An Energy Aware Context Model for Green IT Service Centers

In this paper we propose the development of an Energy Aware Context Model for representing the service centre energy/performance related data in a uniform and machine interpretable manner. The model is instantiated at run-time with the service center energy/performance data collected by monitoring tools. Energy awareness is achieved by using reasoning processes on the model instance ontology representation to determine if the service center Green and Key Performance Indicators (GPIs/KPIs) are fulfilled in the current context. If the predefined GPIs/KPIs are not fulfilled, the model is used as primary resource to generate run-time adaptation plans that should be executed to increase the service center's greenness level.

[1]  Abraham Bernstein,et al.  Querying the Semantic Web with Ginseng: A Guided Input Natural Language Search Engine , 2009 .

[2]  Kenneth M. Anderson,et al.  Templates and queries in contextual hypermedia , 2006, HYPERTEXT '06.

[3]  Werner Retschitzegger,et al.  Context-awareness on mobile devices - the hydrogen approach , 2003, 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the.

[4]  Tudor Cioara,et al.  A Policy-Based Context Aware Self-Management Model , 2009, 2009 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.

[5]  Yuan-Kai Wang,et al.  Context awareness and adaptation in mobile learning , 2004, The 2nd IEEE International Workshop on Wireless and Mobile Technologies in Education, 2004. Proceedings..

[6]  Matthias Baldauf,et al.  A survey on context-aware systems , 2007, Int. J. Ad Hoc Ubiquitous Comput..

[7]  Michael Grossniklaus Context-Aware Data Management- An Object-Oriented Version Model , 2007 .

[8]  Jianhua Ma,et al.  iMuseum: A scalable context-aware intelligent museum system , 2008, Comput. Commun..

[9]  Daniel Moldovan,et al.  An autonomic algorithm for energy efficiency in service centers , 2010, Proceedings of the 2010 IEEE 6th International Conference on Intelligent Computer Communication and Processing.

[10]  Ladan Tahvildari,et al.  Adaptive Action Selection in Autonomic Software Using Reinforcement Learning , 2008, Fourth International Conference on Autonomic and Autonomous Systems (ICAS'08).

[11]  Claudio Bettini,et al.  Composition and Generalization of Context Data for Privacy Preservation , 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).

[12]  Danny Raz,et al.  Fast and Efficient Context-Aware Services (Wiley Series on Communications Networking & Distributed Systems) , 2006 .

[13]  Schahram Dustdar,et al.  A survey on context-aware web service systems , 2009, Int. J. Web Inf. Syst..

[14]  Joan Serrat,et al.  Fast and efficient context-aware services , 2006, Wiley series in communications networking and distributed systems.

[15]  Tudor Cioara,et al.  A Generic Context Model Enhanced with Self-Configuring Features , 2009, J. Digit. Inf. Manag..

[16]  Alexandre Rademaker,et al.  Ontology and Context , 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).

[17]  Thomas Kirste,et al.  Implementing Scenarios in a Smart Learning Environment , 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).

[18]  Danny Raz,et al.  Fast and Efficient Context-Aware Services: Raz/Fast and Efficient Context-Aware Services , 2006 .

[19]  Daniel Moldovan,et al.  A context aware self-adapting algorithm for managing the energy efficiency of IT service centres , 2011, UbiComp 2011.

[20]  Zhou Xingshe,et al.  Integrating Context Aware with Sensornet , 2005, 2005 First International Conference on Semantics, Knowledge and Grid.

[21]  Yarden Katz,et al.  Pellet: A practical OWL-DL reasoner , 2007, J. Web Semant..