32,032 research outputs found
An artificial hormone system for self-organization of networked nodes
The rising complexity of distributed computer systems give reason to investigate self-organization mechanism to build systems that are self-managing in the sense of Autonomic and Organic Computing.
In this paper we propose the Artificial Hormone System (AHS) as a general approach to build self-organizing systems based on networked nodes. The Artificial Hormone System implements a similar information exchange between networked nodes like the human hormone system does between cells. The artificial hormone values are piggy-backed on messages to minimize communication overhead.
To show the efficiency of the mechanism even for large scale systems we implemented a simulation environment in Java to evaluate different optimization strategies. The evaluations show that local information is enough to meet global optimization criterion.1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration 2Red de Universidades con Carreras en Informática (RedUNCI
Autonomic computing architecture for SCADA cyber security
Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator
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Exploring adaptation & self-adaptation in autonomic computing systems
This panel paper sets out to discuss what self-adaptation
means, and to explore the extent to which current
autonomic systems exhibit truly self-adaptive behaviour.
Many of the currently cited examples are clearly
adaptive, but debate remains as to what extent they are
simply following prescribed adaptation rules within preset
bounds, and to what extent they have the ability to
truly learn new behaviour. Is there a standard test that
can be applied to differentiate? Is adaptive behaviour
sufficient anyway? Other autonomic computing issues are
also discussed
Autonomic computing meets SCADA security
© 2017 IEEE. National assets such as transportation networks, large manufacturing, business and health facilities, power generation, and distribution networks are critical infrastructures. The cyber threats to these infrastructures have increasingly become more sophisticated, extensive and numerous. Cyber security conventional measures have proved useful in the past but increasing sophistication of attacks dictates the need for newer measures. The autonomic computing paradigm mimics the autonomic nervous system and is promising to meet the latest challenges in the cyber threat landscape. This paper provides a brief review of autonomic computing applications for SCADA systems and proposes architecture for cyber security
Incorporating prediction models in the SelfLet framework: a plugin approach
A complex pervasive system is typically composed of many cooperating
\emph{nodes}, running on machines with different capabilities, and pervasively
distributed across the environment. These systems pose several new challenges
such as the need for the nodes to manage autonomously and dynamically in order
to adapt to changes detected in the environment. To address the above issue, a
number of autonomic frameworks has been proposed. These usually offer either
predefined self-management policies or programmatic mechanisms for creating new
policies at design time. From a more theoretical perspective, some works
propose the adoption of prediction models as a way to anticipate the evolution
of the system and to make timely decisions. In this context, our aim is to
experiment with the integration of prediction models within a specific
autonomic framework in order to assess the feasibility of such integration in a
setting where the characteristics of dynamicity, decentralization, and
cooperation among nodes are important. We extend an existing infrastructure
called \emph{SelfLets} in order to make it ready to host various prediction
models that can be dynamically plugged and unplugged in the various component
nodes, thus enabling a wide range of predictions to be performed. Also, we show
in a simple example how the system works when adopting a specific prediction
model from the literature
Modeling adaptation with a tuple-based coordination language
In recent years, it has been argued that systems and applications, in order to deal with their increasing complexity, should be able to adapt their behavior according to new requirements or environment conditions. In this paper, we present a preliminary investigation aiming at studying how coordination languages and formal methods can contribute to a better understanding, implementation and usage of the mechanisms and techniques for adaptation currently proposed in the literature. Our study relies on the formal coordination language Klaim as a common framework for modeling some adaptation techniques, namely the MAPE-K loop, aspect- and context-oriented programming
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