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Customers can choose a Virtual Machine configuration from a list of pre-configured instances but cannot choose fine-grain resources.
Changing names after a full machine configuration is do-able, but can cause a lot of headaches.
This paper first illustrates such difficulties with a machine configuration frequently cited in the literature.
The results reveal the improvements achievable in the accuracy, given a particular machine configuration.
The machine configuration defines the machine productivity and the final machine cost.
According to the machine configuration, non-symmetrical rolling process is planned.
Similar(18)
The paper discusses application of the algorithm based information processes modeling and Q-machine learning to the task of virtual machines auto configuration.
To overcome this issue, we propose using method of virtual machines auto configuration.
To help cloud customers understand the tradeoff space and determine their Virtual Machine configurations, a runtime system co-designed with the configurable architecture is designed to dynamically configure the hardware by optimizing the tunable parameters in order to the meet Quality-of-Service (QoS) requirement and minimize cost.
Virtual machine configurations are stored as files with a.vmx extension.
A multi-level and multi-objective optimization method is developed to obtain the optimal design that is modeled by its machine configurations, parameters of machine configurations, reconfiguration processes to change between machine configurations, and parameters of reconfiguration processes.
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