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IBA Probes

The devices managed by Apstra AOS generate large amounts of data over time. On its own, this data is voluminous and unhelpful. Through Intent-Based Analytics (IBA), AOS allows the operator to combine intent from the AOS graph database with current and historic data from devices to reason about the network at-large.

For a detailed explaination of AOS IBA, please watch our recent webinar "Intent-Based Analytics: Prevent Network Outages and Gray Failures"!

Probes are the basic unit of abstraction in IBA. Operators can configure, create, and delete probes. Generally, a given probe consumes some set of data from the network, does various successive aggregations and calculations on it, and optionally specifies some conditions of said aggregations and calculations on which anomalies are raised.

IBA workflow

Below is a collection of readily available probes organized by rough categories. They also serve as examples to help build custom probes yourself.


Validation Description
fabric_interface_flapping Detect interface that are flapping
sfp Detect high and/or low warning thresholds in SFP RX Power, TX Power, Temperature, Voltage, or Current


Validation Description
mlag_domain_config_sanity_anomalies Detect MLAG (a.k.a MCLAG or VPC) domains with inconsistent configuration between member devices
stp_state Detect STP blocked interfaces in all VLANs
stp_state_change Detect any southbound interface STP state change seen in the last specified number of days
mlag_domain_state_anomalies Detects MLAG domain state anomalies


Validation Description
border_leaf_default_gateway_anomalies Verify routing intent on border leafs by ensuring default gateway nexthop count is as expected in all VRFs
non_border_leaf_default_gateway_anomalies Verify routing intent on non-border leafs by ensuring default gateway nexthop count is as expected in all VRFs
border_device_default_gateway_anomalies Verify default routing intent on border devices (leaf or spine) ensuring the default gateway nexthop count is as expected for user specified VRFs

Data plane

Validation Description
fabric_ecmp_imbalance Detect imbalance between interfaces within the Fabric
ecmp_imbalance_external_interfaces Detect imbalance between interfaces exiting the Fabric
mlag_imbalance Detect imbalance between member links of an aggregate MLAG link
drain_node_traffic_anomaly Verify no application traffic on devices under maintenance
counters_error_anomalies Detect Fabric interface showing alignment errors, FCS errors, runts, giants, or error packets
pkt_discard_anomalies Detect Fabric interface having packet drops
interface_queue_drops_anomalies Detect interfaces with overflowing buffers resulting in dropped packets
monitor_packet_loss Detect high packet loss observed from pinging specified destination(s)
server_rtt Detect high roundtrip time observed from pinging specified destination(s)
fabric_bgp_anomalies Detects BGP anomalies in the fabric
interface_status_anomalies Detects physical interface status
leaf_bgp_vrf_anomalies Detects leaf vrf-aware BGP session anomalies

Traffic patterns

Validation Description
fabric_hotcold_ifcounter Detect hot and cold interfaces in the Fabric and flag systems with excessive cold or hot interfaces
eastwest_traffic Show the distribution of north-south vs. east-west traffic in the Fabric
bandwidth_utilization_history History of traffic patterns with varying degrees of aggregation


Validation Description
Headroom Calculate headroom between two servers along all available paths

Virtual Infrastructure

Validation Description
virtual_infra_vlan_match Detect inconsistencies between physical underlay and virtual networking
missing_vlan_vms Detect Virtual Machines that have connectivity issues due to configuration inconsistencies between physical underlay and virtual networking
virtual_infra_lag_match Detect inconsistencies between physical underlay and hypervisor LAG configuration
virtual_infra_missing_lldp_config Detect virtual infra hosts that are not configured for LLDP
virtual_infra_hypervisor_redundancy_checks Detect single point of failures between hypervisor and physical underlay connectivity
hypervisor_mtu_checks Detect hypervisor interfaces with MTU below threshold
hypervisor_mtu_mismatch Detect MTU value deviation across hypervisor pnics

Capacity Planning

Validation Description
arp_usage_anomalies Detect devices with ARP table usage exceeding specified threshold
unicast_route_usage_anomalies Detect devices with Unicast route table usage exceeding specified threshold
multicast_route_usage_anomalies Detect devices with Multicast route table usage exceeding specified threshold
interface_bandwidth_anomalies Detect interfaces with bandwidth exceeding specified threshold


Validation Description
anycast_rp_anomalies Verify rendezvous point intent by ensuring all systems designated as rendezvous points have the expected peers present
anycast_rp_peer_count_anomalies Ensure high-availability of rendezvous points by ensuring all rendezvous points have min number of anycast peers configured
mroute_count_anomalies Detect abnormal changes in number of multicast sources, groups or routes
pim_neighbor_anomalies Verify Multicast intent by ensuring all SVI interfaces, leaf-spine and leaf-leaf links in every VRF have an expected PIM neighbor
pim_rp_anomalies Verify every VRF in every switch in Fabric has expected rendezvous point IP configured
vrfs_missing_rp Ensure any device acting as RP on any VRF is an RP for all other multicast enabled VRFs on that device

Device health

Validation Description
memory_usage_threshold_anomalies Detect memory leaks in specified process on all switches in the Fabric
power_supply_anomalies Detect faults in power supply status, power supply fan status and power supply temperature status
system_memory_usage_threshold_anomalies Detect switches having potential memory leaks in the Fabric


Validation Description
hostname_compliance Ensures the FQDN of any system matches the user specified regex
os_version_anomalies Detect devices not running expected Operating System version


Validation Description
bum_to_total_traffic_anomalies Detect % of BUM traffic to overall traffic exceeding specified threshold
bum_traffic_on_unlearnt_vteps_anomaly Detect when decap traffic is seen from an unlearnt remote VTEP
hardware_vtep_counters_enabled Detect devices where hardware telemetry to capture VXLAN counters is not enabled
vxlan_status Detect devices with VXLAN interface that is not up
static_vxlan_vtep_anomalies Verify VXLAN intent by ensuring all L2 segments have expected VTEP flood list
vxlan_address_table_anomalies Verify that the move count of any MAC address is not greater than 1


Validation Description
acl_stat_anomalies Report on acl rule matches that exceed user defined thresholds


Validation Description
evpn (VTEP floodlist) Validate Type 3 routes for L2 VNIs
evpn (VXLAN Routing) Validate VXLAN subnet (type 5) presence in BGP RIB
evpn (Floodlist limit) Detect excessive per-VNI count of VTEPs in floodlist
evpn (VRF limit) Detect excessive count of VRFs

Fault Tolerance

Validation Description
lag_link_fault_tolerance Determines if a failure of one link in a server LAG can be tolerated based on the current traffic load
spine_fault_tolerance Determines if the failure of a user specified number of spines can be tolerated based on the current traffic load across all spines

Getting Started

All the probes listed above are available as part of AOS server predefined probe list or aos-cli predefined probe list. For the former, use AOS web UI to instantiate a predefined probe - you can find more details in the AOS documentation. For the latter, see the probe templates section below.

Probe Templates

You can find working probes in the templates subfolder.

The files in this folder are IBA probe json payloads that are represented as JINJA templates. You need to use aos-cli to load these probes onto the AOS server. The command to use in aos-cli is probe create --blueprint <id> --file </path/to/template/file> [<additional_args>]

Probe graph queries

Authoring new probes requires familiarity with graph queries to select the elements of interest whose telemetry is ingested into the probes. You can find more details regarding graph queries in the AOS documentation.


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