Solving MiFID II Clock Synchronisation with minimum spend (part 4)

This post is a continuation from Part 3, where we started on collecting metrics from our PTP Bridge and clients. We started graphing NTP offset as a proof of concept and noticed data that could indicate a problem with the accuracy of PTP, but we need more information. We're now diving right in where we left off - collecting PTP metrics.

Collecting PTP Metrics

We've got NTP Offset data into InfluxDB, but what we really want is data from PTP. I'll start with the PTP Bridge. In Part 2 of this series we set up various linuxptp processes being run with supervisord with Puppet, and we specified that STDOUT was written to specific log files under /var/log/linuxptp. We're interested in the stats from the ptp-master instance of ptp4l; this is the Slave Clock that receives Layer 2 PTP from the S300 Grandmaster Clock. A single line of stats output looks like this:

ptp4l[2089949.676]: master offset         38 s2 freq  +34476 path delay     35290

The three interesting stats here are offset (38), frequency adjustment (+34476) and path delay (35290). If you look at the output from a phc2sys process, the format is almost the same:

phc2sys[2090397.362]: phc offset         2 s2 freq  -12406 delay   2416

As mentioned before we chose Collectd as a stats gatherer because it had the ability to tail log files. Since the files are almost identical, we should be able to create a Defined Type in Puppet that will write a collectd Tail plugin configuration file and that we can re-use for any ptp4l or phc2sys log file we want to collect stats from:

There are a lot of lessons learned in the above code that need explaining. First, the type in each one of the matches is one of our custom PTP collectd types. The dstype parameter has to do with how collectd accumulates values. A Tail plugin matcher is designed to read a chunk of a file and accumulate it's contents into one metric. For example, read /var/log/maillog every minute and count how many emails were sent. It's not designed to generate a metric for every line of a file, which is what I want to do.

It can be made to do this by changing the collectd interval to be as fast as the PTP interval - in my case every 1 second - and setting Dstype to "GaugeLast". This will have collectd wake up every second, tail the log file which should find just one extra line from the last time it woke up, and then use that value found as a Gauge value.

Finally, the collectd Tail Plugin regular expressions are compatible with the regex library on the machine as described in the man page regex(7), which is not the same as Perl. I'm mentioning this because I spent almost a day debugging Perl-like regular expressions in collectd, only to find out that I misread the collectd documentation about what Regex engine was used under the hood (I was trying to use PCRE character classes; \w, \s, etc).

The way Collectd is designed to always aggregate data highlights that perhaps it is not the best tool for this job. A better tool might be to write a log file tailer script in Python and send metrics to InfluxDB using it's own wire format (not the collectd wire format). Collectd will do for now though, and I'll slowly build up a list of reasons to move away from it.

Now if we go back to the Puppet Profile for the PTP Bridge, we can use the above collectd tailer Defined Type for every linuxptp instance log file:

You may remember that we have four linuxptp processes running under supervisord, but we have only three collectd tailers here. The fourth process is the ptp4l multicast process (the one that multicasts to our PTP Slaves through a firewall), it is configured as a Master Clock and so doesn't log statistics, so there's no point tailing it's log file.

Lets look at our data in InfluxDB:

If you want to copy it, the query is:

SELECT value from "tail_" WHERE "instance" = 'ptp-master' and "type_instance" = 'offset' AND time > now() - 10s

InfluxDB 0.9 introduces "tags" to namespace sets of data points. In the query above we are filtering on tag "instance" having the value of 'ptp-master' and tag "type_instance" being 'offset'. These tags and their values are translated by InfluxDB from the collectd wire format. If you compare what tags are being created to the Puppet code, you can see the relationship.

The name of the measurement, tail_, is not the most descriptive. InfluxDB's translation of the collectd tail plugin data leaves a lot to be desired. A better way to structure this data in InfluxDB would be to have a measurement called "offset" (because that's what we're actually measuring) and tags of "instance" and "host". This is another +1 to dropping collectd and writing our own tailer script so we can write to InfluxDB in a better way. Anyway, it's working so let's soldier on.

Graphing PTP Metrics

Lets draw the ptp-master instance's offset for a small window of time:

The query for this graph is:

SELECT mean("value") AS "value" FROM "tail_" WHERE "instance" = 'ptp-master' AND "type_instance" = 'offset' AND ( "value" > '0' OR "value" < '0' ) AND $timeFilter GROUP BY time($interval)

You may wonder why I've put the value>0 and value<0. It is a workaround for this bug, which is actually a Go bug. To summarise, the collectd Tail plugin is writing "NaN" (Not a Number) into InfluxDB. It appears to do this on startup, when there are no metrics to read from the files. When you create a simple InfluxDB query in Grafana that returns these NaNs, you trigger that JSON error and the graphs don't draw. Since NaN is not less than zero nor is it greater than zero (because it's not a number), you can effectively "filter out" the NaNs by adding to the WHERE clause. This is the reason why I will always write the full InfluxDB query in Grafana, rather than use the query builder form - you can't specify brackets or OR statements using Grafana's query builder.

