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Multidimensional Robustness Optimization of Embedded Systems

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Arne Hamann, Steffen Stein, IDA, TU Braunschweig. System ... Specification changes, late feature requests, product variants, software updates, bug-fixes ... – PowerPoint PPT presentation

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Title: Multidimensional Robustness Optimization of Embedded Systems


1
Multi-dimensional Robustness Optimization of
Embedded Systems Online Performance
Verification
Arne Hamann Steffen Stein Rolf Ernst
2
Part IMulti-dimensional Robustness Optimization
of Embedded Systems
Arne Hamann Rolf Ernst
3
Outline
  • System property variations
  • Sensitivity Analysis
  • Stochastic Multi-dimensional Sensitivity Analysis
  • Robustness Metrics
  • Hypervolume calculation
  • Minimum Guaranteed Robustness (MGR)
  • Maximum Possible Robustness (MPR)
  • Experiments

4
System Property Variations
  • Why do system property variations occur?
  • Specification changes, late feature requests,
    product variants, software updates, bug-fixes
  • Robustness to property variations
  • decreases design risk, and increases system
    maintainability and extensibility
  • Property variations can have severe unintuitive
    effects on system performance
  • Sensitivity analysis achieve robustness without
    on-line parameter adaptation

5
Problem Formulation
  • Find fixed parameter configuration that
  • maximizes system robustness w.r.t. changes of
    several properties
  • Robustness the system can sustain property
    variations without severe performance degradation
  • Not included dynamic parameter adaptations
    (ongoing work submitted to EMSOFT 2007)

6
Stochastic Sensitivity Analysis (1)
  • Problem of exact sensitivity analysis approaches
    computational effort grows exponentially with
    number of considered dimensions
  • Solution scalable stochastic analysis able to
    quickly bound system sensitivity

7
Stochastic Sensitivity Analysis (2)
  • Sensitivity analysis formulated as
    multi-objective optimization problem
  • ?Pareto-front of optimization task corresponds to
    sought-after sensitivity front
  • Use multi-criteria evolutionary algorithms to
    approximate sensitivity front
  • E.g. SPEA2 (ETH Zurich) diversified sensitivity
    front approximation through Pareto-dominance
    based selection and density approximation

8
Creation of the Initial Population
  • Creates a certain number of points representing a
    first approximation of sensitivity front
  • Uses 1-dim sensitivity analysis
  • to bound the search space in each dimension
    (bounding hypercube)
  • to generate points representing the extrema of
    the sought-after sensitivity front
  • Randomly place the rest of the initial points in
    bounding hypercube

9
Initial Population - Example
10,85
Property 2
6,5
26,2
10
Property 1
10
Bounding the Search Space (1)
  • Idea bound search space containing the
    sought-after sensitivity front
  • Bounding working Pareto-front F n
  • evaluated Pareto-optimal working points
  • Bounding non-working Pareto-front F nw
  • evaluated Pareto-optimal non-working points
  • Bounding Pareto-fronts can be used to derive
    multi-dim. robustness metrics (later)

11
Bounding the Search Space (2)
  • Space between bounding Pareto-fronts is called
    relevant region
  • Variation operators use algorithm ensuring that
    generated offsprings (points) are situated in the
    relevant region
  • Below bounding non-working Pareto-front
  • Above bounding working Pareto-front
  • ? Efficiently focuses exploration effort

12
Bounding the Search Space (3)
10,85
Property 2
6,5
26,2
10
Property 1
13
Front Convergence Mutate (1)
  • Heuristic operator adapted to optimization
    problem
  • Strategy
  • Determine X closest points on opposite
    Pareto-front
  • Choose randomly one of these points
  • Place offspring point randomly on straight line
    connecting the parent point and the chosen random
    point
  • Increases convergence speed of the bounding
    Pareto-fronts

14
Front Convergence Mutate (2)
10,85
Property 2
6,5
26,2
10
Property 1
15
Front Convergence Mutate (3)
10,85
Property 2
6,5
26,2
10
Property 1
16
Hypervolume Calculation
  • Hypervolume as basis of the proposed robustness
    metrics
  • Hypervolume is defined in a given hypercube and
    associated to a point set
  • Two different notions of hypervolume
  • inner hypervolume Volume of space
    Pareto-dominated by the given points inside the
    given hypercube
  • outer hypervolume Volume of space
    Pareto-dominated by all points not
    Pareto-dominating any of the given points

17
Hypervolume Calculation (2)
  • 2D-case
  • inner hypervolume lower step function
  • outer hypervolume upper step function

