| Greg's network link statistics |
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This page shows my network availability and performance statistics connected to the Australian National Broadband Network. It bases on earlier pages that I wrote for the DSL and satellite connections.
I had hoped that with the advent of the NBN, this page would no longer be needed. Unfortunately, this has proven not to be the case. While I don't have issues with throughput, the availability has been the worst I have ever experienced, with outages of up to 48 hours. It's not clear where the problem lies, but I have observed serious issues with the DHCP authentication that my ISP uses. More here when I understand the situation better.
The graphs show:
Network status. This is the number of remote systems responding to a ICMP echo (ping) packet at the same time as the link status ping. I currently ping 5 systems which I frequently access: freefall.freebsd.org (now somewhere in New York state), www.lemis.com, my external web server in Raleigh NC, ffm.lemis.com, located in Frankfurt am Main, ozlabs.org, located in Canberra, and ftp.netbsd.org, located somewhere in the USA. Normally this value should be 5. If it's less, the script retries every second until full connectivity is restored.
Link packet loss, which has proven to be a significant issue since early 2018. I send 20 ICMP ping messages to the other end of the network link and count how many are lost.
I previously had parameters "link status" and "TCP speed”, but they no longer seem to make much sense.
The graphs were made with gnuplot, and I'm not very happy with the smoothing. In particular, the right-hand side of smoothed graphs has too much influence, and it's easy to get the false impression that the link status or TCP speed have changed significantly in the last few minutes. If anybody can point me at a way to fix this problem, I'd be grateful.
Click on the graphs for a 1600x1200 version.
This section monitors the NBN link for outages, including planned outages. I have manually edited the data to remove false positives, such as when moving house. I have a program that evaluates the outage information and produces detailed outage information. Here's the information on the past 20 outages, along with the overall statistics:
Start time End time Duration Badness from to
(seconds)
1553722523 1553722547 24 1.678 # 28 March 2019 08:35:23 28 March 2019 08:35:47
1553723749 1553723975 226 2.995 # 28 March 2019 08:55:49 28 March 2019 08:59:35
1553725034 1553725307 273 3.399 # 28 March 2019 09:17:14 28 March 2019 09:21:47
1553775703 1553776332 629 0.071 # 28 March 2019 23:21:43 28 March 2019 23:32:12
1553778942 1553779133 191 1.379 # 29 March 2019 00:15:42 29 March 2019 00:18:53
1553784154 1553791281 7127 0.717 # 29 March 2019 01:42:34 29 March 2019 03:41:21
1553803499 1553803503 4 0.295 # 29 March 2019 07:04:59 29 March 2019 07:05:03
1555032141 1555032624 483 0.003 # 12 April 2019 11:22:21 12 April 2019 11:30:24
1555034739 1555035952 1213 1.702 # 12 April 2019 12:05:39 12 April 2019 12:25:52
1555037101 1555037416 315 3.133 # 12 April 2019 12:45:01 12 April 2019 12:50:16
1555038451 1555048766 10315 3.478 # 12 April 2019 13:07:31 12 April 2019 15:59:26
1555049372 1555049396 24 5.941 # 12 April 2019 16:09:32 12 April 2019 16:09:56
1555053989 1555054531 542 0.784 # 12 April 2019 17:26:29 12 April 2019 17:35:31
1555055199 1555056528 1329 5.389 # 12 April 2019 17:46:39 12 April 2019 18:08:48
1557239507 1557240731 1224 0.002 # 8 May 2019 00:31:47 8 May 2019 00:52:11
1557277759 1557283322 5563 0.097 # 8 May 2019 11:09:19 8 May 2019 12:42:02
1557285370 1557285394 24 1.758 # 8 May 2019 13:16:10 8 May 2019 13:16:34
1557285642 1557285946 304 14.516 # 8 May 2019 13:20:42 8 May 2019 13:25:46
1558202548 1558202661 113 0.004 # 19 May 2019 04:02:28 19 May 2019 04:04:21
1558966245 1558966382 137 0.005 # 28 May 2019 00:10:45 28 May 2019 00:13:02
Total 375 outages, total time 800912 seconds (9 days, 06:28:32)
Average time between outages: 451342 seconds (5 days, 05:22:22)
Average duration: 2135 seconds (00:35:35)
Availability: 99.53%
“Badness” is an attempt to quantify the effect. It's the reciprocal of the number of seconds per hour that the link was up between failures (i.e. 3600 / uptime).
And here is the summary information for the past 10 days with a less than 100% record:
Timestamp Outages Duration Availability Date
(seconds)
1546174800 3 569 99.34% # 31 December 2018
1548248400 2 1515 98.25% # 24 January 2019
1551704400 1 1428 98.35% # 5 March 2019
1551790800 1 14199 83.57% # 6 March 2019
1553691600 9 8160 90.56% # 28 March 2019
1553778000 3 7322 91.53% # 29 March 2019
1554991200 7 14221 83.54% # 12 April 2019
1557237600 4 7115 91.77% # 8 May 2019
1558188000 1 113 99.87% # 19 May 2019
1558965600 1 137 99.84% # 28 May 2019
The dates in the left columns are in UNIX time_t format to ease further processing. I also have lists of all individual outages, or summaries per day.
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