vania-janet/multiturn-rag-retrieval-data huggingface dataset:
Monitoring network load balancer metrics 

IBM Cloud® Network Load Balancer for VPC monitoring metrics are provided with IBM Cloud Monitoring, a monitoring service that specializes in data aggregation, usage alerts, and in-depth visualizations. For more information, see [IBM Cloud Monitoring](https://cloud.ibm.com/docs/monitoring).

Network load balancers calculate the metrics and send those metrics to your monitoring instance, which reflects different types of use and traffic. You can visualize and analyze metrics from either the IBM Cloud Monitoring dashboard, or its API.



 Metrics available by service plan 

The supported monitoring metrics include:



* Active connections to your load balancer at a given time.
* Connection rate, or an analysis of when more or less connections are made to your load balancer.



These metrics help track the traffic and usage patterns for your NLB and can provide insight about peak traffic hours, usage drop-offs, and overall usage patterns.

Each metric is composed of the following metadata types:



* Metric name - The name for the collected metric.
* Metric type - Metric type determines whether the metric value is a counter metric or a gauge metric. Each of these metrics is of the type gauge, which represents a single numerical value that can arbitrarily fluctuate over time.
* Value type - A unit of measurement for a specific metric. Examples include bytes or counts. A value type of none means that the metric value represents individual occurrences of that metric type.
* Segment - How you want to divide and display the monitoring metrics.





 Active connections 

Active connections are the number of connections established on a load balancer at a specific time.

The active connection metric contains the following metadata:



Table 1: IBM Load Balancer for VPC active connections metrics metadata

 Metadata Description 

 Metric name ibm_is_load_balancer_active_connections 
 Metric type gauge 
 Value type none 
 Segment by IBM Load Balancer for VPC appliance metrics and IBM Load Balancer for VPC listener metrics 







 Connection rate 

Connection rate is the number of new, incoming active connections per second to your load balancer.



Table 2: IBM Load Balancer for VPC connection rate metric metadata
Data from the two facets is displayed in a graph.

Zoom

![Facet pair comparison graph](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/cm-facet-pair.png)

Figure 7. Facet comparison graph



The graph shows two numbers. The first number is a frequency count and the second number is a relevancy value. The frequency count measures how many times the two data points are found together in a document. Relevancy measures the level of uniqueness of the frequency count compared to other documents that match your query. If the relevancy shows 2.0, it means that the number of times that the two data points intersect is 2 times larger than expected. To help you identify anomalies that might require more in-depth analysis, high relevancy values are shown in shades of color with increasing intensity, from yellow to orange to red.





 Analyze relationships between many facets 

Use Connections analysis to see how multiple facets are related to each other.

To compare two or more facets, complete the following steps:



1. From the Facet analysis pane, select Connections.
2. Select the root facet that you want to compare to other facets first.
3. Select up to 4 more facets from the list, and then click Analyze.

Pair analysis is done between the first facet and each other facet in turn.

Zoom

![Facet group comparison graph](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/cm-facet-network.png)

Figure 8. Facet network graph

The resulting network graph shows only highly relevant and high-frequency pairs. Each node represents a facet value. The node color reflects the facet type.
Metric Name ibm_watsonx_data_queries_running_total 
 Metric Type gauge 
 Value Type none 
 Segment By Service Instance, Resource group 
 Metric Description Runs Query to get the total queries that are in Running state in Presto Server. 







 Number of total running tasks 



Table 7: Number Of Total Running Tasks metric metadata

 Metadata Description 

 Metric Name ibm_watsonx_data_tasks_running_total 
 Metric Type gauge 
 Value Type none 
 Segment By Service Instance, Resource group 
 Metric Description Shows the data that is collected by name-value pair collection for tasks in Presto server and populates the tables in the database. 







 Number of total transactions 



Table 8: Number Of Total Transactions metric metadata

 Metadata Description 

 Metric Name ibm_watsonx_data_transactions_total 
 Metric Type gauge 
 Value Type none 
 Segment By Service Instance, Resource group 
 Metric Description Shows the data that is collected by name-value pair collection for transactions in Presto server and populates the tables in the database. 







