4,698 research outputs found
Modern software cybernetics: new trends
Software cybernetics research is to apply a variety of techniques from cybernetics research to software engineering research. For more than fifteen years since 2001, there has been a dramatic increase in work relating to software cybernetics. From cybernetics viewpoint, the work is mainly on the first-order level, namely, the software under observation and control. Beyond the first-order cybernetics, the software, developers/users, and running environments influence each other and thus create feedback to form more complicated systems. We classify software cybernetics as Software Cybernetics I based on the first-order cybernetics, and as Software Cybernetics II based on the higher order cybernetics. This paper provides a review of the literature on software cybernetics, particularly focusing on the transition from Software Cybernetics I to Software Cybernetics II. The results of the survey indicate that some new research areas such as Internet of Things, big data, cloud computing, cyber-physical systems, and even creative computing are related to Software Cybernetics II. The paper identifies the relationships between the techniques of Software Cybernetics II applied and the new research areas to which they have been applied, formulates research problems and challenges of software cybernetics with the application of principles of Phase II of software cybernetics; identifies and highlights new research trends of software cybernetic for further research
Rational bidding using reinforcement learning: an application in automated resource allocation
The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems.
The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized
Policy-based techniques for self-managing parallel applications
This paper presents an empirical investigation of policy-based self-management techniques for parallel applications executing in loosely-coupled environments. The dynamic and heterogeneous nature of these environments is discussed and the special considerations for parallel applications are identified. An adaptive strategy for the run-time deployment of tasks of parallel applications is presented. The strategy is based on embedding numerous policies which are informed by contextual and environmental inputs. The policies govern various aspects of behaviour, enhancing flexibility so that the goals of efficiency and performance are achieved despite high levels of environmental variability. A prototype self-managing parallel application is used as a vehicle to explore the feasibility and benefits of the strategy. In particular, several aspects of stability are investigated. The implementation and behaviour of three policies are discussed and sample results examined
Modern software cybernetics: New trends
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Software cybernetics research is to apply a variety of techniques from cybernetics research to software engineering research. For more than fifteen years since 2001, there has been a dramatic increase in work relating to software cybernetics. From cybernetics viewpoint, the work is mainly on the first-order level, namely, the software under observation and control. Beyond the first-order cybernetics, the software, developers/users, and running environments influence each other and thus create feedback to form more complicated systems. We classify software cybernetics as Software Cybernetics I based on the first-order cybernetics, and as Software Cybernetics II based on the higher order cybernetics. This paper provides a review of the literature on software cybernetics, particularly focusing on the transition from Software Cybernetics I to Software Cybernetics II. The results of the survey indicate that some new research areas such as Internet of Things, big data, cloud computing, cyber-physical systems, and even creative computing are related to Software Cybernetics II. The paper identifies the relationships between the techniques of Software Cybernetics II applied and the new research areas to which they have been applied, formulates research problems and challenges of software cybernetics with the application of principles of Phase II of software cybernetics; identifies and highlights new research trends of software cybernetic for further research
A generic architecture style for self-adaptive cyber-physical systems
Die aktuellen Konzepte zur Gestaltung von Regelungssystemen basieren auf dynamischen
Verhaltensmodellen, die mathematische Ansätze wie Differentialgleichungen zur Ableitung der
entsprechenden Funktionen verwenden. Diese Konzepte stoßen jedoch aufgrund der zunehmenden
Systemkomplexität allmählich an ihre Grenzen. Zusammen mit der Entwicklung dieser Konzepte
entsteht eine Architekturevolution der Regelungssysteme.
In dieser Dissertation wird eine Taxonomie definiert, um die genannte Architekturevolution anhand
eines typischen Beispiels, der adaptiven Geschwindigkeitsregelung (ACC), zu veranschaulichen.
Aktuelle ACC-Varianten, die auf der Regelungstheorie basieren, werden in Bezug auf ihre Architekturen
analysiert. Die Analyseergebnisse zeigen, dass das zukünftige Regelungssystem im ACC eine
umfangreichere Selbstadaptationsfähigkeit und Skalierbarkeit erfordert. Dafür sind kompliziertere
Algorithmen mit unterschiedlichen Berechnungsmechanismen erforderlich. Somit wird die
Systemkomplexität erhöht und führt dazu, dass das zukünftige Regelungssystem zu einem
selbstadaptiven cyber-physischen System wird und signifikante Herausforderungen für die
Architekturgestaltung des Systems darstellt.
