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Monitoring and Tracing

lantz edited this page May 21, 2019 · 9 revisions

Network Invariant Monitoring with Mininet

How do we verify that an emulator is correct without comparing it directly against hardware?

Even if it matches hardware, how do we know that it is actually doing the right thing?

Heller13 suggests the very useful idea of network invariants. Just as loop invariants must hold during every iteration of a loop, or function invariants during every invocation of a function, network invariants shoud hold for the duration of a network experiment.

Network invariants give us a signal to detect when things have gone obviously wrong in some way.

If the invariants do not hold, then we know something is definitely wrong and we should not trust our experimental results.

If the invariants do hold, then we don't know that our experimental results are correct, but we will at least have higher confidence in them since the invariants tell us that there are no obvious problems.

Some Possible Invariants to Monitor

Link data rate

On a link that we expect to be saturated with full-sized packets, the link data rate should be within an acceptable tolerance of the configured link rate.

The obvious reason is that a faster or slower link rate means that the link is not being accurately modeled: a faster rate usually means rate limiting isn't working, and a slower rate usually means that we are hitting resource limits.

We can measure this relatively easily using bwm-ng, or by looking at counters for an interface (e.g. using ifconfig or other methods) or by looking at counters on a switch (e.g. using OpenFlow.)

Monitoring the link data rate is a good idea, but Heller13 shows that it may not be sufficient to detect when an experiment has gone awry.

One deficiency of link data rate is that it can be amortized over time, so the average may be correct even if the traffic is extremely bursty and unrealistic. To overcome this deficiency, we can consider the next option.

Packet Spacing

Heller13 picks Packet Spacing as an examplary network invariant, showing how it can be used in evaluating a dctcp experiment.

On a link that we expect to be saturated with full-sized packets, the packet spacing should be within an acceptable tolerance of the expected spacing.

Following his example, we suggest a maximum deviation of one packet - basically allowing us to be one packet ahead or behind of where we should be at any given time. This may be a generous constraint, but it is clear that hardware could not overlap packet timing intervals, and also that a delay of one packet time would cut the link rate by 50%.

We can measure packet spacing easily using the Linux ftrace (function tracing) subsystem and tracing net:net_dev_xmit events for the interface we are interested in.

CPU Utilization

We want to make sure that virtual hosts are not using more than their configured CPU utilization.

For hosts that we expect to be compute bound, we want to make sure that they are in fact getting their desired CPU utilization.

We can monitor cpu utilization of cgroups using the cpuacct (CPU accounting controller) for cgroups.

Heller13 points out that CPU utilization is good to monitor but is not sufficient as a predictor for accuracy in network experiments.

RX-to-Schedule latency

Since we are multiplexing the underlying system, it is possible that a packet will arrive when a process should be running on a virtual host, but is currently descheduled.

When this happens, we want to make sure that the latency is acceptable. If this is more than a packet time on a saturated link, then the process may find it impossible to keep up with incoming data.

This is trickier to monitor than other statistics and may be more costly. We may be able to monitor it by tracing container scheduling (which we can do by instrumenting sched_switch with bcc/EBPF) and correlating it with packet received events (which could be something like net:netif_rx, but I should look into this more to be absolutely sure.)

Other Monitoring

There are a wealth of additional monitoring options; depending on the experiment these may be important output signals that could potentially be used to evaluate when a particular condition (which could be a network invariant) holds during an experiment.

Queue depth

If the hosts and application can saturate a bottleneck link, this is likely to cause some queueing somewhere in the system.

We need to create a queue that we can monitor, and we do this by using tc, which can also mark packets using ECN. this gives us a pseudo-output-queued switch, though technically it is the links that have queues, and there can be queuing at the host end of the link as well.

If a switch egress port is oversubscribed, then we will expect a queue to build there. (Presumably this is the queue which we use for ECN.) We can sample the queue size using tc -s.

We should also be able to monitor dropped packets on the interface.

TCP Tracing

For TCP reno over long periods of time we should see the expected CWND sawtooth. There may be other values we should look at for something like cubic or bbr.

We can also monitor other internal TCP state.

We can monitor TCP state with ftrace using the net:tcp_probe event.

Full Packet Tracing

We can use tcpdump/tshark/wireshark to monitor the actual packets transmitted over an interface and either decode them immediately or save them for later analysis.

We may wish to analyze them to make sure that other constraints are being maintained, such as packet spacing or data rate. We can also verify that they are being marked properly with ECN.

We can also analyze the packets in a TCP session using tcptrace.

WARNING: Tracing is resource-intensive!!

It's easy to eat up a ton of resources (usually CPU but sometimes memory and I/O as well) using tracing.

Because of this, tracing can perturb the experimental results! If the results are already questionable, it can make them much worse.

Taking a conservative approach and minimizing the amount of tracing during an experiment should mitigate this effect.

One approach to minimize the impact of monitoring is to run an experiment repeatedly. We can run the experiment a number of times without monitoring and verify that a set of base measurements do not change.

Then for each invariant we can rerun the experiment several times and verify that

  1. The base measurements do not change, and

  2. The invariant holds during the experiment

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