The data in this period says we're getting ±100-250ns maximum offset. I'm pretty happy with that result - it means that to receive PTP from our S300 Grandmaster Clock we're losing at most 1/4 of a microsecond in accuracy. That's a good start, but there's plenty more hops to go.

If we draw the ptp-master path delay we can see the time it takes for our PTP packets to cross the switch:

The query is very similar, only the type_instance value needs to change:

SELECT mean("value") AS "value" FROM "tail_" WHERE "instance" = 'ptp-master' AND "type_instance" = 'path-delay' AND ( "value" > '0' OR "value" < '0' ) AND $timeFilter GROUP BY time($interval)

Between 35.25μs and 35.3μs. This may seem alarmingly high at first but remember that the value of path delay is not important to PTP, as long as it is consistent. The variance here, ±50ns, is not too bad.

(Not) Explaining the Negative NTP Offset

Hopefully you remember I stopped the previous post after graphing the NTP Offset of the PTP Bridge (which is the value of the "ntpdate -q" command) and that the data we got threw into question whether PTP or NTP was working correctly.

Here's the data for the same time range as the graphs above:

I said that if we look closely at the metrics and the network architecture that I'd be able to explain the difference. When I wrote that I had convinced myself that the NTP Offset discrepancy was caused by the time it takes to cross the switch, which we measure as the ptp-master Path Delay.

If we assume the clocks are perfectly synchronised by PTP and we know that it takes 35μs to cross the switch, the timestamp in the NTP packet would be 35μs behind. Add a little bit of operating system jitter... That explains our -25μs NTP offset, right?

... Not really. If the NTP packets were delayed by 35μs then our PTP Bridge would be 35μs ahead, not behind. Also, the assumptions about network architecture are incorrect, the NTP packets do not just cross a switch.

Here is a revised diagram of the network architecture from Part 2 showing the path that NTP takes:

The NTP packets flow from PTP Bridge through the switch to the firewall, then back onto the same switch to the S300. I also said that ntpdate was a "onetime" query and has no smarts built in to calculate network delays - this is also incorrect (lesson learned: don't blindly trust what you read on Stack Exchange). The ntpdate man page says it makes several requests to a single NTP server and attempts to calculate the offset, so it's not a "dumb" time query, it is trying to figure out it's own delay and compensate for it - explaining the NTP offset on the PTP Bridge just got a lot harder.

The ntpdate command reports a delay of 25.79 milliseconds. This seems way too high. The time to ping the NTP interface on the S300 is only 0.2ms. The delay to other NTP servers in the same network is around 25ms as well, so this could be a property of how ntpdate works, or the NTP server software. I seem to have more questions than answers at the moment, so lets look at gathering the stats from two PTP Clients and see if that enlightens anything.

Collecting sfptpd Metrics

The Puppet work we've done earlier for the PTP Bridge can be mostly re-used for our PTP Clients. The NTP Offset statistics is exactly the same, and the Collectd Tail plugin for a linuxptp stats file is similar in format to sfptpd, so we've pretty much got the regular expressions we need. There are two things that make it more complicated though; how sfptpd writes to disk, and how sfptpd synchronises all Solar Flare adapter clocks.

The first problem has to do with sfptpd's output. The linuxptp processes are line buffered, so that works nicely with our collectd tailer that reads a new line from the file every second, and we never miss a data point. sfptpd is block buffered, so you get a disk write every 5-10 seconds and it's almost never at the end of a line. Collectd's Tail plugin will read the latest data from the file but it this will be several lines of output. Due to the way the Tail plugin aggregates, it will only ever send one data point even if it read 10 lines.

The issue really lies with collectd's Tail plugin, which we talked about before. We can make sfptpd behave in a way that fits with collectd though; we can run the stdbuf(1) command beforehand to force sfptpd's STDOUT to be line buffered. I can do this by modifying the sfptpd init script, or by running sfptpd with supervisord.