 List of currently available catalogs 



Table 3: List Of Currently Available Catalogs metric metadata

 Metadata Description 

 Metric Name ibm_watsonx_data_catalogs 
 Metric Type gauge 
 Value Type none 
 Segment By Service Instance, Resource group 







 Presto health status 



Table 9: Presto Health Status metric metadata

 Metadata Description 

 Metric Name ibm_watsonx_data_presto_health 
 Metric Type gauge 
 Value Type none 
 Segment By Service Instance, Resource group 
 Metric Description Tracks the heartbeat status by checking if the Presto server is running or not running. 







 Total number of active nodes 



Table 10: Total Number Of Active Nodes metric metadata

 Metadata Description 

 Metric Name ibm_watsonx_data_active_nodes_total 
 Metric Type gauge 
 Value Type none 
 Segment By Service Instance, Resource group 
 Metric Description The total number of active nodes in the Presto Co-ordinator Pod. 







 Total number of inactive nodes 



Table 11: Total Number Of Inactive Nodes metric metadata

 Metadata Description 

 Metric Name ibm_watsonx_data_inactive_nodes_total 
 Metric Type gauge 
 Value Type none 
 Segment By Service Instance, Resource group
You can scope down your metrics by using the following scope filters.



Table 4. Describes the scope filters for Hyper Protect Crypto Services metrics.

 Attribute Name Description 

 ibmResourceGroupName The name of the resource group associated with the Hyper Protect Crypto Services service instance. 
 ibmScope The account, organization, or space GUID associated with the metric. 
 ibmServiceInstanceName The service instance associated with the metric. 
 ibmHpcsApi The Hyper Protect Crypto Services API calls associated with the metric. 



Because of Monitoring limitations, you are able to see the values in the filters for up to 6 hours at a time. You can manually type in value into scope variables to use scope filters for given time periods.







 Setting Alerts 

You can set alerts on your Monitoring dashboard to notify you of certain metrics. To set up alerts, complete the following steps:



1. Click Alerts on the side menu.
2. Click Add Alert and select Metric as the alert type.
3. Select the aggregation and the metric that you would like to be performed on.
4. Select the scope if applicable.
5. Set the metric and time requirements for the alert to trigger.
6. Configure and set up the notification channel and notification interval.
7. Click CREATE.



For more information about configuring metric alerts, see [Metric Alerts](https://docs.sysdig.com/en/metric-alerts.html).
processing_metrics_interval: 0.25,
interim_results: true,
speaker_labels: true
};
websocket.send(JSON.stringify(message));
websocket.send(blob);
}

The following example output shows the first few processing metrics results that the service returns for the request.

{
"processing_metrics": {
"processed_audio": {
"received": 7.04,
"seen_by_engine": 1.59,
"transcription": 0.49,
"speaker_labels": 0.0
},
"wall_clock_since_first_byte_received": 0.32,
"periodic": true
}
}
{
"processing_metrics": {
"processed_audio": {
"received": 7.04,
"seen_by_engine": 1.59,
"transcription": 0.49,
"speaker_labels": 0.0
},
"wall_clock_since_first_byte_received": 0.51,
"periodic": false
},
"result_index": 0,
"results": [
{
"alternatives":
{
"timestamps":

"hello",
0.43,
0.76
],

"world",
0.76,
1.22
]
],
"transcript": "hello world "
}
],
"final": false
}
]
}
{
"processing_metrics": {
"processed_audio": {
"received": 7.04,
"seen_by_engine": 2.59,
"transcription": 1.25,
"speaker_labels": 0.0
},
"wall_clock_since_first_byte_received": 0.57,
"periodic": true
}
Operational monitoring for gauging system health is a very important complement to [monitoring for security and compliance](https://cloud.ibm.com/docs/framework-financial-services?topic=framework-financial-services-best-practicesbest-practices-security-compliance-monitoring). Operational metrics include measurements for CPU usage, memory usage, API response times, etc.

Most Relevant Controls:



Table 9. Related controls in IBM Cloud Framework for Financial Services

 Family Control 

 Contingency Planning (CP) [CP-2 (3) Contingency Plan | Resume Essential Missions / Business Functions](https://cloud.ibm.com/docs/framework-financial-services-controls?topic=framework-financial-services-controls-cp-2.3) <br>[CP-6 Alternate Storage Site](https://cloud.ibm.com/docs/framework-financial-services-controls?topic=framework-financial-services-controls-cp-6) <br>[CP-7 Alternate Processing Site](https://cloud.ibm.com/docs/framework-financial-services-controls?topic=framework-financial-services-controls-cp-7) <br>[CP-10 Information System Recovery and Reconstitution](https://cloud.ibm.com/docs/framework-financial-services-controls?topic=framework-financial-services-controls-cp-10) 
 System and Information Integrity (SI) [SI-11 Error Handling](https://cloud.ibm.com/docs/framework-financial-services-controls?topic=framework-financial-services-controls-si-11) 







 10. Follow secure development processes and ensure software integrity 

Requirement: Ensure that security engineering principles are applied in the design, development, implementation, and modification of the system. Maintain software integrity by using signed images, applying security patches, doing vulnerability scans, etc.

