vania-janet/multiturn-rag-retrieval-data huggingface dataset:
The dashboard shows different lines in different colors where each line represents received bytes per minute for a VPN gateway.



 Global attributes 

The following attributes are available for segmenting all of the VPN metrics:



Table 11: VPN metric attributes

 Attribute Attribute name Attribute description 

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







 Additional attributes 

The following attributes are available for segmenting one or more attributes as described in the previous reference. See the individual metrics for segmentation options.



Table 12: VPN segmentation metric attributes

 Attribute Attribute name Attribute description 

 Connection ID ibm_is_vpn_connection_id IBM VPN for VPC gateway connection ID 
 Connection name ibm_is_vpn_connection_name IBM VPN for VPC gateway connection name 
 IBM IS ibm_is_generation IBM IS; for example, 2 
 Service instance ibm_service_instance Identifies the instance that the metric is associated with 
 Service instance name ibm_service_instance_name Provides the user-provided name of the service instance. This name isn't necessarily a unique value that depends on the name that is provided. 
 VPN gateway name ibm_is_vpn_gateway_name IBM VPN for VPC gateway name 



The displayed metrics contain a timestamp in UNIX epoch time and the metric value for the time intervals that end at that timestamp. You can specify different scopes, and the time interval over which to report the metrics.

You can also specify the time interval over which to report your metrics. The following time intervals that are supported in the IBM Cloud Monitoring dashboard:



* 10 seconds
* 1 minute
* 10 minutes
* 1 hour
Metric Name ibm_databases_for_postgresql_read_replica_replication_lag_bytes 
 Metric Type gauge 
 Value Type count 
 Segment By Service instance 







 Successful archive rate 

Successful archive rate



Table 18: Successful archive rate metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_postgresql_successful_archive_rate 
 Metric Type gauge 
 Value Type rate 
 Segment By Service instance 







 Temporary files size in bytes 

Temporary files size in bytes



Table 19: Temporary files size in bytes metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_postgresql_temp_bytes_count 
 Metric Type gauge 
 Value Type none 
 Segment By Service instance 







 The total number of PostgreSQL connections being used 

The total number of PostgreSQL connections being used.



Table 20. The total number of PostgreSQL connections being used metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_postgresql_total_connections 
 Metric Type gauge 
 Value Type count 
 Segment By Service instance 







 Total disk space for an instance 

Represents the total amount of disk available to your deployment.



Table 21. Total disk space for an instance metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_postgresql_disk_total_bytes 
 Metric Type gauge 
 Value Type byte 
 Segment By Service instance 







 Transaction commit count 

Transaction commit count



Table 22: Transaction commit count metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_postgresql_transaction_commit_count 
 Metric Type gauge 
 Value Type none 
 Segment By Service instance 







 Transaction commit rate 

Transaction commit rate



Table 23: Transaction commit rate metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_postgresql_transaction_commit_rate 
 Metric Type gauge 
 Value Type rate 
 Segment By Service instance 







 Transaction rollback count 

Transaction rollback count



Table 24: Transaction rollback count metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_postgresql_transaction_rollback_count
Table 10: Number of receiving errors for a network interface metric metadata

 Metadata Description 

 Metric name ibm_is_instance_network_in_errors 
 Metric type gauge 
 Value type none 
 Segment by IBM IS Generation 2, resource name, MAC address of the network interface 







 Number of sending errors for a network interface 

The cumulative number of sending errors for a network interface since virtual server instance start.



Table 11: Number of sending errors for a network interface metric metadata

 Metadata Description 

 Metric name ibm_is_instance_network_out_errors 
 Metric type gauge 
 Value type none 
 Segment by IBM IS Generation 2, resource name, MAC address of the network interface 







 Number of dropped incoming packets for a network interface 

The cumulative number of dropped incoming packets for a network interface since virtual server instance start.



Table 12: Number of dropped incoming packets for a network interface metric metadata

 Metadata Description 

 Metric name ibm_is_instance_network_in_dropped_packets 
 Metric type gauge 
 Value type none 
 Segment by IBM IS Generation 2, resource name, MAC address of the network interface 







 Number of dropped outgoing packets for a network interface 

The cumulative number of dropped outgoing packets for a network interface since virtual server instance start.



Table 13: Number of dropped outgoing packets for a network interface metric metadata

 Metadata Description 

 Metric name ibm_is_instance_network_out_dropped_packets 
 Metric type gauge 
 Value type none 
 Segment by IBM IS Generation 2, resource name, MAC address of the network interface 









 Memory monitoring metrics 

For IBM Cloud Hyper Protect Virtual Servers for IBM Cloud® Virtual Private Cloud instances, the memory metrics can't be collected because Hyper Protect Virtual Servers for VPC instances are created by using Secure Execution images and the memory of a secure execution instance isn't accessible.



