Apdex Implementation at AOL CMG International Conference San Diego, California December 5, 2007 Eric Goldsmith Operations Architect [email protected] om Session 45A
Apdex Implementation at AOL CMG International Conference San Diego, California December 5, 2007 Eric Goldsmith Operations Architect [email protected].
Mar 26, 2015
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Apdex Implementation at AOL
CMG International Conference
San Diego, California
December 5, 2007
Eric GoldsmithOperations [email protected]
Session 45A
Slide 2
Our Environment
Operations organization
Measuring Web site performance from customer-centric view Full page load measured from outside datacenter Multiple geographic locations
Goals Short-term: Identify product issues/outages Long-term: Achieve uniform geographic performance, in parity with
competitors
Slide 3
Current Metrics & Shortcomings
Response Time & Availability Often don’t tell whole user-experience story
Reported as averages Hides variance, and is skewed by outliers
Reported in absolute numbers No context of a target (goal) value
Slide 4
Goals of Apdex use
Inclusive view of performance, availability, and data distribution
“Building in” of a target, and data normalization around it
Performance is evaluated qualitatively against a target
Slide 5
Data Source and Collection
Using commercial 3rd-party tool to gather measurements from multiple geographic locations
Data of interest for our Apdex calculations1. Date/Time
2. Measurement Value
3. Success/Error (Error = Frustrated)
4. Test Location
Data collection is batched (daily)
Slide 6
Calculation and Graphing in Excel
Calculate sub-score for each row (data point)If (error) score = 0
else if (measurement <= T) score = 1else if (measurement <= F) score = 0.5
else score = 0
Define interval over which to calculate Adpex score– Hourly, daily, weekly, etc.– Segregate by location, if desired– Apdex spec recommends >100 data points per interval
Then calculate overall Apdex score for interval =sum(sub-scores) / count(measurements)
Get fancy with DSUM() and DCOUNT() Database lookups simplify segregation by date, location, etc.
Slide 7
Target ‘T’ Determination
We chose our targets based on competitor performance For a given Web site, identify its target competitor (may be self)
The ‘T’ marker method we chose initially was based on “Best Time Multiple” “Measure average response time from a ‘good’ location, then add
50% to build in tolerance for other locations”
Instead, we averaged data from all locations Our thinking was that the 50% inflation wasn’t necessary because of
the natural diversity of the data from multiple geographic locations
Slide 8
Example Results Presentation
Performance - National
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0.55
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0.65
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0.75
0.80
0.85
0.90
0.95
1.00
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Ap
dex
Sco
re
A [1.1] B [1.1] C [1.1]
Unacceptable
Poor
Fair
Good
Excellent
Slide 9
Example Results Presentation cont’d
Performance - Regional
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0.55
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0.75
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0.85
0.90
0.95
1.00
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Ap
dex
Sco
re
A-East [1.1] B-East [1.1] C-East [1.1]
A-West [1.1] B-West [1.1] C-West [1.1]
Unacceptable
Poor
Fair
Good
Excellent
Slide 10
Problems with our initial T
Initial results were promising…but as we examined data over time, the Apdex results didn’t always correlate well with observations
Performance - West
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0.35
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0.45
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0.55
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0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
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07
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Ap
de
x S
co
re
A-West [1.1] B-West [1.1] C-West [1.1]
Unacceptable
Poor
Fair
Good
Excellent
Target competitor never achieves Excellent level
Significant performance change not reflected(see next slide)
Slide 11
West Coast Page Load Time
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
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Tim
e (s
ec)
Before After T F
Example of Initial T Problem
• 44% reduction in average load time
• But Apdex score didn’t change
Slide 12
Plan B
