management of time requirements in component-based systemstianhuat.github.io/slides/fm2014.pdf ·...

Management of Time Requirements in

Component-based Systems

Yi Li1 Tian Huat Tan2 Marsha Chechik1

1. University of Toronto 2. Singapore University of Technology and Design

FM 2014 Singapore May 14, 2014

1

Component-based Software Engineering

2

Component-based Software Engineering

Business Goals &System Requirements

2

Component-based Software Engineeringmodularity, reusability, separation of concerns

Business Goals &System Requirements

2

Timing Requirements

3

Timing RequirementsVehicle Control Systems

• Electronic Stability Control (ESC)

• Anti-lock braking system (ABS)

3




Smart Phones

3




Smart Phones• Sensors - motion tracking

3





Web Service Compositions• Ticket Booking

• Stock Quotes

3





Web Service Compositions• Ticket Booking

• Stock Quotes

…

3

Existing Approach: LTR

?

4

Existing Approach: LTR

Failure!?

4

Existing Approach: LTRMust finish within 4s!

4

Previous Work: [ICSE’13]• Local Timing Requirements

(LTR) synthesis • Web Services - BPEL • Monolithic representation


tDS tFS tPS

4




tDS tFS tPS

LTR:¬(0≤tDS⋀1≤tFS⋀1≤tPS)

⋀((0≤tDS⋀0≤tFS⋀0≤tPS)⇒tDS≤3) ⋀((0≤tDS⋀0≤tFS≤1⋀0≤tPS)⇒tDS+tFS≤3) ⋀((0≤tDS⋀1≤tFS⋀0≤tPS≤1)⇒tDS+tPS≤2)

4




tDS tFS tPS



4

LTR - monolithic constraint Pros:+ distills complicated composition structures into a single formula + precisely captures all feasible combinations

Cons:- imposes dependencies across components - lacks support for localized debugging/repairing




tDS tFS tPS



4

uLTR:(0≤tDS<1⋀0≤tFS<1) ∨(0≤tDS<1⋀0≤tPS<1)

LTR vs. uLTR

• Component-dependent timing requirement

• Linear real arithmetic

• Precise

• Monolithic

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uLTR:(0≤tDS<1⋀0≤tFS<1) ∨(0≤tDS<1⋀0≤tPS<1)

• Component-independent under-approximated LTR

• Intervals

• Under-approximated

• Localized



All possible timing configurations,

e.g., tDS = 1, tFS = 0.5, tPS = 0.8

LTR vs. uLTR

6

Precision


e.g., tDS = 1, tFS = 0.5, tPS = 0.8

LTR vs. uLTR

6

LTR

Precision

unsafe

safe


e.g., tDS = 1, tFS = 0.5, tPS = 0.8

LTR vs. uLTR

6

LTR

uLTRfalse negatives

Precision

Precision(uLTR) =

#configurations satisfied by uLTR

#configurations satisfied by LTR

⇥ 100%

under- approximation


e.g., tDS = 1, tFS = 0.5, tPS = 0.8

LTR vs. uLTR

6

LTR

uLTR

Precision

Precision(uLTR) =

#configurations satisfied by uLTR

#configurations satisfied by LTR

⇥ 100%

ChecklistWhat is uLTR?

• Component-independent under-approximated LTR • Soundness: ensure timing safety

How to break up the monolithic constraint?• Compute uLTR from LTR • Precision: preserve as many choices as possible

How can localized constraints support the management of timing requirements?

