Network Telemetry Orchestration via Cisco gRPC and gNMI Implementations

The landscape of modern network management has undergone a seismic shift from reactive, pull-based polling mechanisms to proactive, push-based streaming architectures. At the center of this architectural revolution is gRPC (Google Remote Procedure Call), a high-performance, open-source universal RPC framework. Within the Cisco ecosystem, specifically concerning Nexus 9000 series NX-OS devices and IOS XR platforms, gRPC serves as the foundational transport layer for streaming granular, real-time telemetry data. Unlike legacy protocols such as SNMP (Simple Network Management Protocol), which rely on inefficient periodic polling that can miss critical micro-bursts or transient state changes, gRPC enables a continuous stream of operational data. This capability is vital for maintaining the visibility required in modern, high-scale service provider and enterprise environments. By leveraging gRPC in conjunction with gNMI (gRPC Network Management Interface), network administrators can not only monitor network health but also perform complex device manipulations, effectively treating the network as code. This evolution allows for a more granular understanding of device states, including interface statistics, BGP neighbor transitions, and hardware-level performance metrics, all delivered through a highly efficient, low-latency communication channel.

Architectural Fundamentals of gRPC in Cisco Environments

gRPC operates on top of HTTP/2, utilizing Protocol Buffers (protobuf) as its interface definition language and efficient serialization mechanism. In Cisco networking hardware, this protocol is utilized to stream various types of metric data, the scope of which is determined by the specific device configuration and the availability of YANG (Yet Another Next Generation) data models. The integration of gRPC with gNMI provides a modern, unified interface for both telemetry collection and configuration management, offering a superior alternative to the more cumbersome NETCONF or RESTCONF protocols.

The efficiency of gRPC stems from its ability to handle large-scale data transmission, encompassing both operation requests and continuous telemetry streams. This is particularly critical when dealing with the massive volumes of data generated by high-density switches and core routers. However, the granularity provided by this streaming capability necessitates careful design considerations. Because collecting high-frequency metric data can result in massive datasets, a robust ingestion pipeline is required. A common architectural pattern involves using an intermediate processing layer, such as Telegraf, to collect, pre-process, and buffer this data before it is ingested into a centralized analytics platform like Splunk. This prevents the overwhelming of the primary analytics engine and allows for the correlation of network telemetry with other machine data sources, such as logs and traces, to support advanced use cases in security, infrastructure monitoring, and service-level agreement (SLA) verification.

The underlying data structure for these operations is governed by the YANG data model. Cisco devices use YANG to define and expose metrics, events, and configuration parameters in a structured, machine-readable format. Because this same model is used with the NETCONF protocol, network engineers can leverage existing knowledge of YANG to perform complex data modeling within their analytics platforms, specifically by targeting the remote procedure calls (RPCs) that invoke network elements.

Cisco IOS XR gRPC Service Operations and Functionality

On Cisco IOS XR devices, gRPC provides a specific set of managed service operations designed to facilitate both configuration management and operational visibility. These operations are structured as RPCs that can be invoked by a client to interact with the device's control plane. The following table details the specific manageability service operations available within the Cisco IOS XR gRPC implementation:

| GetOper | Retrieves real-time operational data, such as interface statistics or routing tables. |
| CliConfig | Invokes and applies the input configuration using the standard Cisco CLI syntax. |
| ShowCmdTextOutput | Returns the output of a standard 'show' command in raw, unstructured text format. |
| ShowCmdJSONOutput | Returns the output of a standard 'show' command formatted as structured JSON. |

To illustrate the practical application of these operations, consider a GetConfig request targeting the CDP (Cisco Discovery Protocol) feature. In this workflow, the client initiates a message specifically requesting the current configuration parameters for CDP. The router processes the request against its internal configuration database and responds with the precise, current configuration state for that feature. This programmatic approach eliminates the need for screen-scraping CLI outputs and ensures that the client receives structured, reliable data.

Configuration of gRPC Services on Cisco Hardware

Implementing gRPC requires specific configuration steps on both the network device and the management software. The configuration requirements differ slightly between the NX-OS (Nexus) and IOS XR platforms, but both emphasize the need for defined ports, security parameters, and concurrency limits.