We experimented with various T determination techniques, and eventually settled on the “Empirical Data” method “Find T that results in the proper Apdex for a well studied group”
In our environment… For a given Web site, identify its target competitor (may be self)
– The performance of this competitor is defined as “Excellent” Determine the smallest T such that the competitor’s Apdex score
remains Excellent for a period of time (at least 1 month)
Slide 13
Performance - West
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0.25
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0.35
0.40
0.45
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0.55
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0.65
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0.75
0.80
0.85
0.90
0.95
1.00
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31-A
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Ap
dex
Sco
re
A-West [1.6] B-West [1.6] C-West [1.6]
Unacceptable
Poor
Fair
Good
Excellent
New T
With the new T, the Apdex results correlate better with observations
Target competitor now achieves Excellent level
Performance change now reflected
Slide 14
Changing T
Define technique for reevaluating T on an ongoing basis But don’t want to change T too often
Suggestions for reevaluating T: Quarterly, looking at prior 3 months of data When a significant product change occurs When requested (from business)
Slide 15
Example - T Change
Performance - National
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0.25
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0.55
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0.65
0.70
0.75
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0.85
0.90
0.95
1.00
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31-A
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Ap
dex
Sco
re
A [1.6] B [1.6] C [1.6]A [1.1] B [1.1] C [1.1]
Unacceptable
Poor
Fair
Good
Excellent
Slide 16
Apdex vs. Other Metrics
Performance - National
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1-S
ep-0
7
2-S
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3-S
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30-S
ep-0
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Ap
dex
Sco
re
A [1.6] B [1.6] C [1.6]
Unacceptable
Poor
Fair
Good
Excellent
Slide 17
95.0
95.5
96.0
96.5
97.0
97.5
98.0
98.5
99.0
99.5
100.0
1-S
ep-0
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Ava
ilab
iilty
(p
erce
nt)
0.000.050.100.150.200.250.300.350.400.450.500.550.600.650.700.750.800.850.900.951.00
Ap
dex
Sco
re A [1.6]
B [1.6]
C [1.6]
Unacceptable
P oor
Fair
Good
Excellent
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Per
form
ance
(se
con
ds)
A
B
C
Apdex vs. Performance & Availability
Deep Dive 1
Virtually no change in Apdex for B, despite large change in performance and availability.
Deep Dive 2
Apdex shows B performing better than A. Perf/Avail charts show opposite.
Slide 18
Performance - National
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
12-S
ep-0
7
13-S
ep-0
7
14-S
ep-0
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15-S
ep-0
7
Per
form
ance
(se
con
ds)
B B-Avg
T F
Deep Dive 1
0
10
20
30
40
50
60
Pe
rfo
rman
ce
(sec
on
ds)
S 377 (0.53)
T 314 (0.22)
F 22
A 0.75
S 422 (0.60)
T 195 (0.14)
F 88
A 0.74
S 419 (0.59)
T 219 (0.15)
F 73
A 0.74
Virtually no change in Apdex for B, despite large change in performance and availability.
Slide 19
Performance - National
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
19-S
ep-0
7
20-S
ep-0
7
21-S
ep-0
7
22-S
ep-0
7
23-S
ep-0
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Per
form
ance
(se
con
ds)
A BA-Avg B-AvgT F
Deep Dive 2
0
10
20
30
40
50
60
Pe
rfo
rman
ce (
sec
on
ds)
807 (0.59)
390 (0.55)
552 (0.20)
215 (0.15)
15 106
0.79 0.70
341 (0.24)
436 (0.61)
1074 (0.38) 213 (0.15)
7 63
0.62 0.76
Apdex shows B performing better than A. Perf/Avail charts show opposite.
506 (0.36)
553 (0.79)
919 (0.32) 146 (0.10)
5 5
0.68 0.89
942 (0.66)
607 (0.84)
469 (0.17) 111 (0.08)
4 3
0.83 0.92
Slide 20
Closing Thoughts
We’re still exploring the application of Apdex in an Operations organization Can Apdex be used to identify the day to day "issues" traditionally
identified through analysis of performance and availability metrics? Or is it better suited as a method of performance representation for
the business side of the house?
Interesting to calc: what would it take for a product to achieve the next "band" of performance What performance level do I need to move from Poor to Fair Help in establishing interim targets
Thank You
Questions?
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