I've gone with the second option because I already use supervisord for the linuxptp processes, and I think modifying the init script is "more dirty". Here is the modified Puppet code for sfptpd:


Nothing too hard there, the complicated bit is generating the collectd Tail Plugin config for the sfptpd stats file.

sfptpd will synchronise the adapter clocks of all Solar Flare adapters in the server, as well as the system clock. In the machines I'm using as PTP Clients a single second of stats logs looks like this:

2015-12-22 17:14:10.759688 [ptp-gm->system(em1)], offset: 348.000, freq-adj: 1444.090, in-sync: 1, one-way-delay: 57616.000, grandmaster-id: 0000:0000:0000:0000
2015-12-22 17:14:10.759688 [system->phc4(p5p1/p5p2)], offset: -227.562, freq-adj: -334.226, in-sync: 0
2015-12-22 17:14:10.759688 [system->phc5(p4p1/p4p2)], offset: -227.938, freq-adj: -261.973, in-sync: 0
2015-12-22 17:14:10.759688 [system->phc6(p3p1/p3p2)], offset: -223.562, freq-adj: 88.446, in-sync: 0

The first line is the "Local Reference Clock". The output "ptp-gm->system(em1)" shows that we are receiving PTP from a Grand Master using software timestamping on interface em1. The subsequent lines are Solar Flare adapters; there are three dual port cards, each with a single PTP Hardware Clock. The output "system->phc4(p5p1/p5p2)" shows that the adapter clock phc4 is being synchronised, which is the clock for interfaces p5p1 and p5p2.

The system line is the most interesting to me right now, but we should capture metrics for all cards as well. Specifying each Solar Flare adapter clock for every node would be very tedious. We also can't use Collectd's Tail plugin multiple times on the same file, so we have to build a big list of regular expressions of everything we want to match on one Puppet Array. Here's how I've done it automatically.

First, we need a Facter Fact to "know" which interfaces are our Solar Flare adapters. Here is a Custom Fact that will create Facts for every network interface in a machine and what driver they are using. An example of these Facts on a PTP Client:

nic_driver_bonding => bond0,bond1
nic_driver_igb => em1,em2,em3,em4
nic_driver_sfc => p3p1,p3p2,p4p1,p4p2,p5p1,p5p2
nic_driver_virtual => dummy0,lo

... and here is the Fact itself. It comes from an internal "standard lib" Puppet module which is not available to the public:


Next we need a way of knowing what PHC devices belong to our Solar Flare adapters. Many other brand of network card will create /dev/phcX devices on recent kernels, we just want the Solar Flare ones. Here is a Fact from the lmaxexchange-linuxptp Puppet module:


The last thing we want to do is take a template of Collectd Tail Plugin Matches and substitute in the PHC devices we are looking for. We need a chunk of imperative programming logic to do this efficiently, something which Puppet 3.x is very poor at. I've written a Ruby function that substitutes and replicates a template hash against an array. The Gist below has docs that demonstrate an example that explains all:


Now I put all these things together in a Puppet class to create the Collectd Tail Plugin config file for our sfptpd stats file:


The resulting file is large and repetitive, you can see an example from one of my PTP Clients here. With the above Puppet class applied we are now gathering PTP stats on the test clients.

All those code examples have made this a long post. The next post will be drawing graphs and analysing data.

Solving MiFID II Clock Synchronisation with minimum spend (part 3)

In my last post we created a "PTP Bridge" which translates from one Precision Time Protocol transport mechanism to another in order to distribute precise time to a PTP client. I'm doing this to try solve specific clock synchronisation requirements that are coming in new regulations. You can read about the regulations, my limitations, and subsequent design ideas in the first post in this series. This post will be half technical and half observation as I go in to how I'm capturing statistics, and what we can see from the data.

We know that the design we've started with is inherently flawed. PTP is the most accurate when it runs on dedicated network infrastructure where every device in the network is PTP aware. Every device in the network path that's not PTP aware adds some amount of variable delay. Switches will add some, software firewalls and routers will add more.

There's a few reasons we started with a flawed design. The first is the effort involved was low - multicasting over a network that already exists required only a small amount of configuration changes. We only had to build one new server and attach half a dozen network cables to get up and running. Expenditure is also a big factor - we haven't had to buy anything new so far, we've just used what we've got on hand. It's also a nicely Agile-ish approach - lets get our hands dirty with PTP early, learn what we need to learn and make small improvements as we need it. After all, we are trying to hit a specific level of clock sync accuracy by year 2017, it's going to be an iterative improvement process over a period of time.

When you adopt a design you know will be "bad", soon you ask yourself a very important question...

How bad is bad?

How slow is that firewall? How much time is lost through that switch? Very good questions that are answered with statistics. Before we get into that though there is another important point that I haven't talked about so far.

