In this Dagstuhl talk, I presented my current research on cloud auto-scaling and component connector self-adaptation and how I employed type-2 fuzzy control to tame the uncertainty regarding knowledge specification.

1. Fuzzy Control meets Software Engineering
Pooyan Jamshidi
IC4-Irish Centre for Cloud Computing and Commerce
School of Computing,Dublin City University
Pooyan.jamshidi@computing.dcu.ie
DagstuhlSeminar
Control Theory meets Software Engineering
September, 2014

2. Naeem Esfahani and Sam Malek,
“Uncertainty in Self-Adaptive
Software Systems”
Knowledge
Specification
Uncertainty
2

3. 3

4. Application #1:
Auto-scaling

5. ~50% = wasted hardwareActual trafficTypical weekly traffic to Web-based applications (e.g., Amazon.com)
5

6. Problem 1: ~75% wasted capacityActual demandProblem 2: customer lostTraffic in an unexpected burst in requests (e.g. end of year traffic to Amazon.com)
6

7. Really like this?? Auto-scaling enables you to realize this ideal on-demand provisioning
Time
Demand
? Enacting change in the Cloud resources are notreal-time
7

8. Capacity we can provision with Auto-ScalingA realistic figure of dynamic provisioning
8

9. 9

10. 0 50 100
0
500
1000
1500
0 50 100
100
200
300
400
500
0 50 100
0
1000
2000
0 50 100
0
200
400
600
10

11. 11

12. These quantitative values are required to be determined by the user requires deep knowledge of application (CPU, memory, thresholds) requires performance modeling expertise (when and how to scale) A unified opinion of user(s) is required
Amazon auto scaling
Microsoft Azure Watch
12
Microsoft Azure Auto- scaling Application Block
12

13. 13

14. Uncertainty related to enactment latency:
The same scaling action (adding/removing
a VM with precisely the same size) took
different time to be enacted on the
cloud platform (here is Microsoft Azure)
at different points and
this difference were significant
(up to couple of minutes).
The enactment latency would be also different
on different cloud platforms. 14

15. Offline benchmarking
Trial-and-error
Expert knowledge
Costly and
not systematic
A. Gandhi, P. Dube, A. Karve, A. Kochut, L. Zhang, Adaptive, “Model-driven Autoscalingfor Cloud Applications”, ICAC’14
arrival rate (req/s)
95% Resp. time (ms)
400 ms
60 req/s 15

16. RobusT2Scale Initial setting +
elasticity rules +
response-time SLA
environment
monitoring
application
monitoring
scaling
actions
Fuzzy Reasoning
Users
Prediction/
Smoothing
16

17. 0 0.5 1 1.5 2 2.5 3
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Region of
definite
satisfaction
Region of
definite
Region of dissatisfaction
uncertain
satisfaction
Performance Index
Possibility
Performance Index
Possibility
words can mean different
things to different people
Different users often
recommend
different elasticity policies
0 0.5 1 1.5 2 2.5 3
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Type-2 MF
Type-1 MF
17

18. 18

19. Workload
Response time
0 10 20 30 40 50 60 70 80 90 100
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
2
Membershuip grade
0 10 20 30 40 50 60 70 80 90 100
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Membersuhip grade
=>
=>
mean
sd
19

