Maestro Monitoring
Maestro provides powerful functionality that allows gathering, processing and delivering to users the information on infrastructure performance and cost. In such a way our application ensures good analytics for both developers and responsible managers.
Statistics are provided on two levels - Instance level and Tenant level.
For Project managers it is important to obtain detailed information on the Tenant level, such as:
The information acquired from monitoring is of sufficient help while maintaining the infrastructure and predicting upcoming expenses, as well as optimizing future costs and instances performance based on analytical data.
Statistics are provided on two levels - Instance level and Tenant level.
For Project managers it is important to obtain detailed information on the Tenant level, such as:
- cloud statistics for all projects to which the project manager is assigned
- statistics by a single project and region
- chargeback (project expenditures)
- remaining balance for the specified project
- cloud statistics for all projects, as well as for a single project and region
- statistics by performance monitoring of a single VM (CPU utilization, network traffic, etc.).
By default monitoring metrics are displayed on the respective tabs of the Cloud Management Console.
You can also set metrics to appear as additional widgets on the main Cloud Dashboard, being always at hand. To set metrics monitoring on Dashboard use the Manage Metrics wizard.
Apart from displaying metric’s value, metrics widgets also include a number of controls that make the tool usage comprehensible and easy, such as:
- Metrics update information. Displays the period of metrics coverage and the frequency of an update.
- Metrics value and scope. The value is represented as a number.
- Remove Metrics icon. Removes the metric from the Dashboard.
- Metrics name. Hover the mouse over the icon to see the description of the metric.
The information acquired from monitoring is of sufficient help while maintaining the infrastructure and predicting upcoming expenses, as well as optimizing future costs and instances performance based on analytical data.
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