An amendment to Regulatory Technical Standard 25 (RTS 25) in the Markets in Financial Instruments Regulation (MiFIR) talks about venues documenting the design of their clock synchronisation system, and specifications showing that the required accuracy is maintained. What this means is that we need to be able to prove to any auditors that we maintain ±100μs off UTC at all times. To do this we have to monitor our system clocks and how accurate PTP is.

So, by answering the question "how bad is bad" we're taking early steps towards our requirement to prove we can maintain the accuracy we need to. Enough theory for now, lets get technical.

PTP Metrics

We are using two software implementations of PTP - ptp4l and sfptpd. Both output the standard PTP statistics. ptp4l is very basic and can only write to STDOUT, while sfptpd can write to STDOUT, a file, or syslog. The three main PTP statistics are offset, frequency adjustment and path delay.

Offset is how much the PTP protocol has calculated that the Slave Clock is off from the Master Clock. Frequency Adjustment is how much the oscillator of the clock is adjusted to try make it run at the same rate as the Master Clock. Path Delay is how long it takes for a PTP packet to get from Master Clock to Slave Clock (or vice versa, because PTP assumes they are the same).

The file format of both daemons is not exactly the same but it is very similar. If we can get something to constantly read from the files and parse metrics from them then we should be able to handle both ptp4l and sfptpd in an almost identical manner.

Capturing, Storing and Displaying Metrics

Historically it's common to store server metrics in Round Robin Database (RRD) files, and for most metrics this is perfectly fine. After all, most metrics get less important the longer ago in time they were recorded. My use case is a little different though. RRD's smoothing over time affect is not helpful if you are trying to compare a day six months ago with yesterday in per second granularity, the detail just gets normalised away. The smoothing effect is also not appropriate for proving compliance at any point in time over the past year.  So I want to avoid RRD backends for the moment, which also includes Graphite's Whisper/Carbon backend which is similar in concept to RRD.

I've used an early version of InfluxDB before and found it reasonably intuitive. We also use it in a few places elsewhere in the company. InfluxDB seems to change data storage engines every phase of the moon, and what should be harmless RPM upgrades have corrupted data stores here in the past. From version 0.9.5 InfluxDB are using a storage engine they developed themselves. Hopefully it remains stable, but even if it doesn't, we're only playing around at the moment and we can change storage backends if need be.

There are a number of ways to send data into InfluxDB, and it can run as a collector for various different wire formats as well. One of these is collectd, which is a popular metrics collection system with a huge library of Plugins. Collectd has a "Tail" plugin which reads from a file and applies regular expressions in order to gather metrics - that sounds like it can extract numbers from the ptp4l and sfptpd stats files for me.

For displaying data I've used Graphite in the past but I'm really taken with Grafana, which I find much prettier and modern. Grafana also works with InfluxDB 0.9.

Adding Statistics Collection

For lack of a better place right now I'm going to send my time metrics to the PTP Bridge server. So it is both the Master Clock and source of record of how accurate all it's Slave Clocks are. Until I'm happy that Grafana and InfluxDB are the right choices I'm not going to code their setup into Puppet just yet. I've just installed influxdb and grafana RPMs by hand. In hindsight this was a good move - I've upgraded the InfluxDB RPM twice now, and it has moved data and config files each time. Back porting these constant changes into Puppet would have annoyed me.

I am using a puppetlabs-apache module to proxy Grafana:

You'll notice there's only a non-ssl Apache vhost here. I ran into issues proxying InfluxDB's API and using SSL. In the interest of getting some results on screen I've allowed my browser to simply fetch data from InfluxDB directly on it's default port 8086. The problem was a certificate / mixed content issue so it's probably solvable, I'll come back to it later.

Turning our PTP clients into statistics generators is pretty simple, the puppet-community-collectd module has all of the features I've needed so far. Here is a profile to install collectd on a server and add some extra collectd types that are more relevant for PTP than the standard types:


Collectd has native types - time_offset, frequency_offset and delay - that are almost appropriate to use for PTP but not quite, so I've created my own. PTP Offset is always measured in nanoseconds rather than collectd's time_offset which is in seconds. PTP Frequency is measured in Parts Per Billion (ppb), not frequency_offset's Parts Per Million (ppm). PTP Path Delay is also measured in nanoseconds, and should never be negative so the minimum value is zero.

The Annoying Thing About Time

We're working towards collecting PTP statistics which is great, but how do we know that PTP is actually doing it's job right? It would be nice to be able to compare the clocks that PTP is disciplining to some other time source so we can see how accurate it is. A digital version of looking at your wrist watch's seconds hand tick around and comparing it to the speed of the clock on the wall, so to speak.

