A Research Roadmap for Context-Awareness-Based Self-managed Systems
Identifieur interne : 001659 ( Main/Exploration ); précédent : 001658; suivant : 001660A Research Roadmap for Context-Awareness-Based Self-managed Systems
Auteurs : Weishan Zhang [République populaire de Chine] ; Klaus Marius Hansen [Danemark] ; Paolo Bellavista [Italie]Source :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: Cloud computing, autonomic computing, pervasive and mobile computing tends to converge, maximizing the benefits from different computing paradigms. This convergence makes emerging applications such as search and rescue applications, smart city and smart planet applications, e.g., to minimize the power consumption of a full city to achieve green computing vision, more promising on the one hand, but more complex to manage on the other hand. Interesting research questions arise due to this convergence. For example, how to efficiently retrieve underlying contexts that are difficult to recognize especially with resource-limited handheld devices, how to make use of these contexts for achieving self-management, and how to process large-scale contexts. These challenges require that researchers from software engineering, artificial intelligence, pattern recognition, high-performance distributed systems, cloud and mobile computing, etc. collaborate in order to make systems work in an efficiently self-managed manner.
Url:
DOI: 10.1007/978-3-642-37804-1_28
Affiliations:
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This convergence makes emerging applications such as search and rescue applications, smart city and smart planet applications, e.g., to minimize the power consumption of a full city to achieve green computing vision, more promising on the one hand, but more complex to manage on the other hand. Interesting research questions arise due to this convergence. For example, how to efficiently retrieve underlying contexts that are difficult to recognize especially with resource-limited handheld devices, how to make use of these contexts for achieving self-management, and how to process large-scale contexts. These challenges require that researchers from software engineering, artificial intelligence, pattern recognition, high-performance distributed systems, cloud and mobile computing, etc. collaborate in order to make systems work in an efficiently self-managed manner.</div></front></TEI><affiliations><list><country><li>Danemark</li><li>Italie</li><li>République populaire de Chine</li></country></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Zhang, Weishan" sort="Zhang, Weishan" uniqKey="Zhang W" first="Weishan" last="Zhang">Weishan Zhang</name></noRegion><name sortKey="Zhang, Weishan" sort="Zhang, Weishan" uniqKey="Zhang W" first="Weishan" last="Zhang">Weishan Zhang</name></country><country name="Danemark"><noRegion><name sortKey="Hansen, Klaus Marius" sort="Hansen, Klaus Marius" uniqKey="Hansen K" first="Klaus Marius" last="Hansen">Klaus Marius Hansen</name></noRegion><name sortKey="Hansen, Klaus Marius" sort="Hansen, Klaus Marius" uniqKey="Hansen K" first="Klaus Marius" last="Hansen">Klaus Marius Hansen</name></country><country name="Italie"><noRegion><name sortKey="Bellavista, Paolo" sort="Bellavista, Paolo" uniqKey="Bellavista P" first="Paolo" last="Bellavista">Paolo Bellavista</name></noRegion><name sortKey="Bellavista, Paolo" sort="Bellavista, Paolo" uniqKey="Bellavista P" first="Paolo" last="Bellavista">Paolo Bellavista</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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