Purpose & value: It is critical to follow security engineering principles and manage changes to your service using a system development life cycle (SDLC) that incorporates information security considerations. Doing so minimizes the risk of introducing code or configuration changes that undermine your system security.

Lack of software integrity leaves you vulnerable to security problems.
The raw format data contains a series of text strings, identifying the name of the metric and associated values.

The text string (for example sumSeries(net.cloudant.mycustomer001.db.df.srv.used)) is the name of the metric. The next two numbers are the start and end times, expressed as UTC epoch seconds. The final number is the step size in seconds.

The numbers after the | character contain the metric data that is obtained from your chosen endpoint. For example, requesting metric data from the disk use endpoint returns the output from a df command, with the disk use expressed as bytes stored.

See an example monitoring request for disk use data returned in raw format:

curl "https://$ACCOUNT.cloudant.com/_api/v2/monitoring/disk_use?cluster=myclustername&format=raw"

See an example result after you request disk use data in raw format:

sumSeries(net.cloudant.mycustomer001.db.df.srv.used),1391019780,1391020080,60|344708448256.0,345318227968.0,346120126464.0,346716471296.0,175483256832.0
sumSeries(net.cloudant.mycustomer001.db.df.srv.free),1391019780,1391020080,60|6.49070326579e+12,6.4896982057e+12,6.48884414054e+12,6.48801589658e+12,4.32277107507e+12







 Monitoring endpoints 

To list all of the currently supported monitoring endpoints, make a request to the monitoring endpoint.

The following table lists the supported monitoring endpoints that are provided by the API:



Table 3. Monitoring API endpoints

 Endpoint Description
Monitoring metrics for Continuous Delivery 

IBM Cloud® Monitoring is a third-party cloud-native, and container-intelligence management system that you can include as part of your IBM Cloud architecture. Use it to gain operational visibility into the performance and health of your applications, services, and platforms. It offers administrators, DevOps teams and developers full stack telemetry with advanced features to monitor and troubleshoot, define alerts, and design custom dashboards.



 Platform metrics overview 

You can configure one instance only of the IBM Cloud Monitoring service per region to collect platform metrics.



* To configure the Monitoring instance, you must set the platform metrics configuration.
* If a Monitoring instance in a region is already enabled to collect platform metrics, metrics from enabled-monitoring services are collected automatically and available for monitoring through this instance.
* After a Monitoring instance in an account and region is enabled to collect platform metrics, it automatically collects metrics from all Continuous Delivery service instances in the same account and region. You do not need to opt in to the Continuous Delivery service metrics collection. You also cannot opt out of the Continuous Delivery service metrics collection.



To monitor platform metrics, check that the IBM Cloud Monitoring instance is provisioned in the same region where the Continuous Delivery instance is provisioned.





 Enabling platform metrics from the Continuous Delivery dashboard 

Complete the following steps to configure platform metrics:



1. Log in to IBM Cloud.
2. Click Resource list.
3. In the Services and software section, click the Continuous Delivery instance that you plan to monitor.
4. From the Manage page, click Actions > Add monitoring to configure platform metrics in the region of your Continuous Delivery instance.

If the menu choices include the Monitoring option, then the account and region that contain the Continuous Delivery instance is already configured for platform metrics.
5. Provision an instance of the IBM Cloud Monitoring service.

After you provision the Monitoring instance, the Observability page opens. To continue working with Continuous Delivery, go back to the Continuous Delivery UI.
Metric Name ibm_watsonx_data_active_nodes_total 
 Metric Type gauge 
 Value Type none 
 Segment By Service Instance, Resource group 
 Metric Description The total number of active nodes in the Presto Co-ordinator Pod. 







