| 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)
1558966245 1558966382 137 0.005 # 28 May 2019 00:10:45 28 May 2019 00:13:02
1561561236 1561561820 584 0.001 # 27 June 2019 01:00:36 27 June 2019 01:10:20
1561562006 1561562252 246 19.355 # 27 June 2019 01:13:26 27 June 2019 01:17:32
1561562440 1561562666 226 19.149 # 27 June 2019 01:20:40 27 June 2019 01:24:26
1561563998 1561564458 460 2.703 # 27 June 2019 01:46:38 27 June 2019 01:54:18
1562107462 1562126930 19468 0.007 # 3 July 2019 08:44:22 3 July 2019 14:08:50
1562805630 1562806828 1198 0.005 # 11 July 2019 10:40:30 11 July 2019 11:00:28
1562807924 1562808605 681 3.285 # 11 July 2019 11:18:44 11 July 2019 11:30:05
1562810598 1562810856 258 1.806 # 11 July 2019 12:03:18 11 July 2019 12:07:36
1564360861 1564383404 22543 0.002 # 29 July 2019 10:41:01 29 July 2019 16:56:44
1564388494 1564388674 180 0.707 # 29 July 2019 18:21:34 29 July 2019 18:24:34
1564389806 1564390279 473 3.180 # 29 July 2019 18:43:26 29 July 2019 18:51:19
1564445000 1564445845 845 0.066 # 30 July 2019 10:03:20 30 July 2019 10:17:25
1564446029 1564446436 407 19.565 # 30 July 2019 10:20:29 30 July 2019 10:27:16
1564447588 1564448037 449 3.125 # 30 July 2019 10:46:28 30 July 2019 10:53:57
1564465203 1564465216 13 0.210 # 30 July 2019 15:40:03 30 July 2019 15:40:16
1564535641 1564541563 5922 0.051 # 31 July 2019 11:14:01 31 July 2019 12:52:43
1564551643 1564554720 3077 0.357 # 31 July 2019 15:40:43 31 July 2019 16:32:00
1564677320 1564677333 13 0.029 # 2 August 2019 02:35:20 2 August 2019 02:35:33
1564840938 1564843162 2224 0.022 # 4 August 2019 00:02:18 4 August 2019 00:39:22
Total 394 outages, total time 860179 seconds (9 days, 22:56:19)
Average time between outages: 442087 seconds (5 days, 02:48:07)
Average duration: 2183 seconds (00:36:23)
Availability: 99.51%
“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)
1558188000 1 113 99.87% # 19 May 2019
1558965600 1 137 99.84% # 28 May 2019
1561557600 4 1516 98.25% # 27 June 2019
1562076000 1 19468 77.47% # 3 July 2019
1562767200 3 2137 97.53% # 11 July 2019
1564322400 3 23196 73.15% # 29 July 2019
1564408800 4 1714 98.02% # 30 July 2019
1564495200 2 8999 89.58% # 31 July 2019
1564668000 1 13 99.98% # 2 August 2019
1564840800 1 2224 97.43% # 4 August 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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