Inspiriert durch Ansätze des Software-Engineering zur Gestaltung von Architekturen von
softwareintensiven Systemen wird in dieser Dissertation ein generischer Architekturstil entwickelt. Der
entwickelte Architekturstil dient als Vorlage, um vernetzte Architekturen mit Verfolgung der
entwickelten Designprinzipien nicht nur für die aktuellen Regelungssysteme, sondern auch für
selbstadaptiven cyber-physischen Systeme in der Zukunft zu konstruieren. Unterschiedliche
Auslösemechanismen und Kommunikationsparadigmen zur Gestaltung der dynamischen Verhalten
von Komponenten sind in der vernetzten Architektur anwendbar.
Zur Bewertung der Realisierbarkeit des Architekturstils werden aktuelle ACCs erneut aufgenommen,
um entsprechende logische Architekturen abzuleiten und die Architekturkonsistenz im Vergleich zu
den originalen Architekturen basierend auf der Regelungstheorie (z. B. in Form von Blockdiagrammen)
zu untersuchen. Durch die Anwendung des entwickelten generischen Architekturstils wird in dieser
Dissertation eine künstliche kognitive Geschwindigkeitsregelung (ACCC) als zukünftige ACC-Variante
entworfen, implementiert und evaluiert. Die Evaluationsergebnisse zeigen signifikante
Leistungsverbesserungen des ACCC im Vergleich zum menschlichen Fahrer und aktuellen ACC-Varianten.Current concepts of designing automatic control systems rely on dynamic behavioral
modeling by using mathematical approaches like differential equations to
derive corresponding functions, and slowly reach limitations due to increasing
system complexity. Along with the development of these concepts, an
architectural evolution of automatic control systems is raised.
This dissertation defines a taxonomy to illustrate the aforementioned architectural
evolution relying on a typical example of control application: adaptive cruise control
(ACC). Current ACC variants, with their architectures considering control theory, are
analyzed. The analysis results indicate that the future automatic control system in ACC
requires more substantial self-adaptation capability and scalability. For this purpose,
more complicated algorithms requiring different computation mechanisms must be
integrated into the system and further increase system complexity. This makes the future
automatic control system evolve into a self-adaptive cyber-physical system and
consistitutes significant challenges for the system’s architecture design.
Inspired by software engineering approaches for designing architectures of software-intensive systems, a generic architecture style is proposed. The proposed architecture
style serves as a template by following the developed design principle to construct
networked architectures not only for the current automatic control systems but also for
self-adaptive cyber-physical systems in the future. Different triggering mechanisms and
communication paradigms for designing dynamic behaviors are applicable in the
networked architecture.
To evaluate feasibility of the architecture style, current ACCs are retaken to derive
corresponding logical architectures and examine architectural consistency compared to
the previous architectures considering the control theory (e.g., in the form of block
diagrams). By applying the proposed generic architecture style, an artificial cognitive
cruise control (ACCC) is designed, implemented, and evaluated as a future ACC in this
dissertation. The evaluation results show significant performance improvements in the
ACCC compared to the human driver and current ACC variants
Elastic-PPQ: A two-level autonomic system for spatial preference query processing over dynamic data streams
Paradigms like Internet of Things and the most recent Internet of Everything are shifting the attention towards systems able to process unbounded sequences of items in the form of data streams. In the real world, data streams may be highly variable, exhibiting burstiness in the arrival rate and non-stationarities such as trends and cyclic behaviors. Furthermore, input items may be not ordered according to timestamps. This raises the complexity of stream processing systems, which must support elastic resource management and autonomic QoS control through sophisticated strategies and run-time mechanisms. In this paper we present Elastic-PPQ, a system for processing spatial preference queries over dynamic data streams. The key aspect of the system design is the existence of two adaptation levels handling workload variations at different time-scales. To address fast time-scale variations we design a fine regulatory mechanism of load balancing supported by a control-theoretic approach. The logic of the second adaptation level, targeting slower time-scale variations, is incorporated in a Fuzzy Logic Controller that makes scale in/out decisions of the system parallelism degree. The approach has been successfully evaluated under synthetic and real-world datasets
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