Management of Time Requirements in Component-based Systems Yi Li 1 - - PowerPoint PPT Presentation

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

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Component-based Software Engineering

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Component-based Software Engineering

Business Goals & System Requirements

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Component-based Software Engineering

modularity, reusability, separation of concerns

Business Goals & System Requirements

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Timing Requirements

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Timing Requirements

Vehicle Control Systems

• Electronic Stability Control (ESC)
• Anti-lock braking system (ABS)

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Timing Requirements

Vehicle Control Systems

• Electronic Stability Control (ESC)
• Anti-lock braking system (ABS)

Smart Phones

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Timing Requirements

Vehicle Control Systems

• Electronic Stability Control (ESC)
• Anti-lock braking system (ABS)

Smart Phones

• Sensors - motion tracking

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Timing Requirements

Vehicle Control Systems

• Electronic Stability Control (ESC)
• Anti-lock braking system (ABS)

Smart Phones

• Sensors - motion tracking

Web Service Compositions

• Ticket Booking
• Stock Quotes

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Timing Requirements

Vehicle Control Systems

• Electronic Stability Control (ESC)
• Anti-lock braking system (ABS)

Smart Phones

• Sensors - motion tracking

Web Service Compositions

• Ticket Booking
• Stock Quotes

…

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Existing Approach: LTR

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Existing Approach: LTR

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Existing Approach: LTR

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Existing Approach: LTR

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Existing Approach: LTR

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Existing Approach: LTR

Failure!

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Existing Approach: LTR

Must finish within 4s!

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Previous Work: [ICSE’13]

• Local Timing Requirements

(LTR) synthesis

• Web Services - BPEL
• Monolithic representation

Existing Approach: LTR

Must finish within 4s! tDS tFS tPS

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Previous Work: [ICSE’13]

• Local Timing Requirements

(LTR) synthesis

• Web Services - BPEL
• Monolithic representation

Existing Approach: LTR

Must finish within 4s! 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)

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Previous Work: [ICSE’13]

• Local Timing Requirements

(LTR) synthesis

• Web Services - BPEL
• Monolithic representation

Existing Approach: LTR

Must finish within 4s! 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)

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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

Previous Work: [ICSE’13]

• Local Timing Requirements

(LTR) synthesis

• Web Services - BPEL
• Monolithic representation

Existing Approach: LTR

Must finish within 4s! 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)

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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

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)

All possible timing configurations, e.g., tDS = 1, tFS = 0.5, tPS = 0.8

LTR vs. uLTR

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Precision

All possible timing configurations, e.g., tDS = 1, tFS = 0.5, tPS = 0.8

LTR vs. uLTR

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LTR

Precision

unsafe safe

All possible timing configurations, e.g., tDS = 1, tFS = 0.5, tPS = 0.8

LTR vs. uLTR

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LTR

uLTR

false negatives

Precision

Precision(uLTR) = #configurations satisfied by uLTR

#configurations satisfied by LTR × 100% under- approximation

All possible timing configurations, e.g., tDS = 1, tFS = 0.5, tPS = 0.8

LTR vs. uLTR

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LTR

uLTR

Precision

Precision(uLTR) = #configurations satisfied by uLTR

#configurations satisfied by LTR × 100%

Checklist

What 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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Checklist

What 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) ⋀((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)

Compute uLTR from LTR

3 1 2 1 tDS tF S tP S

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φ:

¬(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)

Compute uLTR from LTR

3 1 2 1 tDS tF S tP S 3 1 2 1 tDS tF S tP S

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φ:

¬(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)

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

Compute uLTR from LTR

3 1 2 1 tDS tF S tP S

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3 1 2 1 tDS tF S tP S

Compute uLTR from LTR

3 1 2 1 tDS tF S tP S

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Compute uLTR from LTR

3 1 2 1 tDS tF S tP S 3 1 2 1 tDS tF S tP S

B1= MaxCube(φ)

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Compute uLTR from LTR

3 1 2 1 tDS tF S tP S 3 1 2 1 tDS tF S tP S 3 1 2 1 tDS tF S tP S

B1= MaxCube(φ) InfCube(φ,B1)

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Compute uLTR from LTR

3 1 2 1 tDS tF S tP S 3 1 2 1 tDS tF S tP S 3 1 2 1 tDS tF S tP S 3 1 2 1 tDS tF S tP S

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

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Compute uLTR from LTR

3 1 2 1 tDS tF S tP S

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

3 1 2 1 tDS tF S tP S

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B=Merge(B1,…,Bi)

Compute uLTR from LTR

3 1 2 1 tDS tF S tP S

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

3 1 2 1 tDS tF S tP S 3 1 2 1 tDS tF S tP S

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if (h(Bi)<ω) return; B=Merge(B1,…,Bi)

Compute uLTR from LTR

3 1 2 1 tDS tF S tP S

B1= MaxCube(φ) InfCube(φ,B1) B2= MaxCube(φ) … Soundness Termination

3 1 2 1 tDS tF S tP S 3 1 2 1 tDS tF S tP S

Precision

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if (h(Bi)<ω) return; B=Merge(B1,…,Bi)

SMT Encodings

MaxCube(φ) //return the hypercube in φ with maximum volume InfCube(φ,B) //relax in one direction if possible

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SMT Encodings

MaxCube(φ) //return the hypercube in φ with maximum volume InfCube(φ,B) //relax in one direction if possible

