9,550 research outputs found
Autonomic Road Transport Support Systems
The work on Autonomic Road Transport Support (ARTS) presented here aims at
meeting the challenge of engineering autonomic behavior in Intelligent Transportation
Systems (ITS) by fusing research from the disciplines of traffic engineering
and autonomic computing. Ideas and techniques from leading edge artificial intelligence
research have been adapted for ITS over the last years. Examples include
adaptive control embedded in real time traffic control systems, heuristic algorithms
(e.g. in SAT-NAV systems), image processing and computer vision (e.g. in automated
surveillance interpretation). Autonomic computing which is inspired from the
biological example of the bodyâs autonomic nervous system is a more recent development.
It allows for a more efficient management of heterogeneous distributed
computing systems. In the area of computing, autonomic systems are endowed
with a number of properties that are generally referred to as self-X properties,
including self-configuration, self-healing, self-optimization, self-protection and more
generally self-management. Some isolated examples of autonomic properties such
as self-adaptation have found their way into ITS technology and have already proved
beneficial. This edited volume provides a comprehensive introduction to Autonomic
Road Transport Support (ARTS) and describes the development of ARTS systems. It
starts out with the visions, opportunities and challenges, then presents the foundations
of ARTS and the platforms and methods used and it closes with experiences
from real-world applications and prototypes of emerging applications. This makes
it suitable for researchers and practitioners in the fields of autonomic computing,
traffic and transport management and engineering, AI, and software engineering.
Graduate students will benefit from state-of-the-art description, the study of novel
methods and the case studies provided
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 âAutonomic Computingâ 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
Coordinated Autonomic Managers for Energy Efficient Date Centers
The complexity of todayâs data centers has led researchers to investigate ways in which autonomic methods can be used for data center management. Autonomic managers try to monitor and manage resources to ensure that the components they manage are self-configuring, self-optimizing, self-healing and self-protecting (so called âself-*â properties). In this research, we consider autonomic management systems for data centers with a particular focus on making data centers more energy-aware. In particular, we consider a policy based, multi-manager autonomic management systems for energy aware data centers. Our focus is on defining the foundations â the core concepts, entities, relationships and algorithms - for autonomic management systems capable of supporting a range of management configurations. Central to our approach is the notion of a âtopologyâ of autonomic managers that when instantiated can support a range of different configurations of autonomic managers and communication among them. The notion of âpolicyâ is broadened to enable some autonomic managers to have more direct control over the behavior of other managers through changes in policies. The ultimate goal is to create a management framework that would allow the data center administrator to a) define managed objects, their corresponding managers, management system topology, and policies to meet their operation needs and b) rely on the management system to maintain itself automatically. A data center simulator that computes its energy consumption (computing and cooling) at any given time is implemented to evaluate the impact of different management scenarios. The management system is evaluated with different management scenarios in our simulated data center
Recommended from our members
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
A formal approach to autonomic systems programming: the SCEL Language
The autonomic computing paradigm has been proposed to cope with size, complexity and dynamism of contemporary
software-intensive systems. The challenge for language designers is to devise appropriate abstractions
and linguistic primitives to deal with the large dimension of systems, and with their need to
adapt to the changes of the working environment and to the evolving requirements. We propose a set of
programming abstractions that permit to represent behaviors, knowledge and aggregations according to
specific policies, and to support programming context-awareness, self-awareness and adaptation. Based on
these abstractions, we define SCEL (Software Component Ensemble Language), a kernel language whose
solid semantic foundations lay also the basis for formal reasoning on autonomic systems behavior. To show
expressiveness and effectiveness of SCELâs design, we present a Java implementation of the proposed abstractions
and show how it can be exploited for programming a robotics scenario that is used as a running
example for describing features and potentials of our approac
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
Semantic-based policy engineering for autonomic systems
This paper presents some important directions in the use of ontology-based semantics in achieving the vision of Autonomic Communications. We examine the requirements of Autonomic Communication with a focus on the demanding needs of ubiquitous computing environments, with an emphasis on the requirements shared with Autonomic Computing. We observe that ontologies provide a strong mechanism for addressing the heterogeneity in user task requirements, managed resources, services and context. We then present two complimentary approaches that exploit ontology-based knowledge in support of autonomic communications: service-oriented models for policy engineering and dynamic semantic queries using content-based networks. The paper concludes with a discussion of the major research challenges such approaches raise
Towards goal-based autonomic networking
The ability to quickly deploy and efficiently manage services is critical to the telecommunications industry. Currently, services are designed and managed by different teams with expertise over a wide range of concerns, from high-level business to low level network aspects. Not only is this approach expensive in terms
of time and resources, but it also has problems to scale up to new outsourcing and/or multi-vendor models, where subsystems and teams belong to different organizations. We endorse the idea, upheld among others in the autonomic computing community, that the network and system components involved in the provision of a service must be crafted to facilitate their management. Furthermore, they should help bridge the gap between network and business concerns. In this paper, we sketch an approach based on
early work on the hierarchical organization of autonomic entities that possibly belong to different organizations. An autonomic entity governs over other autonomic entities by defining their goals. Thus, it is up to each autonomic entity to decide its line of actions in order to fulfill its goals, and the governing entity needs not know about the internals of its subordinates. We illustrate the approach with a simple but still rich example of a telecom service
- âŠ