Simulating Live Cloud Adaptations Prior to a Production Deployment using a Models at Runtime Approach

2021 ◽  
Author(s):  
Johannes Erbel ◽  
Alexander Trautsch ◽  
Jens Grabowski
Keyword(s):  
Models At Runtime

Autonomic Computing through Reuse of Variability Models at Runtime: The Case of Smart Homes

Computer ◽  
2009 ◽  
Vol 42 (10) ◽  
pp. 37-43 ◽  
Author(s):  
Carlos Cetina ◽  
Pau Giner ◽  
Joan Fons ◽  
Vicente Pelechano
Keyword(s):  
Autonomic Computing ◽  
Smart Homes ◽  
Models At Runtime

Using Models at Runtime to Support Adaptable Monitoring of Multi-clouds Applications

2014 ◽  
Author(s):  
Lorenzo Cianciaruso ◽  
Francesco Di Forenza ◽  
Elisabetta Di Nitto ◽  
Marco Miglierina ◽  
Nicolas Ferry ◽  
...  
Keyword(s):  
Models At Runtime

Ubiquitous User Interfaces

2010 ◽  
pp. 24-52 ◽  
Author(s):  
Marco Blumendorf ◽  
Grzegorz Lehmann ◽  
Dirk Roscher ◽  
Sahin Albayrak
Keyword(s):  
User Interface ◽  
User Interfaces ◽  
Multimodal Interaction ◽  
Interactive Systems ◽  
Interface Models ◽  
Multiple Devices ◽  
Smart Home Environment ◽  
Multimodal User Interfaces ◽  
Models At Runtime ◽  
User Device

The widespread use of computing technology raises the need for interactive systems that adapt to user, device and environment. Multimodal user interfaces provide the means to support the user in various situations and to adapt the interaction to the user’s needs. In this chapter we present a system utilizing design-time user interface models at runtime to provide flexible multimodal user interfaces. The server-based system allows the combination and integration of multiple devices to support multimodal interaction and the adaptation of the user interface to the used devices, the user and the environment. The utilization of the user interface models at runtime allows exploiting the design information for advanced adaptation possibilities. An implementation of the system has been successfully deployed in a smart home environment throughout the Service Centric Home project (www.sercho.de).

Predictions-on-chip: model-based training and automated deployment of machine learning models at runtime

2021 ◽  
Author(s):  
Sebastian Pilarski ◽  
Martin Staniszewski ◽  
Matthew Bryan ◽  
Frederic Villeneuve ◽  
Dániel Varró
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Model Based ◽  
On Chip ◽  
Models At Runtime ◽  
Machine Learning Models
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