20. Rule
(풍)
Antecedents Consequent
풄풂풗품
풍
Workload
Response-time
Normal
(-2)
Effort
(-1)
Medium
Effort
(0)
High
Effort
(+1)
Maximum
Effort (+2)
1 Very low Instantaneous 7 2 1 0 0 -1.6
2 Very low Fast 5 4 1 0 0 -1.4
3 Very low Medium 0 2 6 2 0 0
4 Very low Slow 0 0 4 6 0 0.6
5 Very low Very slow 0 0 0 6 4 1.4
6 Low Instantaneous 5 3 2 0 0 -1.3
7 Low Fast 2 7 1 0 0 -1.1
8 Low Medium 0 1 5 3 1 0.4
9 Low Slow 0 0 1 8 1 1
10 Low Very slow 0 0 0 4 6 1.6
11 Medium Instantaneous 6 4 0 0 0 -1.6
12 Medium Fast 2 5 3 0 0 -0.9
13 Medium Medium 0 0 5 4 1 0.6
14 Medium Slow 0 0 1 7 2 1.1
15 Medium Very slow 0 0 1 3 6 1.5
16 High Instantaneous 8 2 0 0 0 -1.8
17 High Fast 4 6 0 0 0 -1.4
18 High Medium 0 1 5 3 1 0.4
19 High Slow 0 0 1 7 2 1.1
20 High Very slow 0 0 0 6 4 1.4
21 Very high Instantaneous 9 1 0 0 0 -1.9
22 Very high Fast 3 6 1 0 0 -1.2
23 Very high Medium 0 1 4 4 1 0.5
24 Very high Slow 0 0 1 8 1 1
25 Very high Very slow 0 0 0 4 6 1.6
Rule
(퐥)
Antecedents Consequent
풄풂풗품
Work 풍
load
Response
-time
-2 -1 0 +1 +2
12 Medium Fast 2 5 3 0 0 -0.9
10 experts’ responses
푅푙 : IF (the workload (푥1) is 퐹 푖1, AND the response-time
(푥2) is 퐺 푖2), THEN (add/remove 푐푎푣푔
푙 instances).
푐푎푣푔
푙 =
푢=1
푁푙 푤푢푙
× 퐶
푢=1
푁푙 푤푢푙
Goal: pre-computations of costly calculations
to make a runtime efficient elasticity
reasoning based on fuzzy inference 20

21. Liang, Q., Mendel, J. M. (2000). Interval type-2 fuzzy
logic systems: theory and design. Fuzzy Systems, IEEE
Transactions on, 8(5), 535-550.
Scaling Actions
Monitoring Data
21

22. 0 10 20 30 40 50 60 70 80 90 100
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.5954
0.3797
푀
0 10 20 30 40 50 60 70 80 90 100
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.2212
0.0000
0 10 20 30 40 50 60 70 80 90 100
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
2
u
0 10 20 30 40 50 60 70 80 90 100
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
2
u
Monitoring data
Workload
Response time
22

23. 010203040506070809010000.10.20.30.40.50.60.70.80.910.59540.3797 010203040506070809010000.10.20.30.40.50.60.70.80.910.95680.9377
23

24. 24

25. 푦푙 , 푦푟
25

26. 26

27. 050100050010001500050100100200300400500 0501000100020000501000200400600 0501000500100005010005001000
27

28. 28

29. 0 10 20 30 40 50 60 70 80 90 100
-500
0
500
1000
1500
2000
Time (seconds)
Number of hits
Original data
betta=0.10, gamma=0.94, rmse=308.1565, rrse=0.79703
betta=0.27, gamma=0.94, rmse=209.7852, rrse=0.54504
betta=0.80, gamma=0.94, rmse=272.6285, rrse=0.70858
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Big spike Dual phase Large variations Quickly varying Slowly varying Steep tri phase
0 50 100
0
500
1000
1500
0 50 100
100
200
300
400
500
0 50 100
0
1000
2000
0 50 100
0
200
400
600
0 50 100
0
500
1000
0 50 100
0
500
1000
Root Relative Squared Error
29

30. SUT
Criteria
Big spike
Dual phase
Large variations
Quickly varying
Slowly varying
Steep tri phase
RobusT2Scale
푟푡95%
973ms
537ms
509ms
451ms
423ms
498ms 푣푚
3.2
3.8
5.1
5.3
3.7
3.9
Overprovisioning
푟푡95%
354ms
411ms
395ms
446ms
371ms
491ms 푣푚
6
6
6
6
6
6
Under provisioning
푟푡95%
1465ms
1832ms
1789ms
1594ms
1898ms
2194ms 푣푚
2
2
2
2
2
2
SLA: 풓풕ퟗퟓ≤ퟔퟎퟎ풎풔
For every 10s control interval•RobusT2Scale is superior to under-provisioning in terms of guaranteeing the SLA and does not require excessive resources•RobusT2Scale is superior to over-provisioning in terms of guaranteeing required resources while guaranteeing the SLA 30