• uLTR for component selection • uLTR for runtime adaptation and recovery

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φ:¬(0≤tDS⋀1≤tFS⋀1≤tPS)


Compute uLTR from LTR

0

3

1

2

1

tDS

tF S

tP S

8




0

3

1

2

1

tDS

tF S

tP S

0

3

1

2

1

tDS

tF S

tP S

8



B:(0≤tDS<1⋀0≤tFS<1) ∨(0≤tDS<1⋀0≤tPS<1)


0

3

1

2

1

tDS

tF S

tP S

8

0

3

1

2

1

tDS

tF S

tP S


0

3

1

2

1

tDS

tF S

tP S

8


0

3

1

2

1

tDS

tF S

tP S

0

3

1

2

1

tDS

tF S

tP S

B1= MaxCube(φ)

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0

3

1

2

1

tDS

tF S

tP S

0

3

1

2

1

tDS

tF S

tP S

0

3

1

2

1

tDS

tF S

tP S

B1= MaxCube(φ)InfCube(φ,B1)

8


0

3

1

2

1

tDS

tF S

tP S

0

3

1

2

1

tDS

tF S

tP S

0

3

1

2

1

tDS

tF S

tP S

0

3

1

2

1

tDS

tF S

tP S

B1= MaxCube(φ)InfCube(φ,B1)B2= MaxCube(φ)

8


0

3

1

2

1

tDS

tF S

tP S


…

0

3

1

2

1

tDS

tF S

tP S

8

B=Merge(B1,…,Bi)


0

3

1

2

1

tDS

tF S

tP S


…

0

3

1

2

1

tDS

tF S

tP S

0

3

1

2

1

tDS

tF S

tP S

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if (h(Bi)<ω)return;

B=Merge(B1,…,Bi)


0

3

1

2

1

tDS

tF S

tP S


…

Soundness

Termination

0

3

1

2

1

tDS

tF S

tP S

0

3

1

2

1

tDS

tF S

tP S

Precision

8

if (h(Bi)<ω)return;

B=Merge(B1,…,Bi)

SMT EncodingsMaxCube(φ) //return the hypercube in φ with maximum volume

InfCube(φ,B) //relax in one direction if possible

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// sample arbitrary hyper-rectangle

9

✓ , 8V ars(') · ((V

vi2V ars(')

li vi ui) ) ')




// sample maximal hyper-cube

9

✓ , 8V ars(') · ((V

vi2V ars(')

li vi ui) ) ')

Optimize(✓ ^ (V

vi2V ars(')

(ui � li = h)), h)





9

✓ , 8V ars(') · ((V

vi2V ars(')

li vi ui) ) ')

Optimize(✓ ^ (V

vi2V ars(')

(ui � li = h)), h)

Symbolic Optimization

[POPL’14]





// relax lower bound

// relax upper bound

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✓ , 8V ars(') · ((V

vi2V ars(')

li vi ui) ) ')

unSAT? (¬(B[li/1] ) '))

unSAT? (¬(B[ui/1] ) '))

Optimize(✓ ^ (V

vi2V ars(')

(ui � li = h)), h)





// relax lower bound

// relax upper bound

// heights of sampled hyper-cubes form a non-increasing sequence

9

✓ , 8V ars(') · ((V

vi2V ars(')

li vi ui) ) ')

unSAT? (¬(B[li/1] ) '))

unSAT? (¬(B[ui/1] ) '))

Optimize(✓ ^ (V

vi2V ars(')

(ui � li = h)), h)






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uLTR for component selection

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publish

11


publish

LTR:(tFS<1⋀

tDS≤3⋀tDS+

tFS≤3)∨ …

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publish

request

retrieve

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Carminati et al., 2005

Rajendran et al., 2010

Al-Masri & Mahmoud, 2007


publish

request

retrieve

tFS<1s

finds the “best” match given localized

constraints

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• Real-world Web Service data: QWS dataset • Case studies: online booking service, … • Evaluate the percentage of false-negatives (precision)

w.r.t. size of the uLTR model


100 200

Size of uLTR model (|BS|)

10

20

30

40

50

60

70

80

90

100

Precision

ofuLTR

mod

el(%

)

QWS, Te = 10.8s

Rand, Te ≈ 201.4s

1 4 7 10


60

65

70

75

80

85

90

95

100

Precision

ofuLTR

mod

el(%

)

QWS, Te = 0.9s

Rand, Te ≈ 10.8s

1 4 7 10


60

65

70

75

80

85

90

95

100

Precision

ofuLTR

mod

el(%

)

