Due to the Covid pandemic, we decided to offer this lecture as a self-study course. If you are interested in attending this lecture, we will provide the required material and also an exam. Depending on the number of interested students, we will decide later on if the exam will be an oral or written exam.
Contact: Dr. Herbst nikolas.herbst@uni-wuerzburg.de, Dr. Krupitzer christian.krupitzer@uni-wuerzburg.de
Modern computer-based systems are subject to high and increasing but varying demands in terms of performance and scalability. Recent developments in information and communication technology highlight that especially mobility of such systems is increasing, e.g., visible in the context of the Internet of Things. Self-aware computing systems address these developments. More specifically, self-aware computing systems are understood as having two main properties. They (i) learn models, capturing knowledge about themselves and their environment (such as their structure, design, state, possible actions, and runtime behavior) on an ongoing basis; and (ii) reason using the models (to predict, analyze, consider, or plan), which enables them to act based on their knowledge and reasoning (for example, to explore, explain, report, suggest, self-adapt, or impact their environment).
Table of Contents:
Introduction to self-aware computing
Monitoring and modeling approaches
Techniques for learn, reasons, and act for self-aware behavior
Metrics and evaluation procedures
Case studies
Goals and Prerequisites:
The aim of this lecture is to provide an introduction to the most important methods and techniques for self-aware computing systems. Firstly, we discuss concepts and algorithms for self-aware computing systems as well as related concepts, such as Autonomic Computing, self-organized systems, or self-adaptive systems. Such systems adapt themselves, e.g. their behavior or structure to the current state of their execution environment. We discuss current application areas within the internet of things / cyber-physical systems, such as autonomic vehicles, or cloud computing, e.g., serverless computing. Then, we focus on the underlying capabilities of these systems, namely monitoring their environment and themselves, reasoning on the information for adaptation, how to improve the reasoning process through the integration of machine learning, as well as how to enact these execution, also in settings with decentralized autonomous subsystems. One important aspect is the modeling of the relevant information as well as the evaluation of the adaptation performance. Finally, we highlight the recent developments in the area in different case studies related to the internet of things / cyber-physical systems and cloud computing domains.
Contact: Dr. Herbst nikolas.herbst@uni-wuerzburg.de, Dr. Krupitzer christian.krupitzer@uni-wuerzburg.de
Modern computer-based systems are subject to high and increasing but varying demands in terms of performance and scalability. Recent developments in information and communication technology highlight that especially mobility of such systems is increasing, e.g., visible in the context of the Internet of Things. Self-aware computing systems address these developments. More specifically, self-aware computing systems are understood as having two main properties. They (i) learn models, capturing knowledge about themselves and their environment (such as their structure, design, state, possible actions, and runtime behavior) on an ongoing basis; and (ii) reason using the models (to predict, analyze, consider, or plan), which enables them to act based on their knowledge and reasoning (for example, to explore, explain, report, suggest, self-adapt, or impact their environment).
Table of Contents:
Introduction to self-aware computing
Monitoring and modeling approaches
Techniques for learn, reasons, and act for self-aware behavior
Metrics and evaluation procedures
Case studies
Goals and Prerequisites:
The aim of this lecture is to provide an introduction to the most important methods and techniques for self-aware computing systems. Firstly, we discuss concepts and algorithms for self-aware computing systems as well as related concepts, such as Autonomic Computing, self-organized systems, or self-adaptive systems. Such systems adapt themselves, e.g. their behavior or structure to the current state of their execution environment. We discuss current application areas within the internet of things / cyber-physical systems, such as autonomic vehicles, or cloud computing, e.g., serverless computing. Then, we focus on the underlying capabilities of these systems, namely monitoring their environment and themselves, reasoning on the information for adaptation, how to improve the reasoning process through the integration of machine learning, as well as how to enact these execution, also in settings with decentralized autonomous subsystems. One important aspect is the modeling of the relevant information as well as the evaluation of the adaptation performance. Finally, we highlight the recent developments in the area in different case studies related to the internet of things / cyber-physical systems and cloud computing domains.