[[TOC]]

= Performance data =
  
We have gathered performance data of two types of ABAC scenario.
The first is the daisychaining of a large answer proof from a 
similarly sized ruleset.  The second is the searching of a proof 
from a noisy ruleset. 

== daisychain ==

Many cascading Likes-rules and their matching principal credentials are
generated:

{{{
John0.likes <- John0
John1.likes <- John0
John2.likes <- John1
John3.likes <- John2
..
JohnMAX.likes <- JohnMAX-1
}}}


Following graph shows the the time needed to load both attribute
credentials and principal certificates ((2*MAX)+2) into the YAP prolog 
db. The Y axis is the total time in milliseconds. The X axis is the 
number of credentials that are in the YAP backend db.

[[Image(source:/examples/scaling_tests/plotting/JPEGS/likes_load.jpeg)]] 

A valid query, 
'''JohnMAX.likes<-?-John0'''
, is issued multiple
times. The first query's time is collected and then subsequent 100 queries
are collected and an average is taken.  Next graph shows these lines. The
X axis is the query time in microseconds and the Y axis is number of 
credentials in the YAP backend db.

[[Image(source:/examples/scaling_tests/plotting/JPEGS/likes_goodquery.jpeg)]]

An invalid query in the form of '''John0.likes<-?-JohnMAX''' is issued 
multiple times. The first query's time is collected and then subsequent 
100 queries are collected and an average is taken.  Next graph shows these 
lines. The X axis is the query time in microseconds and the Y axis is 
number of credentials in the YAP backend db.

[[Image(source:/examples/scaling_tests/plotting/JPEGS/likes_badquery.jpeg)]]


== haystack ==

The core credential setup is borrowed from the
Ralphs' fruitprice and shopper's eating preference RT2 example,
 
{{{
Mary.what2eat <- Ralphs.fruitprice(P:[..2.00])
Bob.what2eat <- Ralphs.fruitprice(P:[1.00 .. 5.00])
Ralphs.fruitprice(1.50) <- 'apple'
Ralphs.fruitprice(1.50) <- 'kiwi'
Ralphs.fruitprice(2.50) <- 'black rasberry'
Ralphs.fruitprice(0.50) <- 'navel orange'
}}}

Noises are introduced by adding the rules about bananas
and Johns.

{{{
Ralphs.fruitprice(1.00) <- 'banana1'
Ralphs.fruitprice(2.00) <- 'banana2'
..
Ralphs.fruitprice(MAX.00) <- 'bananaMAX'
}}}

and,

{{{
John1.what2eat <- Ralphs.fruitprice(P:[1.00 .. 5.00])
John2.what2eat <- Ralphs.fruitprice(P:[1.00 .. 5.00])
..
JohnMAX.what2eat <- Ralphs.fruitprice(P:[1.00 .. 5.00])
}}}

Following graph shows the the time needed to load both attribute
credentials and principal certificates ((3*MAX)+9) into the YAP prolog 
db. The Y axis is the total time in milliseconds. The X axis is the 
number of credentials that are in the YAP backend db.

[[Image(source:/examples/scaling_tests/plotting/JPEGS/fruit_load.jpeg)]]

A valid query, '''Mary.what2eat<-?-'navel orange'''', is issued multiple 
times. The returning answer proof is, 
{{{
Mary.what2eat <- pRalphs.fruitprice(P:[..2.00])
Ralphs.fruitprice(0.50) <- 'navel orange'
}}}
  
The first query's time is collected and the subsequent 100 queries 
are collected and averaged. Next graph shows these lines. The X 
axis is the query time in 
microseconds and the Y axis is number of credentials in the YAP 
backend db.

[[Image(source:/examples/scaling_tests/plotting/JPEGS/fruit_goodquery.jpeg)]]

An invalid query in the form of '''Bob.what2eat<-?-'navel orange''''
is issued multiple times. The first query's time is collected and
then subsequent 100 queries are collected and averaged.  Next graph 
shows these lines. The X axis is the query time in microseconds 
and the Y axis is number of credentials in the YAP backend db.

[[Image(source:/examples/scaling_tests/plotting/JPEGS/fruit_badquery.jpeg)]]