 Free memory 

The free memory of the virtual server instance in kibibytes (1024 bytes).



Table 14: Free memory metric metadata

 Metadata Description 

 Metric name ibm_is_instance_memory_free_kib 
 Metric type gauge 
 Value type byte 
 Segment by IBM IS Generation 2, resource name 







 Memory usage percentage 

The percent of used memory of the virtual server instance.



Table 15: Memory usage percentage metric metadata

 Metadata Description
[Connected clients software name and version](https://cloud.ibm.com/docs/EventStreams?topic=EventStreams-metricsibm_eventstreams_connected_clients_software_name_and_version) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/e0c3acba1296ff980e76d5db456d53adefaabba4/icons/checkmark-icon.svg) 
 [Consume message conversion time](https://cloud.ibm.com/docs/EventStreams?topic=EventStreams-metricsibm_eventstreams_instance_consume_conversions_time_quantile) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/e0c3acba1296ff980e76d5db456d53adefaabba4/icons/checkmark-icon.svg) 
 [Estimated connected clients percentage](https://cloud.ibm.com/docs/EventStreams?topic=EventStreams-metricsibm_eventstreams_kafka_recommended_max_connected_clients_percent) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/e0c3acba1296ff980e76d5db456d53adefaabba4/icons/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/e0c3acba1296ff980e76d5db456d53adefaabba4/icons/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/e0c3acba1296ff980e76d5db456d53adefaabba4/icons/checkmark-icon.svg)
Metric name ibm_is_vpn_connection_bytes_out 
 Metric type gauge 
 Value type byte 
 Segment by Service instance, VPN name, Connection name, Connection ID, IBM IS 







 VPN connection packets input 

Packets received per minute for a VPN gateway connection



Table 8: VPN connection packets input

 Metadata Description 

 Metric name ibm_is_vpn_connection_packets_in 
 Metric type gauge 
 Value type none 
 Segment by Service instance, VPN Name, Connection Name, Connection ID, IBM IS 







 VPN connection packets output 

Packets sent per minute for a VPN gateway connection



Table 9: VPN connection packets output

 Metadata Description 

 Metric name ibm_is_vpn_connection_packets_out 
 Metric type gauge 
 Value type none 
 Segment by Service instance, VPN name, Connection name, Connection ID, IBM IS 







 VPN connection status 

Status of a VPN gateway connection (for example, 1=up, 0=down)



Table 10: VPN connection status

 Metadata Description 

 Metric name ibm_is_vpn_connection_status 
 Metric type gauge 
 Value type none 
 Segment by Service instance, VPN name, Connection name, Connection ID, IBM IS 









 Metric segmentation 

You can split the metrics that IBM Cloud Monitoring presents into various visualizations in the IBM Cloud Monitoring dashboard, allowing views of different metrics based on your preference. For example, if you have multiple VPN gateways or accounts with different VPN gateways in each account, you might want to focus on a particular gateway by name.

As an example, you can segment the VPN Gateway Total Bytes Input by IBM VPN for VPC gateway name to show how many bytes per minute are received for a VPN gateway. The dashboard shows different lines in different colors where each line represents received bytes per minute for a VPN gateway.



 Global attributes 

The following attributes are available for segmenting all of the VPN metrics:



Table 11: VPN metric attributes

 Attribute Attribute name Attribute description 

 Cloud type ibm_ctype A value of public, dedicated, or local 
 Location ibm_location The location of the monitored resource - a region, data center, or global
[Mirroring throughput](https://cloud.ibm.com/docs/EventStreams?topic=EventStreams-metricsibm_eventstreams_instance_mirroring_throughput) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/e0c3acba1296ff980e76d5db456d53adefaabba4/icons/checkmark-icon.svg) 
 [Mirroring latency](https://cloud.ibm.com/docs/EventStreams?topic=EventStreams-metricsibm_eventstreams_instance_mirroring_latency) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/e0c3acba1296ff980e76d5db456d53adefaabba4/icons/checkmark-icon.svg) 