// sample arbitrary hyper-rectangle

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θ , ∀V ars(ϕ) · (( V

vi∈V ars(ϕ)

li ≤ vi ≤ ui) ⇒ ϕ)

SMT Encodings

MaxCube(φ) //return the hypercube in φ with maximum volume InfCube(φ,B) //relax in one direction if possible

// sample arbitrary hyper-rectangle // sample maximal hyper-cube

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θ , ∀V ars(ϕ) · (( V

vi∈V ars(ϕ)

li ≤ vi ≤ ui) ⇒ ϕ)

Optimize(θ ∧ ( V

vi∈V ars(ϕ)

(ui − li = h)), h)

SMT Encodings

MaxCube(φ) //return the hypercube in φ with maximum volume InfCube(φ,B) //relax in one direction if possible

// sample arbitrary hyper-rectangle // sample maximal hyper-cube

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θ , ∀V ars(ϕ) · (( V

vi∈V ars(ϕ)

li ≤ vi ≤ ui) ⇒ ϕ)

Optimize(θ ∧ ( V

vi∈V ars(ϕ)

(ui − li = h)), h)

Symbolic Optimization [POPL’14]

SMT Encodings

MaxCube(φ) //return the hypercube in φ with maximum volume InfCube(φ,B) //relax in one direction if possible

// sample arbitrary hyper-rectangle // sample maximal hyper-cube // relax lower bound // relax upper bound

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θ , ∀V ars(ϕ) · (( V

vi∈V ars(ϕ)

li ≤ vi ≤ ui) ⇒ ϕ) unSAT? (¬(B[li/∞] ⇒ ϕ))

unSAT? (¬(B[ui/∞] ⇒ ϕ))

Optimize(θ ∧ ( V

vi∈V ars(ϕ)

(ui − li = h)), h)

SMT Encodings

MaxCube(φ) //return the hypercube in φ with maximum volume InfCube(φ,B) //relax in one direction if possible

// sample arbitrary hyper-rectangle // sample maximal hyper-cube // relax lower bound // relax upper bound // heights of sampled hyper-cubes form a non-increasing sequence

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θ , ∀V ars(ϕ) · (( V

vi∈V ars(ϕ)

li ≤ vi ≤ ui) ⇒ ϕ) unSAT? (¬(B[li/∞] ⇒ ϕ))

unSAT? (¬(B[ui/∞] ⇒ ϕ))

Optimize(θ ∧ ( V

vi∈V ars(ϕ)

(ui − li = h)), h)

Checklist

What 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

10

Checklist

What 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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uLTR for component selection

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uLTR for component selection

publish

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uLTR for component selection

publish

LTR:(tFS<1⋀tDS≤3⋀tDS+tFS≤3)∨ …

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uLTR for component selection

publish

LTR:(tFS<1⋀tDS≤3⋀tDS+tFS≤3)∨ …

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uLTR for component selection

publish request r e t r i e v e

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Carminati et al., 2005 Rajendran et al., 2010 Al-Masri & Mahmoud, 2007

uLTR for component selection

publish request r e t r i e v e

tFS<1s finds the “best” match given localized constraints

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uLTR for component selection

publish request r e t r i e v e

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

uLTR for component selection

100 200 Size of uLTR model (|BS|) 10 20 30 40 50 60 70 80 90 100 Precision of uLTR model (%) QWS, Te = 10.8s Rand, Te ≈ 201.4s 1 4 7 10 Size of uLTR model (|BS|) 60 65 70 75 80 85 90 95 100 Precision of uLTR model (%) QWS, Te = 0.9s Rand, Te ≈ 10.8s 1 4 7 10 Size of uLTR model (|BS|) 60 65 70 75 80 85 90 95 100 Precision of uLTR model (%) 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

uLTR for component selection

100 200 Size of uLTR model (|BS|) 10 20 30 40 50 60 70 80 90 100 Precision of uLTR model (%) QWS, Te = 10.8s Rand, Te ≈ 201.4s 1 4 7 10 Size of uLTR model (|BS|) 60 65 70 75 80 85 90 95 100 Precision of uLTR model (%) QWS, Te = 0.9s Rand, Te ≈ 10.8s 1 4 7 10 Size of uLTR model (|BS|) 60 65 70 75 80 85 90 95 100 Precision of uLTR model (%) 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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uLTR for runtime adaptation and recovery

Must finish within 4s! response time

Monitor

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uLTR for runtime adaptation and recovery

Must finish within 4s! response time

Monitor

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uLTR for runtime adaptation and recovery

Must finish within 4s! response time repairing plan

Monitor

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uLTR for runtime adaptation and recovery

Must finish within 4s! response time repairing plan

Monitor

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¬(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)

uLTR for runtime adaptation and recovery

Must finish within 4s! response time repairing plan

Monitor

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¬(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)

uLTR for runtime adaptation and recovery

Must finish within 4s!

?

response time repairing plan

Monitor

Have to replace both DS and FS.

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uLTR for runtime adaptation and recovery

Must finish within 4s! tDS<1 tFS<∞ tPS<1 response time repairing plan

Monitor

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uLTR for runtime adaptation and recovery

Must finish within 4s! tDS<1 tFS<∞ tPS<1 response time repairing plan

Monitor

Replacing DS is enough!

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uLTR for runtime adaptation and recovery

Must finish within 4s! 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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uLTR for runtime adaptation and recovery

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 Work

Limited 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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Checklist

What 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

16

Checklist

What 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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Questions?

Thank you!

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References

Li, 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.

• f 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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