NX-OS and Nexus 9000 Implementation

For devices such as the Nexus 9000 series, the implementation may require the installation of specific software packages to enable advanced telemetry features. In some environments, such as virtualized Nexus instances (e.g., n9300v), an RPM package containing OpenConfig libraries must be installed and activated. The process typically follows these steps:

1. Log into the iOS/NX-OS environment.
2. Install the required RPM file using the following command:
   install add mtx-openconfig-all-1.0.0.182-9.3.5.lib32_n9000.rpm activate
3. Verify the installation status by checking the active packages:
   show version

Once the necessary libraries are active, the gRPC service must be configured within the running configuration. A standard configuration for the gRPC service on a Nexus device might include the following elements:

feature grpc grpc gnmi max-concurrent-multistream 16 use-vrf default certificate gnmicert

In this configuration, the grpc command activates the service, while gnmi enables the gNMI-specific capabilities. The max-concurrent-calls (or max-concurrent-multistream) parameter is crucial for resource management, as it limits the number of simultaneous streams the device will handle to prevent CPU exhaustion.

IOS XR gRPC and Telemetry Subscription Models

On the IOS XR platform, the configuration is more granular, involving the setup of the gRPC service itself, the definition of sensor groups, and the creation of subscriptions. The gRPC service can be configured to run on a specific port (defaulting to 57400) and can be set to operate without TLS for simplified laboratory testing, though production environments should utilize TLS.

A fundamental aspect of IOS XR telemetry is the distinction between the sensor group and the subscription. The sensor group defines the "what"—the specific YANG paths to be monitored. The subscription defines the "how"—the frequency of the data collection and the destination for the data stream.

An example of a gRPC service configuration on IOS XR:

grpc port 57400 no-tls

The configuration for model-driven telemetry follows:

telemetry model-driven sensor-group 1minute sensor-path Cisco-IOS-XR-asr9k-np-oper:hardware-module-np/nodes/node/nps/np/load-utilization sensor-path Cisco-IOS-XR-telemetry-model-driven-oper:telemetry-model-driven/destinations/destination ! subscription 1minute sensor-group-id 1minute sample-interval 60000

In the example above, the sample-interval is defined in milliseconds (60000 ms equals 1 minute). After applying these configurations, it is imperative to verify that the sensor paths are correctly resolved. This can be achieved by executing:

show telemetry model-driven subscription

If the subscription is functioning correctly, the state will transition from NA (No active session) to ACTIVE. A successful, active session will also display the destination group information, including the transport encoding (e.g., self-describing-gpb), the transport protocol (dialin), the destination IP address, and the port.

Deploying Telegraf for Data Ingestion and Splunk Integration

Telegraf acts as the critical bridge between the Cisco network devices and the Splunk analytics platform. As an open-source agent, Telegraf is capable of subscribing to gRPC/gNMI streams and transforming the incoming protobuf data into a format suitable for indexing.

Telegraf Installation and Compilation

The deployment of Telegraf requires a structured approach to ensure the agent can communicate with the Cisco device's specific telemetry output. The process involves:

1. Downloading the Telegraf binary to the target server instance.
2. Generating a custom configuration file (telegraf.conf) that contains:
   • Input configurations: These must include the specific YANG paths (sensor paths) from the Cisco device and the subscription modes (e/g, sample).
   • Output configurations: These define how the data is sent to Splunk (via HEC or Universal Forwarder).
3. Compiling the Telegraf binary with the appropriate plugins enabled to support gNMI and gRPC parsing.
4. Installing the compiled binary on the server.