 Enhanced metrics available with topic enabled 



Table 3. Metrics available for topic

 Metric name Enterprise Lite Standard 

 [Maximum partition retention percentage](https://cloud.ibm.com/docs/EventStreams?topic=EventStreams-metricsibm_eventstreams_instance_max_partition_retention_percent) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/e0c3acba1296ff980e76d5db456d53adefaabba4/icons/checkmark-icon.svg) 
 [Topic size](https://cloud.ibm.com/docs/EventStreams?topic=EventStreams-metricsibm_eventstreams_instance_topic_size) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/e0c3acba1296ff980e76d5db456d53adefaabba4/icons/checkmark-icon.svg) 







 Enhanced metrics available with consumers enabled 



Table 4. Metrics available for consumers
They offer insight to the connections that are established between your applications, containers, and servers. For example, you can find information about the bytes that are being sent or received, or the number of HTTP requests, connections, and latency. In addition, for SQL or MongoDB, the agent collects additional information when it is configured in troubleshooting mode.



Through the Monitoring UI, you can analyze data in the Advisor tab, the Explore tab, and in the Dashboard tab. You monitor the data through metric views and dashboards.

Consider the following information when monitoring your data:



* In the Explorer tab, you can monitor individual metrics.
* In the Advisor tab, you can monitor Red Hat OpenShift or host level metrics.

This tab is only available for users that belong to a team that has access to monitor Red Hat OpenShift or host level metrics.
* In the Dashboard tab, you can monitor through panels predefined dashboards or custom ones and get a specialized insight into network data, application data, topology, services, hosts, and containers. A panel displays a metric or group of metrics in a dashboard.



For each metric view and dashboard, you can define the scope of the data, how to aggregate data, and what time and group filters to apply to the data. For more information, see [Managing panels](https://cloud.ibm.com/docs/monitoring?topic=monitoring-panels).

You can configure a dashboard as the default entry point for a team, unifying a team's experience, and allowing users to focus their immediate attention on the most relevant information for them.

For more information, see [Viewing metrics](https://cloud.ibm.com/docs/monitoring?topic=monitoring-monitoring).



 Before you begin 

[Read about IBM Cloud Monitoring](https://cloud.ibm.com/docs/monitoring?topic=monitoring-getting-started).

Work in a supported region, for example the US South region.

Use a user ID that is a member or an owner of an IBM Cloud account. To get an IBM Cloud IBMID, go to [Create an account](https://cloud.ibm.com/login).
* [Script](https://cloud.ibm.com/docs/monitoring?topic=monitoring-agent_Kube)



The Monitoring agent automatically collects the following types of system metrics per host:



* System hosts metrics provide information about CPU, memory, and storage usage metrics, that you can use to analyze the performance and resource utilization of all your processes.
* File and File System metrics provide information about files and file system that you can use to analyze file interactions that occur in your system. For example, you can find information about your open files, bytes going in and out, or the percentage of usage of a given file system.
* Process metrics provide information about the processes that run in your servers. For example, you can use these metrics to explore the number of processes, or get client or server information.
* Network metrics provide information about the network. They offer insight to the connections that are established between your applications, containers, and servers. For example, you can find information about the bytes that are being sent or received, or the number of HTTP requests, connections, and latency. In addition, for SQL or MongoDB, the agent collects additional information when it is configured in troubleshooting mode.



The Monitoring agent automatically collects the following types of metrics per Kubernetes cluster:



* State metrics: Kube state metrics report on the health and state of the various objects that run inside Kubernetes components, such as deployments, nodes and pods. To see the list of metrics that are collected by default, see Kubernetes State.
* Resource usage metrics: Resource usage metrics reports on the health and state of CPU and memory for workers (nodes) and pods that are running in the cluster. The data can be analyzed by namespace, by worker, by pod, by workload object such as deployments, daemonSets, and more.



For a list of collected metrics, see [Metrics Available for orchestrated environments](https://docs.sysdig.com/en/docs/sysdig-monitor/using-monitor/metrics/metrics-library/prometheus-format/).
Each individual bar graph highlights trends in your data that deviate from the normal distribution by displaying increase indicators.

Increase indicators measure how much the frequency of a facet value on a specific date or in a particular time interval deviates from the expected average frequency. The average is calculated based on the changes in the past time interval frequencies.



You can click individual items in a visualization or click and drag the cursor to select contiguous items.

The cyclical data is calculated from the current time zone setting of your collection. If you want to change the time zone that is used by the graph, see [Change the time zone](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-cm-edit-collectioncm-edit-collection-time-zone).





 Identify anomalies in cyclical patterns 

Use Topic analysis to find anomalies in seasonal, monthly, or even daily patterns that are present in your data.

Your documents must contain at least one date or time field for topic information to be available.