This is actually a very difficult thing to do, made even harder because we're working with such small units of time. What we would do in an NTP environment is run "ntpdate -q TIMESOURCE" and check the drift is acceptable. PTP is supposed to be more accurate than NTP though, so this is not going to give us the level of depth we want. The PTP protocol does output statistics about it's accuracy, but this can only be trusted if it calculates it's path delay. If there's a device in our network that's introducing a consistent one-sided delay, the PTP algorithm will not be able to correct this and we'll end up with a clock that's slightly out of sync - and PTP will never know.

The only way to test PTP accuracy is to compare it to a second reference clock that you trust more. MicroSemi have a good paper on how you would go about doing this. In short, you plug a 1 Pulse-Per-Second (1PPS) card into your PTP slave and compare the PTP card to the 1PPS card.

I don't have a 1PPS card (yet...), so collecting NTP Offset while we're messing around with PTP is better than nothing. NTP running as a daemon has some clever math to improve it's accuracy over time. ntpdate as a one-shot command doesn't have that, so it will be subject to all sorts of network delays. So I'm not expecting it to be very accurate or usable as a trusted reference clock, but at the very least it will make a good starting point for our metrics collection.

Collecting NTP Offset

Now to test we can gather metrics correctly with collectd. I've created a simple collectd Exec plugin that executes a Bash script in an endless loop, outputting the offset from a given NTP server in the correct collectd format. The NTP server I'll be querying is the same Symmetricom S300. This is the Puppet class:

The Bash script is pretty easy, I translate the output from seconds to microseconds because I think it's better to visualise (less decimal places):

We also need a bit of collectd configuration to do something with our statistics. When debugging it's helpful to quickly add the CSV plugin which writes to disk locally. We want to send our data the InfluxDB server, which you can configure to accept in collectd format. Here's a simple Puppet class to send data to a remote server using the collectd Network plugin:

We can now add the above components to the PTP Bridge Puppet Profile:

After a Puppet run on the PTP Bridge server I can see my first InfluxDB Measurement "remote_ntp_offset" being created and storing data points:

The name of the measurement in the screenshot above is confusing. It gets constructed from the plugin instance remote_ntp_offset that I wrote, plus the unit of measurement of the type I used. I chose time_offset, which is in the default collectd types database as being measured in seconds. My original script worked in seconds but I didn't like the long decimal places, so I translated it the script output to microseconds but didn't change the collectd type - I'll fix it later.

Graphing NTP Offset

We have data in InfluxDB, now let's visualise this in Grafana. I find the Query Builder editor mode doesn't work for me very well; the JavaScript has issues (clicking on certain controls doesn't work) and you just can't represent some features of the InfluxDB query language using the form (like an OR statement). Below I show the pure InfluxDB query to draw the remote NTP Offset for the PTP Bridge server:

If you want to copy it, the InfluxDB query is:

SELECT mean("value") AS "value" FROM "remote_ntp_offset_seconds" WHERE "host" = 'FQDN' AND $timeFilter GROUP BY time($interval)

First thing to note, I have obfuscated the "host" tag value, it's not just a list of spaces or 'FQDN'. We're selecting mean("value") and grouping by a dynamic time($interval). This is a common pattern that Grafana does so that you don't request every single data point over a large period of time and grind your browser to a halt. The $interval is worked out by whatever $timeFilter is applied to the graph. Grafana has a very easy to use time range selector and zoom button in the top right corner, so the $timeFilter and $interval change each time you "move" the graph to a different time range. If you do want to see every single data point, it's simple enough to modify the query, but be careful not to zoom out too far.

The astute of you will also note that the actual values in the graph above are pretty strange. It shows that the PTP Bridge is -25 ±5 μs behind the GPS timesource. A quick glance of the ptp4l log file that is the Slave Clock of the PTP Grandmaster (the same time source as NTP) shows that we barely drift over a microsecond offset fromt the Master Clock, and most PTP Sync messages are offset by only ±100 nanoseconds.

So which is correct? PTP or NTP? Which do I trust more? If only we had a perfect reference clock plugged in to the server for comparison... Even without a fully trusted reference clock, we can still deduce with reasonable confidence which of NTP or PTP is more correct. To do this we will first gather more data and look at the PTP metrics from both our PTP Bridge and PTP clients, and then take a close look at the network architecture to see if we can make some reasonable conclusions.


This post has already gotten very long, so I'll continue with PTP metrics collection and what we can infer from the data in the next post.