11,399 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
Management and Service-aware Networking Architectures (MANA) for Future Internet Position Paper: System Functions, Capabilities and Requirements
Future Internet (FI) research and development threads have recently been gaining momentum all over the world and as such the international race to create a new generation Internet is in full swing: GENI, Asia Future Internet, Future Internet Forum Korea, European Union Future Internet Assembly (FIA). This is a position paper identifying the research orientation with a time horizon of 10 years, together with the key challenges for the capabilities in the Management and Service-aware Networking Architectures (MANA) part of the Future Internet (FI) allowing for parallel and federated Internet(s)
Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges
Cloud computing is offering utility-oriented IT services to users worldwide.
Based on a pay-as-you-go model, it enables hosting of pervasive applications
from consumer, scientific, and business domains. However, data centers hosting
Cloud applications consume huge amounts of energy, contributing to high
operational costs and carbon footprints to the environment. Therefore, we need
Green Cloud computing solutions that can not only save energy for the
environment but also reduce operational costs. This paper presents vision,
challenges, and architectural elements for energy-efficient management of Cloud
computing environments. We focus on the development of dynamic resource
provisioning and allocation algorithms that consider the synergy between
various data center infrastructures (i.e., the hardware, power units, cooling
and software), and holistically work to boost data center energy efficiency and
performance. In particular, this paper proposes (a) architectural principles
for energy-efficient management of Clouds; (b) energy-efficient resource
allocation policies and scheduling algorithms considering quality-of-service
expectations, and devices power usage characteristics; and (c) a novel software
technology for energy-efficient management of Clouds. We have validated our
approach by conducting a set of rigorous performance evaluation study using the
CloudSim toolkit. The results demonstrate that Cloud computing model has
immense potential as it offers significant performance gains as regards to
response time and cost saving under dynamic workload scenarios.Comment: 12 pages, 5 figures,Proceedings of the 2010 International Conference
on Parallel and Distributed Processing Techniques and Applications (PDPTA
2010), Las Vegas, USA, July 12-15, 201
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