Sentence examples for executions on the cloud from inspiring English sources

Staff in many organisations do not possess the skills to perform parameter sweep executions on the Cloud – starting up Cloud instances, deploying and managing jobs on these instances along with transferring data to and from the Cloud.

In this section we present an architecture which abstracts the user away from the complexities of using the Cloud to perform parameter sweep executions presenting them instead with a command line interface that captures the information required to run these executions on the Cloud.

Results showed that first, the actual and expected execution time on the Cloud was lower than on the non-Cloud system.

Second, there was more than 99% consistency between the actual and expected execution time on the Cloud while no comparable consistency was found on the non-Cloud system.

A framework is proposed in [68], to execute the mobile applications on the cloud virtualization environment where the user can control the deployment and execution of the application.

The first possibly charges to the objective function either the execution cost of the operator on the cloud infrastructure or on the premise infrastructure.

To the best of our knowledge, it is the first docking web environment that reduces both the dimensionality of FFR models and the overall docking execution time using an HPC environment on the cloud.

Notable examples of bioinformatics workflow execution in the cloud include [11, 33] and an interesting review on bioinformatics workflow optimization in the cloud can be found in [15].

To reduce the failure effect of workflow tasks on the Cloud resources during execution, task failures can be intelligently predicted by proactively analyzing the data of multiple scientific workflows using the state of the art of machine learning approaches for failure prediction.

Depending on the cloud infrastructure used, the Code Execution Framework influences the total cost of ownership [46] (e.g., maintenance and ownership costs), as well.

Our approach consists of two components: a testing client that executes test cases against a cloud instance and a server component running on the cloud instance and recording the traffic during test execution.