 Total number of inactive nodes 



Table 11: Total Number Of Inactive Nodes metric metadata

 Metadata Description 

 Metric Name ibm_watsonx_data_inactive_nodes_total 
 Metric Type gauge 
 Value Type none 
 Segment By Service Instance, Resource group 
 Metric Description The total number of inactive nodes in the Presto Co-ordinator Pod. 







 Total number of nodes 



Table 12: Total Number Of Nodes metric metadata

 Metadata Description 

 Metric Name ibm_watsonx_data_nodes_total 
 Metric Type gauge 
 Value Type none 
 Segment By Service Instance, Resource group 
 Metric Description The total number of nodes in the Presto Co-ordinator Pod. 







 List of roles 



Table 4: List Of Roles metric metadata

 Metadata Description 

 Metric Name ibm_watsonx_data_roles_name 
 Metric Type gauge 
 Value Type none 
 Segment By Service Instance, Resource group 







 List of tables 



Table 5: List Of Tables metric metadata

 Metadata Description 

 Metric Name ibm_watsonx_data_table_total 
 Metric Type gauge 
 Value Type none 
 Segment By Service Instance, Resource group 









 Attributes for segmentation 



 Global attributes 

The following attributes are available for segmenting all of the metrics that are listed above.



Table 13: Global attributes

 Attribute Attribute Name Attribute Description 

 Cloud Type ibm_ctype The cloud type is a value of public, dedicated, or local 
 Location ibm_location The location of the monitored resource - this can be a region, data center or global 
 Resource ibm_resource The resource being measured by the service - typically an indentifying name or GUID 
 Resource Type ibm_resource_type The type of the resource being measured by the service 
 Resource group ibm_resource_group_name The resource group where the service instance was created 
 Scope ibm_scope The scope is the account, organization, or space GUID associated with this metric 
 Service name ibm_service_name Name of the service generating this metric 







 Additional attributes
],
"yaxis14": null,
"yaxis15": null,
"yaxis16": null,
"yaxis17": null,
"yaxis18": null,
"yaxis19": null,
"yaxis20": null
}
}
Show more





 Example code for getting real-time metrics 

This example shows you how to get the real-time metrics with getCustomerRealTimeMetrics and getMappingRealTimeMetrics in the class SoftLayer_Network_CdnMarketplace_Metrics.

To get the real-time metrics for a customer, you can call the getCustomerRealTimeMetrics API.

$client = SoftLayerSoapClient::getClient('SoftLayer_Network_CdnMarketplace_Metrics', null, $apiUsername, $apiKey);
try {
$metrics = $client->getCustomerRealTimeMetrics(
'akamai',
1576627200, // Start timestamp, must be later than 48 hours ago
1576663200, // End timestamp, must be later than start time
60 // Time interval, must be a multiple of 60 seconds
);
print_r($vendors);
} catch (Exception $e) {
die('Unable to get customer real-time metrics: ' . $e->getMessage());
}

The getCustomerRealTimeMetrics API returns an array of metrics. Example output is as follows:

(
[0] => SoftLayer_Container_Network_CdnMarketplace_Metrics: {
type: 'REALTIME',
names: [
'TotalHits',
'TotalBandwidth'
],
totals: [
1301, Total hits from the start time to the end time
20.05 Total bandwidth from the start time to the end time
],
time: [
1576627200,
url: "http://10.245.0.5:9182/metrics"
tags:
region: us-east
instance: my-windows-hostname
job: my-job-name
3. Configure the monitoring agent to reduce the number of metrics that are collected by the Windows windows_exporter.

You can configure the metrics_filter section to remove metrics. For example, you can remove collector metrics. You can also remove specific metrics that you do not wish to collect.

For example, to remove go metrics and the logical disk request metrics, you can set the section in the following way:

metrics_filter:
- exclude: go_
- exclude: windows_logical_disk_requests_queued

The go_* metrics are metrics generated by the Go programming language, and are collected by default. The windows_logical_disk_requests_queued is the number of requests outstanding on the disk at the time the performance data is collected.
4. Restart the monitoring agent. Run the following command:

service dragent restart







 Option 2. Collect metrics by running Prometheus as a client collector on Windows 

Use the Prometheus remote-write capabilities to push the metrics from the Windows system by running Prometheus as a client collector on Windows.