QWS, Te = 2.7s

Rand, Te ≈ 242.2s

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• Real-world Web Service data: QWS dataset • Case studies: online booking service, … • Evaluate the percentage of false-negatives (precision)

w.r.t. size of the uLTR model


100 200


10

20

30

40

50

60

70

80

90

100

Precision

ofuLTR

mod

el(%

)

QWS, Te = 10.8s

Rand, Te ≈ 201.4s

1 4 7 10


60

65

70

75

80

85

90

95

100

Precision

ofuLTR

mod

el(%

)

QWS, Te = 0.9s

Rand, Te ≈ 10.8s

1 4 7 10


60

65

70

75

80

85

90

95

100

Precision

ofuLTR

mod

el(%

)

QWS, Te = 2.7s

Rand, Te ≈ 242.2s

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Strong dependency in the original LTR: t1+t2+3t3-2t4<4

uLTR for runtime adaptation and recovery

Must finish within 4s! Monitor

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Must finish within 4s!

response time

Monitor

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response time

repairing plan

Monitor

13

¬(0≤tDS⋀1≤tFS⋀1≤tPS) ⋀((0≤tDS⋀0≤tFS⋀0≤tPS)⇒tDS≤3)

⋀((0≤tDS⋀0≤tFS≤1⋀0≤tPS)⇒tDS+tFS≤3) ⋀((0≤tDS⋀1≤tFS⋀0≤tPS≤1)⇒tDS+tPS≤2)



response time

repairing plan

Monitor

13

¬(0≤tDS⋀1≤tFS⋀1≤tPS) ⋀((0≤tDS⋀0≤tFS⋀0≤tPS)⇒tDS≤3)

⋀((0≤tDS⋀0≤tFS≤1⋀0≤tPS)⇒tDS+tFS≤3) ⋀((0≤tDS⋀1≤tFS⋀0≤tPS≤1)⇒tDS+tPS≤2)



?

response time

repairing plan

Monitor

Have to replace both DS and FS.

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tDS<1 tFS<∞ tPS<1

response time

repairing plan

Monitor

13



tDS<1 tFS<∞ tPS<1

response time

repairing plan

Monitor

Replacing DS is enough!

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tDS<1 tFS<∞ tPS<1

response time

repairing plan

Monitor

Replacing DS is enough!

The “meaning” of LTR: safe if one of tFS and tPS is less than 1.

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Experiments:• Use real service response time • Simulate violations by adding uniform random delays to

components • Compare the length of recovery plans generated by

LTR and uLTR • In ~90% cases, uLTR discovers shorter repairs

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Limitations & Future WorkLimited evaluation

• Need to look at other domains

Proof of concept, not the silver bullet• Generalize the sampling algorithm: allow arbitrary

hyper-rectangles

Scalability issues:• Quantifier elimination • Balance between precision and performance

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Questions?Thank you!

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ReferencesLi, Y., Albarghouthi, A., Gurfinkel, A., Kincaid, Z., Chechik, M.: Symbolic Optimization with SMT Solvers. In: Proc. of POPL 2014 (2014)

Tan, T.H., André, E., Sun, J., Liu, Y., Dong, J.S., Chen, M.: Dynamic Synthesis of Local Time Requirement for Service Composition. In: Proc. of ICSE 2013, pp. 542–551 (2013)

Al-Masri,E.,Mahmoud,Q.H.:QoS-based Discovery and Ranking of Web Services.In:Proc. of ICCCN 2007, pp. 529–534. IEEE (2007)

Wang, S., Rho, S., Mai, Z., Bettati, R., Zhao, W.: Real-time Component-based Systems. In: Proc. of RTETAS 2005, pp. 428–437 (2005)

Carminati, B., Ferrari, E., Hung, P.C.: Exploring Privacy Issues in Web Services Discovery Agencies. IEEE Security & Privacy 3(5), 14–21 (2005)

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