Subject: [ippm] Visualization of metric term definitions

From: Andreas Åkre Solberg <[email-masked]>
Date: Thu, 18 Aug 2005 11:37:15 +0200

We are trying to understand and use the terms defined in RFC2330 [1]  
in a report concerning metric composition and aggregation.

More specific we have read the defintitions of:
- singleton metric
- sample metric
- statistical metric
- derived metric

In the context of aggregation we need to separate "sample metrics  
from singleton metric" from "sample metrics from statistical metric".  
We made a figure, and would appreciate if anybody would comment on  
our visualization whether our perception and usage of the terms is  
consistent or conflicting with IPPM.

http://homepage.mac.com/erlang/.Public/clippings/metric-categories- 
visualization.jpg

-- 
Andreas Åkre Solberg
[email-masked]
UNINETT - http://uninett.no

Contact Info and PGP Public Key:
http://andreas.solweb.no/?Account=Work

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From: Andreas Åkre Solberg <[email-masked]>
Date: Thu, 18 Aug 2005 16:18:17 +0200

Den Aug 18, 2005 kl. 11:37 skrev Andreas Åkre Solberg:

> from singleton metric" from "sample metrics from statistical  
> metric". We made a figure, and would appreciate if anybody would  
> comment on our visualization whether our perception and usage of  
> the terms is consistent or conflicting with IPPM.
>

I also would like comments on the following figure:
http://homepage.mac.com/erlang/.Public/clippings/stepwiseAggregation.jpg

I am trying to define new terms that are not conflicting with the  
current ones in IPPM. Therefore I ask you for advice. We clearly need  
to define some terms to be used in the context of time aggregation,  
because the current framework in RFC2330 would cause every metric  
after the first aggregation step to be derived metrics.

Singleton: OK. As defined by IPPM.

Sample metric: OK. As defined by IPPM.

Statistical sample metric: We need to have a term describing a set of  
statistical metric values. The definition of sample metric explicitly  
mention that the values must be singletons, so we need  a new sample  
term to include sets of statistical values.

Statistical metric: OK. As defined by IPPM. But it should be added  
that a statistical metric is allowed to be calculated from a  
statistical sample metric, if the statistical metric is composed  
using the same aggregate function. As an example the sum of sum of  
the singleton values are equivalent to the sum of the singleton values.

Help metrics: A subgroup of statistical metrics, which include  
metrics that are defined only to support calculation of simple  
derived metrics. These help metrics are often defined because the  
wanted derived metric cannot be aggregated (in example sample  
variance is impossible to aggregate). On the contrary square sum is  
very easy to aggregate, and therefore we use it a temporary step.

Derived metrics: OK. As defined by IPPM, derived metrics can be  
composed purely by other metrics. That means a derived metric can be  
achieved both through calculation or aggregation. If aggregation is  
used, the complexity of calculation is often O(t), where t is time,  
but may be worse (in example median O(t * log t).

Simple Derived metrics: Is a derived metrics that are composed using  
only calculation and not aggregation. A simple derived metric cannot  
be defined using any samples, neither statistical samples or samples.  
This means simple derived metrics can be calculated O(1), where t is  
time. The definition of simple derived metric is done from values of  
the same time resolution as the result.

Composition methodology definitions:

Calculation: Algebraic expression of values from different  
statistical metrics or singletons.
Aggregation: Aggregation function applied on a sample of singletons  
or a statistical sample (a set of statistical metric values).

First order aggregation: Aggregation functions applied on singletons.  
Or an aggregation function applied on a statistical sample which are  
aggregated using the same aggregation function - meaning that the  
resulting aggregated data would be the same as if the aggregation  
function was applied on the full sample metric over the complete time  
period we are aggregating to.

Second order aggregation: Aggregation functions applied on  
statistical samples where the aggregation function used is not the  
same as the one used to compose the statistical metric.