data compression and may mitigate measurement noise, but it is still slower compared to a central computer (it entails a larger computational delay). However, while nodes can process the data in parallel, the centralized computational is sequential in nature. On the other hand, if a node sends raw data to a central computer for processing, it incurs communication delay. This leads to a fundamental communication-computation trade-off, where each node has to decide on the optimal amount of preprocessing in order to maximize the network performance. We consider a network in charge of estimating the state of a dynamical system and provide three contributions. First, we provide a rigorous problem formulation for optimal real-time estimation in processing networks in the presence of delays. Second, we show that, in the case of a homogeneous network (where all sensors have the same computation) that monitors a continuous-time scalar linear system, the optimal amount of local preprocessing maximizing the network estimation performance can be computed analytically. Third, we consider the realistic case of a heterogeneous network monitoring a discrete-time multi-variate linear system and provide algorithms to decide on suitable preprocessing at each node, and to select a sensor subset when computational constraints make using all sensors suboptimal. Numerical simulations show that selecting the sensors is crucial. Moreover, we show that if the nodes apply the preprocessing policy suggested by our algorithms, they can largely improve the network estimation performance.Comment: 15 pages, 16 figures. Accepted journal versio