Model-driven engineering and run-time model-usage in service robotics

2012 ◽  
Vol 47 (3) ◽  
pp. 73-82 ◽  
Author(s):  
Andreas Steck ◽  
Alex Lotz ◽  
Christian Schlegel
Keyword(s):  
Model Driven Engineering ◽  
Service Robotics ◽  
Time Model ◽  
Model Driven ◽  
Run Time

Model-Driven Automated Error Recovery in Cloud Computing

2011 ◽  
pp. 136-155 ◽  
Author(s):  
Yu Sun ◽  
Jules White ◽  
Jeff Gray ◽  
Aniruddha Gokhale
Keyword(s):  
Cloud Computing ◽  
Error Detection ◽  
Error Recovery ◽  
Inference Engine ◽  
Innovative Approach ◽  
Time Model ◽  
Model Driven ◽  
Recovery Pattern ◽  
On Demand ◽  
Run Time

Cloud computing provides a platform that enables users to utilize computation, storage, and other computing resources on-demand. As the number of running nodes in the cloud increases, the potential points of failure and the complexity of recovering from error states grows correspondingly. Using the traditional cloud administrative interface to manually detect and recover from errors is tedious, time-consuming, and error prone. This chapter presents an innovative approach to automate cloud error detection and recovery based on a run-time model that monitors and manages the running nodes in a cloud. When administrators identify and correct errors in the model, an inference engine is used to identify the specific state pattern in the model to which they were reacting, and to record their recovery actions. An error detection and recovery pattern can be generated from the inference and applied automatically whenever the same error occurs again.

Model-Driven Automated Error Recovery in Cloud Computing

2012 ◽  
pp. 680-700
Author(s):  
Yu Sun ◽  
Jules White ◽  
Jeff Gray ◽  
Aniruddha Gokhale
Keyword(s):  
Cloud Computing ◽  
Error Detection ◽  
Error Recovery ◽  
Inference Engine ◽  
Innovative Approach ◽  
Time Model ◽  
Model Driven ◽  
Recovery Pattern ◽  
On Demand ◽  
Run Time

Cloud computing provides a platform that enables users to utilize computation, storage, and other computing resources on-demand. As the number of running nodes in the cloud increases, the potential points of failure and the complexity of recovering from error states grows correspondingly. Using the traditional cloud administrative interface to manually detect and recover from errors is tedious, time-consuming, and error prone. This chapter presents an innovative approach to automate cloud error detection and recovery based on a run-time model that monitors and manages the running nodes in a cloud. When administrators identify and correct errors in the model, an inference engine is used to identify the specific state pattern in the model to which they were reacting, and to record their recovery actions. An error detection and recovery pattern can be generated from the inference and applied automatically whenever the same error occurs again.

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