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MTBF can’t be used alone to predict failure probability, because product mortality follows the infamous “bathtub curve”. Products are as likely to fail early in their lives as later in their lives. MTBF as a scalar value is just an average. <br>
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<div id="AppleMailSignature" dir="ltr">-mel via cell</div>
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On Jan 16, 2019, at 12:43 PM, "<a href="mailto:adamv0025@netconsultings.com">adamv0025@netconsultings.com</a>" <<a href="mailto:adamv0025@netconsultings.com">adamv0025@netconsultings.com</a>> wrote:<br>
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<p class="MsoNormal"><span style="mso-fareast-language:EN-US">My understanding was that the tool will combine historic data with the MTBF datapoints form all components involved in a given link in order to try and estimate a likelihood of a link failure.
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-US">Heck I imagine if one would stream a heap load of data at a ML algorithm it might draw some very interesting conclusions indeed -i.e. draw unforeseen patterns across huge datasets while trying to
understand the overall system (network) behaviour. Such a tool might teach us something new about our networks.
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-US">Next level would be recommendations on how to best address some of the potential pitfalls it found.
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<p class="MsoNormal"><span style="mso-fareast-language:EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-US">Maybe in closed systems like IP networks, with use of streaming telemetry from SFPs/NPUs/LC-CPUs/Protocols/etc.., we’ll be able to feed the analytics tool with enough data to allow it to make fairly
accurate predictions (i.e. unlike in weather or markets prediction tools where the datasets (or search space -as not all attributes are equally relevant) is virtually endless).<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="mso-fareast-language:EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-US">adam<o:p></o:p></span></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-US"><o:p> </o:p></span></p>
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<p class="MsoNormal"><b><span lang="EN-US">From:</span></b><span lang="EN-US"> NANOG <<a href="mailto:nanog-bounces@nanog.org">nanog-bounces@nanog.org</a>>
<b>On Behalf Of </b>Mel Beckman<br>
<b>Sent:</b> Tuesday, January 15, 2019 10:40 PM<br>
<b>To:</b> Vanbever Laurent <<a href="mailto:lvanbever@ethz.ch">lvanbever@ethz.ch</a>><br>
<b>Cc:</b> <a href="mailto:nanog@nanog.org">nanog@nanog.org</a><br>
<b>Subject:</b> Re: Your opinion on network analysis in the presence of uncertain events<o:p></o:p></span></p>
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<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">I know of none that take probabilities as inputs. Traditional network simulators, such as GNS3, let you model various failure modes, but probability seems squishy enough that I don’t see how it can be accurate, and thus helpful. It’s like
that Dilbert cartoon where the pointy haired boss asks for a schedule of all future unplanned outages :)
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal" style="margin-bottom:12.0pt"><a href="https://dilbert.com/strip/1997-01-29">https://dilbert.com/strip/1997-01-29</a><o:p></o:p></p>
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<p class="MsoNormal"> -mel<o:p></o:p></p>
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On Jan 15, 2019, at 11:59 AM, Vanbever Laurent <<a href="mailto:lvanbever@ethz.ch">lvanbever@ethz.ch</a>> wrote:<o:p></o:p></p>
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<p class="MsoNormal">I took the survey. It’s short and sweet — well done!<o:p></o:p></p>
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Thanks a lot, Mel! Highly appreciated!<br>
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<p class="MsoNormal">I do have a question. You ask "Are there any good?” Any good what?<o:p></o:p></p>
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I just meant whether existing network analysis tools were any good (or good enough) at reasoning about probabilistic behaviors that people care about (if any).<br>
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All the best,<br>
Laurent<o:p></o:p></p>
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