Complete the following steps:



1. Download the Prometheus monitoring system and time series database. [Download prometheus-2.27.1.windows-amd64.tar.gz](https://prometheus.io/download/)
2. Unzip the file prometheus-2.27.1.windows-amd64.tar.gz.
3. Edit the prometheus.yml file. For example, you can edit it with Notepad.
4. Configure the scrape_configs section of prometheus.yml configuration file as follows to have prometheus scrape the windows_exporter.

scrape_configs:
Monitoring metrics using IBM Cloud Monitoring 

Support for the IBM Cloud Monitoring service ended 31 March 2020. IBM Cloud® Load Balancer monitoring is now provided with IBM Cloud Monitoring, a third-party monitoring tool that specializes in data aggregation, usage alerts, and in-depth visualizations. For more information, see [IBM Cloud Monitoring](https://www.ibm.com/cloud/sysdig).

Load balancers calculate the metrics and send those metrics to your IBM Cloud Monitoring instance, which reflects different types of use and traffic. You can visualize and analyze metrics from either the IBM Cloud Monitoring dashboard, or its API.



 Metrics available by service plan 

The supported monitoring metrics include:



* Active connections to your load balancer at a given time.
* Throughput of data passing through your load balancer over a given time.
* Connection rate, or an analysis of when more or less connections are made to your load balancer.



These metrics help track the traffic and usage patterns for your load balancers and can provide insight about peak traffic hours, usage dropoffs, and overall usage patterns.

Each metric is composed of the following metadata types:



* Metric name - The name for the collected metric.
* Metric type - Determines whether the metric value is a counter metric or a gauge metric. Each of these metrics is of the type gauge, which represents a single numerical value that can arbitrarily fluctuate over time.
* Value type - A unit of measurement for a specific metric. Examples include bytes or counts. A value type of none means that the metric value represents individual occurrences of that metric type.
* Segment - How you want IBM Cloud Monitoring to divide and display the monitoring metrics.





 Active connections 

Active connections are the number of connections that are established on a load balancer at a specific time.

The active connection metric contains the following metadata:



Table 1: IBM Cloud Load Balancer active connections metrics metadata

 Metadata Description 

 Metric name ibm_cloud_load_balancer_active_connections 
 Metric type gauge 
 Value type none 
 Segment by IBM Cloud Load Balancer appliance metrics and IBM Cloud Load Balancer listener metrics 







 Connection rate
Metric Description The number of hours since there was a last successful backup. 







 Total Activities Aborted 



Table 14. Total activities aborted metric

 Metadata Description 

 Metric Name ibm_db2_db_act_aborted 
 Metric Type counter 
 Value Type none 
 Segment By Service instance, Resource 
 Metric Description The total number of coordinator activities at any nesting level that completed with errors. 







 Total Activities Completed 



Table 15. Total activities completed metric

 Metadata Description 

 Metric Name ibm_db2_db_act_completed 
 Metric Type counter 
 Value Type none 
 Segment By Service instance, Resource 
 Metric Description The total number of coordinator activities at any nesting level that completed successfully. 







 Total Activities Rejected 



Table 16. Total activities rejected metric

 Metadata Description 

 Metric Name ibm_db2_db_act_rejected 
 Metric Type counter 
 Value Type none 
 Segment By Service instance, Resource 
 Metric Description The total number of coordinator activities at any nesting level that were rejected instead of being allowed to execute. 







 Total Connections 



Table 17. Total connections metric

 Metadata Description 

 Metric Name ibm_db2_total_conn 
 Metric Type gauge 
 Value Type none 
 Segment By Service instance, Resource 
 Metric Description Indicates the number of applications that are currently connected to the database. 







 Total number of commits 



Table 18. Total number of commits metric

 Metadata Description 

 Metric Name ibm_db2_db_num_commits 
 Metric Type counter 
 Value Type none 
 Segment By Service instance , Resource 
 Metric Description Total number of commit statements issued by the client application and the total number of commits initiated internally by the database manager. 









 Attributes for Segmentation 



 Global Attributes 

The following attributes are available for segmenting all of the metrics listed above



 Attribute Attribute Name Attribute Description 

 Cloud Type ibm_ctype The cloud type is a value of public, dedicated or local 
 Location ibm_location The location of the monitored resource - this may be a region, data center or global 
 Resource ibm_resource The database member resource being measured by the service 
 Scope ibm_scope The scope is the account, organization or space GUID associated with this metric