Queue-depth monitoring gives more visibility to the user of the queue depths being experienced on a set of queues when the traffic is bursty. The instantaneous depth of a queue can be seen using the show pools command, whereas queue-depth monitoring shows the variation in queue depth over a period of time. It is applicable to SAP ingress unicast and multipoint queues and SAP egress queues, and for ingress and egress access and network queue group queues used by any service or network interfaces. The monitoring uses a polling mechanism by the line card CPU. Consequently, the results provided are statistical.
An override (monitor-queue-depth command) is used to enable queue-depth monitoring, which is configured under the SAP or queue group queue-overrides. There are show and clear commands, using the queue-depth parameter, for both service SAPs and port queue groups with associated MIB variables.
The queue depth information is shown in the following example.
A:PE-1# show service id 1 sap 1/2/1 queue-depth
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Queue Depth Information (Ingress SAP)
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No Matching Entries
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Queue Depth Information (Egress SAP)
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Name : 1->1/2/1->1
MBS : Def
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Queue Depths (percentage)
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0%-10% 11%-20% 21%-30% 31%-40% 41%-50% 51%-60% 61%-70% 71%-80% 81%-90% 91%-100%
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68.21 3.64 3.43 3.47 3.86 3.22 3.86 2.87 3.78 3.66
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Average Elapsed Time : 0d 00:11:48
Wghtd Avg Polling Interval: 99 ms
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The preceding output shows the percentage of polls for each 10% range of queue depth. The output includes the name of the queue, its MBS configuration, the average elapsed time over which the depth was monitored (this is the elapsed time since the start of monitoring or the last clear), and the weighted average polling interval.
For example, the output shows a queue depth in the range of 51% to 60% for 3.22% of the polls, for polling that was performed over an elapsed time of 11 minutes and 48 seconds, and with an average polling interval of 99 milliseconds.
The monitoring is performed on the hardware queues corresponding to the configured queue. The set of related hardware queues for a specific configured queue could change over time; for example, when LAG ports are added or removed resulting in monitored hardware queues being added or removed. If the set of hardware queues for the configured queue changes, the system only reports occupancy information of all currently instantiated hardware queues; it does not keep historical occupancy information.
The average polling interval is weighted based on the elapsed monitoring time of the individual hardware queues corresponding to the configured queue, and the elapsed monitoring time is averaged over the same set of hardware queues.
There is no limit on the number of queues that can be monitored, but the amount of each line card’s CPU resources allocated to the monitoring is bounded. Consequently, the average polling interval increases as more queues are monitored on the line card.
If the MBS of a queue is modified, the occupancy information is cleared, and the elapsed timers are reset to zero. Issuing a clear card command also clears this information. Packet drops caused at the pool level, instead of at the queue level, results in lower queue depths being reported
At egress, sap-egress queues as well as queue-groups used at network egress or sap-egress, the monitor-queue-depth command provides two additional parameters.
The fast-polling parameter enables polling of the hardware queue-depth by the on-chip CPU. This supports reduced polling to the order of 10 ms, depending on number of queues with fast-polling enabled. The values are integrated and reported in the same fashion as previously described. The main advantage of the fast-polling option is that short-term burst can be detected by the monitor-queue-depth command.
The violation-threshold parameter is used to set the queue-depth threshold and record the number of violations that occurred. This supports frequency monitoring of specific bursts that can occur in the traffic; allowing for better capacity planning.
All information gathered by the queue-depth monitoring feature is available in YANG state model and therefore it can be monitored through telemetry.