On 8/9/2013 4:19 PM, Marc Chiarini (school) wrote:
There is a very important academic & practical discussion to be had
about this. In fact Alva Couch and I and others have been examining
similar topics for years. Unfortunately I don't have the bandwidth
right now to get into it, perhaps in a few months. I'll leave you
with these two tidbits: thresholds are no good in these circumstances
(except as a coarse lower/upper bound)...you need to combine learning
(small amounts of hysteresis) and highly reactive management. Second,
one might be able to obtain unrefined but useful estimates of
performance in various components (e.g., cpu, disk, network, etc)
without an agent -- via analysis of response-time and other
statistics...essentially building a black-box model over time of how
the system is *expected* to work.
Regards,
Marc
Thanks for this tidbit.
I read the slides from your 2009 paper,
http://www.cs.tufts.edu/~couch/publications/mace-09-slides.pdf
Not sure I understood the details, but enough to move forward.
I presume you are aware of the work that Jake Brutlag did and added to
RRDTool, presented at
https://www.usenix.org/legacy/events/lisa00/full_papers/brutlag/brutlag_html/
He implemented the Holt-Winters algorithm for time-series modeling. I'm
going to use that because it's already been done for me.
So the only thing I'm going to add is a meta-analysis where you collect
say 10 SNMP variables from 10 switches each of which has 24 ports, total
2400 time-serieses, and then ask the question do enough of these differ
from their predicted values enough to indicate a systemic problem.
My question is, does anyone have a suggestion for what statistical
method to use for the meta-analysis? In your paper, it looks like you
were only looking at one time-series at a time: has anyone looked at how
to sensibly combine? Alternatively, I have not looked closely at what
you can get from the Holt-Winters stuff in RRDTool - has anyone used
that for any purpose?
- Alex Aminoff
BaseSpace.net & NBER
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