An example of a Telegraf input configuration for BGP telemetry is as follows:

```toml
[[inputs.gnmi.subscription]]
name = "instanceSpecificBGPData-prefixes-accepted"
origin = "Cisco-IOS-XR-ipv4-bgp-oper"
path = "bgp/instances/instance/instance-active/default-vrf/afs/af/neighbor-af-table/neighbor/af-data/prefixes-accepted"
subscriptionmode = "sample"
sampleinterval = "30s"

[[inputs.gnmi.subscription]]
name = "instanceSpecificBGPData-prefixes-advertised"
origin = "Cisco-IOS-XR-ipv4-bgp-oper"
path = "bgp/instances/instance/instance-active/default-vrf/afs/af/neighbor-af-table/neighbor/af-data/prefixes-advertised"
subscriptionmode = "sample"
sampleinterval = "30s"
```

Configuring Splunk Output via HTTP Event Collector (HEC)

Once the data is collected by Telegraf, it must be forwarded to Splunk. The most efficient method for high-volume telemetry is using the Splunk HTTP Event Collector (HEC). The Telegraf [agent] and [outputs.http] sections must be configured to manage the batching and transmission of these metrics.

A robust telegraf.conf global and agent configuration:

```toml
[global_tags]
# Tagging allows for easy filtering in Splunk by data center or rack
# dc = "us-int-1"
# rack = "01"

[agent]
interval = "30s"
roundinterval = true
metricbatchsize = 1000
metricbufferlimit = 10000
collectionjitter = "0s"
flushinterval = "10s"
flushjitter = "0s"
precision = ""
debug = false
quiet = false
logtarget = "file"
logfile = "/var/log/telegraf/telegraf.log"
logfilerotationinterval = "0d"
logfilerotationmax_size = "1MB"

[outputs.http]
url = "https://splunk-hec-server:8088/services/collector"
token = "YOUR-HEC-TOKEN-HERE"
data_format = "json"
```

In this configuration, the metric_batch_size and metric_buffer_limit are vital for managing memory usage on the Telegraf server. Setting a batch size of 1000 ensures that the agent does not overwhelm the network with too many small HTTP requests, while the buffer limit provides a safety net during periods of network congestion or Splunk ingestion latency.

Advanced Security and Service Parameters

When configuring gRPC on Cisco devices, particularly for production-grade deployments, administrators must consider security and resource constraints. The gRPC service can be fine-tuned to accommodate specific security requirements and network topologies.

The service allows for the following configuration options:

• Port Customization: While the default is 57400, the service can be configured within the range of 57344 to 57999.
• TLS Activation: For secure communication, the tls command can be enabled, requiring the management of certificates (e.g., grpc certificate gnmicert).
• Address Family: The service can be restricted to IPv4 or IPv6 depending on the management network architecture.
• Concurrency Management: Administrators can define max-request-total to prevent DoS (Denial of Service) attacks and max-concurrent-calls to manage per-user resource allocation.

Example of a restricted-port, TLS-enabled configuration:

grpc port 57344 tls ! max-request-total 32 !

This configuration ensures that all incoming gRPC requests are encrypted and that the total number of simultaneous requests is capped at 32, protecting the router's control plane from exhaustion.

Analysis of Telemetry Implementation Strategies

The transition to gRPC-based telemetry represents more than just a protocol change; it is a fundamental shift in the operational philosophy of network engineering. The move from a "pull" model to a "push" model reduces the computational overhead on the network device, as the device is no longer required to respond to thousands of individual SNMP polling requests. Instead, the device maintains a single, persistent stream of data.

However, this shift introduces new complexities in the management of the data pipeline. The primary challenge is no longer the retrieval of data, but the management of its volume. As demonstrated in the configuration of Telegraf and Splunk, the introduction of an intermediate collection layer is essential. Without a well-configured Telegraf instance—properly tuned with metric_batch_size and flush_interval—the sheer volume of granular, sub-second telemetry can lead to "telemetry storms" that can degrade the performance of both the network device and the analytics platform.

Furthermore, the reliance on YANG models means that the complexity of network management is now inextricably linked to the accuracy of the data modeling. The ability of a network engineer to navigate the YANG tree and correctly identify the sensor-path for a specific metric (such as load-utilization on an NP node) is now a critical skill. As the industry moves toward even more automated, closed-loop systems, the precision of gRPC-driven telemetry will remain the cornerstone of network observability and autonomous operations.

Sources

1. Splunk Lantern: Monitoring Cisco network devices using gRPC
2. Cisco: gRPC Session Configuration for IOS XR
3. Cisco Community: Implementing gRPC Telemetry on XR Devices
4. GitHub: Cisco DevNet gRPC Getting Started