31. 0
0.02
0.04
0.06
0.08
0.1
alpha=0.1 alpha=0.5 alpha=0.9 alpha=1.0
Root Mean Square Error
Noise level: 10% 31

32. Application #2: Self-Adaptive Software Connectors

33. ⊭푅
Environment=D
Environment=D’
Environment=D’
⊨푅
⊨푅 Adapted to satisfy requirements while it is running Reliable RuntimeEfficient
33

34. 0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
1.5
2
2.5
3
3.5
4
output1
34

35. 35

36. 0
1
2
3
4
5
x 10
-3
1 2 3 4 5 6 7 8
0
0.05
0.1
0.15
0.2
0.25
1 2 3 4 5
36

37. 0.05
0.1
0.15
0.2
0.25
0.3
0.35
Type-1 FLS Type-2 FLS
RMSE
• The rule reduction reduced the rules
quite considerably.
• IT2 FLCs are more robust due to less
mean error and less variation in the
estimation error.
• T1 FLCs in some realization drop more
rules in comparison with the IT2 FLCs.
• IT2 FLC original designs can be designed
with less rules.
37

38. 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1% 2% 3% 4% 5% 6% 7% 8% 9% 10%
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1% 2% 3% 4% 5% 6% 7% 8% 9% 10%
0
0.2
0.4
0.6
0.8
1
1% 2% 3% 4% 5% 6% 7% 8% 9% 10%
Noise Amplitude (α=0.5)
RMSE
α=0.7 α=0.95
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1% 2% 3% 4% 5% 6% 7% 8% 9% 10%
IT2 FLC T1 FLC
38

39. Future Directions

40. Current Solution
(my PhD Thesis
+ IC4 work on
auto-scaling)
Future Plan
Updating K
in MAPE-K
Design-time @ Runtime
Assistance
Multi-cloud

41. 41

42. 42

43. Challenge 1: ~75% wasted capacity
Actual
demand
Challenge 2:
customer lost
⊭ 푅
Environment=D
Environment=D’
Environment=D’
⊨ 푅
⊨ 푅
Adapted to satisfy
requirements
while it is running
 Reliable
 Run-time Efficient
Current Solution
(my PhD Thesis
+ IC4 work on
auto-scaling)
Future Plan
Updating K
in MAPE-K
Design-time @ Runtime
Assistance
Multi-cloud
RobusT2Scale Initial setting +
elasticity rules +
response-time SLA
environment
monitoring
application
monitoring
scaling
actions
Fuzzy Reasoning
Users
Prediction/
Smoothing
43

44. http://computing.dcu.ie/~pjamshidi/PDF/SEAMS2014.pdf
More
Details?
=>
http://www.slideshare.net/pooyanjamshidi/
Slides?
=>
44
Thank you!
=>
Aakash Ahmad
Claus Pahl
SoodehFarokhi

46. Requirement: R
0 50 100
0
500
1000
1500
0 50 100
100
200
300
400
500
0 50 100
0
1000
2000
0 50 100
0
200
400
600
0 50 100
0
500
1000
0 50 100
0
500
1000
Environment: D
Model: S
Implementation Monitoring
Execution
Runtime
Design-time
Reasoning
Self-adaptation
Specification
S,D⊨R
S,D⊭R
46

47. Looking for references?
http://computing.dcu.ie/~pjamshidi/PDF/SEAMS2014.pdf
47

48. 48

49. 49

50. 50

51. 푦푟 − 푦푙
51

52. 52