Topic analysis focuses on how much the frequency of a keyword deviates from the expected average frequency in a specific time period. The expected average uses all of the averages of the frequency counts for other keywords in the same time period. This method of analysis is useful for identifying patterns that occur cyclically and highlights any unexpected changes that might occur in these cyclical patterns.

To find anomalies, complete the following steps:



1. From the initial search page, enter a keyword or select a facet with number values to filter the documents.
2. From the search results page in guided mode, click Analyze cause or characteristics.
3. From the Facet analysis pane, select Topic.
4. Adjust the following values to suit your analysis:



* Number of results
* Date facet
* Time scale
* Date range



5. Choose a target facet or subfacet, and then click Analyze.

The resulting time series graph shows changes in the frequency of keyword mentions over time.

Zoom

!
For more information, see [Retrieving an instance ID](https://cloud.ibm.com/docs/hs-crypto?topic=hs-crypto-retrieve-instance-ID). 
 correlation_ID The unique identifier that is used to track and correlate transactions. 



A successful POST api/v2/key_rings request returns an HTTP 201 Created response, which indicates that the key ring is created and is now available for holding standard and root keys.









 Transferring a key to a different key ring 

As requirements change and new team members are brought into an org, you might create new key rings to reflect these organizational changes. After creating the key rings, it might be necessary to move a key from an existing key ring to a new key ring that has different IAM permissions. For example, you might be onboarding a team that will need specific access to a key that belongs to a custom, non-default key ring. You can create a new key ring that is dedicated to the onboarding team. Since keys can only be associated with one key ring at a time, you need to move the key to the new key ring.

After transferring a key to a different key ring, it can take up to ten minutes for the change to take effect.



 Transferring a key to a different key ring with the console 

You can transfer a key to a different key ring with the console by completing the following steps:



1. [Log in to the IBM Cloud console](https://cloud.ibm.com/login).
2. Go to Menu > Resource list to view a list of your resources.
3. From your IBM Cloud resource list, select your provisioned instance of Hyper Protect Crypto Services.
4. Select the KMS keys tab in the side menu to open the Keys table.
5. Find the key that you want to transfer from the list and click the Actions icon ![Actions icon](https://cloud.ibm.com/docs-content/v1/content/217f108cc44e2ce15fb692d4b57b4c628464e908/icons/action-menu-icon.svg) to open the option list.
Threshold value: Accuracy - 0.98 and click Next
2. Minimum sample size (number of transactions) - 10 and click Save
3. On the left pane, Click on Go to model summary



The quality monitor (previously known as the accuracy monitor) reveals how well your model predicts outcomes.



As the tutorial uses a small dataset, configuring Fairness and Drift won't have any impact.





 Evaluate the deployed model 

In this section, you will evaluate the model by uploading a iris_retrain.csv file which contains 10 instances of each species. Download[iris_retrain.csv](https://github.com/IBM-Cloud/ml-iris-classification/blob/master/data/iris_retrain.csv).



1. Click on Actions and then Evaluate now.
2. Choose from CSV file as your import option and click on browse, upload the iris_retrain.csv file and click on Upload and evaluate.
3. After the evaluation is completed, you should see the dashboard with different metrics.



1. Click on 1.00 under Quality to check the Accuracy of the model. Click on iris_deployment in the navigation menu.
2. Click on the Number of explanations (2), select one of the transactions and click Explain.
3. You can see important information like How this prediction was determined, Most important factors influencing prediction, confidence etc.,



To understand the quality metrics, refer to [Quality metric overview](https://cloud.ibm.com/docs/ai-openscale?topic=ai-openscale-anlz_metrics)









 Step 7: Remove resources 



1. Navigate to [IBM Cloud® Resource List](https://cloud.ibm.com/resources/).
2. Under Name, enter tutorial in the search box.
3. Delete the services which you created for this tutorial.



Depending on the resource it might not be deleted immediately, but retained (by default for 7 days).
Monitoring application load balancer metrics 

IBM Cloud® Application Load Balancer for VPC (ALB) monitoring metrics are provided with IBM Cloud® Monitoring, a 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?topic=monitoring-getting-started).

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 application 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
* Total number of HTTP/HTTPS requests received by the back-end
* Average response time for HTTP/HTTPS requests
* Count of responses with the HTTP 2xx back-end response code (successful request)
* Count of responses with the HTTP 3xx back-end response code (redirect)
* Count of responses with the HTTP 4xx back-end response code (client side failure)
* Count of responses with the HTTP 5xx back-end response code (server side error)
* Count of total back-end members in a pool
* Count of active back-end members in a pool



These metrics help track the traffic and usage patterns for your load balancers 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.