The anatomy of a context-aware application
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Energy conservation in heterogeneous server clusters
Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
Principles of Constraint Programming
Principles of Constraint Programming
Pervasive Service Composition in the Home Network
AINA '07 Proceedings of the 21st International Conference on Advanced Networking and Applications
Exploiting Platform Heterogeneity for Power Efficient Data Centers
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
LiteGreen: saving energy in networked desktops using virtualization
USENIXATC'10 Proceedings of the 2010 USENIX conference on USENIX annual technical conference
USENIXATC'10 Proceedings of the 2010 USENIX conference on USENIX annual technical conference
Evaluating the effectiveness of model-based power characterization
USENIXATC'11 Proceedings of the 2011 USENIX conference on USENIX annual technical conference
Parasite: A System for Energy Saving with Performance Improvement in Networked Desktops
GREENCOM '11 Proceedings of the 2011 IEEE/ACM International Conference on Green Computing and Communications
Components mobility for energy efficiency of digital home
Proceedings of the 16th International ACM Sigsoft symposium on Component-based software engineering
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As our society becomes more and more digital, the corresponding demand for electric energy is increasing. Despite the power efficient design of devices, this rising trend of energy consumption does not weaken because of more and more devices used in our daily life. Collaboration strategies between devices can reduce their overall electrical consumption. Consolidation -- i.e., migrating tasks among devices to place into low power state or to switch off a maximum of unused devices -- is a mean of optimizing the consumption of a group of devices. So far, consolidation is mainly used in datacenters. Here, we propose a model to extend this approach to Digital Home. This model takes into account properties, such as the unforeseeable appearance of devices or restrictions due to task nature. Its implementation in a Digital Home environment saves around 25% of the energy consumption in a scenario based on the daily life of a family of four persons.