(15,18)
Bounding Box 15,28x6,18
(18,16)
(20,12)
(26,10)
(28,6)
18
Robustness Metrics
  • Given a set of properties
  • use stochastic sensitivity analysis to derive
    upper and lower robustness bounds
  • Minimum Guaranteed Robustness (MGR)
  • Defined as inner hypervolume of the bounding
    working Pareto-front F w
  • Maximum Possible Robustness (MPR)
  • Defined as outer hypervolume of the bounding
    non-working Pareto-front F nw

19
Robustness Metrics (2)
10,85
Property 2
MPR
MGR
6,5
26,2
10
Property 1
Obviously MGR lt Real Robustness lt MPR
20
Robustness Exploration
  • Idea Pareto-optimize MGR and MPR
  • Advantages
  • Stochastic sensitivity analysis is scalable
  • ?Little computational effort necessary to
    reasonably bound robustness potential of given
    configuration
  • In-depth analysis can be performed once
    interesting configurations are identified
    (i.e. high MGR or high MPR)
  • ?Perfectly suited for robustness optimization

21
Example System
  • Distributed embedded system
  • 4 computational resources
  • connected via CAN bus
  • 3 constrained applications
  • Sens?Act
  • Sin?Sout
  • Cam?Vout

22
Approximation Quality (1)
  • Approximation after 100 evaluations (20 sec)
  • MGR 2447
  • MPR 2937
  • Approximation after 200 evaluations (40 sec)
  • MGR 2580
  • MPR 2813

23
Approximation Quality (2)
  • Approximation after 300 evaluations (60 sec)
  • MGR 2632
  • MPR 2777
  • Result using exact sensitivity analysis (85 sec)
  • MGR 2585
  • MPR 2826

24
3D - Robustness Maximization
Original configuration
Optimized configuration
25
Integration of New Functionality
  • Integration of a fourth application with lowest
    priorities
  • What combinations WCET T9 and WCCT C6 are
    feasible?
  • Is there optimization potential?
  • Idea initially assume WCET T9 and WCCT C6 equal
    zero

Sink
C6
T9
Sens 2
26
Integration of New Functionality (2)
WCCT C6
WCET T9
  • Areas below the curves represent feasible systems

27
Part IIOnline Performance Verification
Steffen Stein Rolf Ernst
28
Outline
  • Motivation
  • Framework Architecture
  • In Detail Global Analysis Layer
  • System Setup
  • Approach to Analysis Control
  • Experimental Results

29
Future Challenges
  • In-Field updates
  • Run-time Reconfigurations
  • 90 of Innovation in Software
  • Networked Systems
  • Not Manageable
  • at Design-Time!

30
Approach
  • Generally Speaking
  • Make Systems clever enough to handle Integration
    Problem themselves
  • Here Timing Properties
  • ToDo
  • Gather performance Data during runtime
  • Evaluate/ Optimise online
  • Feed Results back into running Systems
  • Result Evolving Systems

31
Architecture Organic Computing
Goals/ Design Rules
selects observation model
  • Single Instance
  • Multiple Instances
  • Multiple collaborating Instances
  • Layered approach

observer
controller
reports
observes
controls
System under Observationand Control (SuOC)
Source Towards a generic observer/controller
architecture for Organic Computing, U. Richter,
M. Mnif, J. Branke, C. Müller-Schloer, H.
Schmeck, INFORMATIK 2006 -- Informatik für
Menschen
32
Control Framework
Global Analysis Layer
Control Plane
Global Controller Layer
Global Observer Layer
Self-Organisation
Self-Organisation
Observer
Controller
Observer
Controller
Analysis Engine
Observer
Controller
Local Layer
Data Exchange
Use resources
Gather data
Adjust settings
Heterogeneous Networked Embedded System (SuOC)
33
Distributed Setup
uC
PPC
T1
S2
T2
T3
T6
S3
T5
CAN
T4
S1
T8
T7
S4
T0
T9
DSP
ARM
Real System
Global Model
34
Analysis Control
Distributed Analysis Control
Analysis Control
T1
T2
T1
T1
T2
T1
Network Tunnel
T3
T4
T6
T3
T4
T6
Do for all not up-to-date Resources Analyse en
d Until all Resources are up to date
While (true) if Resource invalidated analyse
Resource end end
35
Performance of trivial Approach
analysis runs (resource level)
System size ( tasks)
36
Problem
Exponential increase in number of necesary
Analysis runs
T1
T2
S1
T3
T6
T4
T5
S2
T7
T8
S3
T9
Solution Caching
37
Performance with caching
analysis runs (resource level)
System size ( tasks)
38
Conclusion
  • Distributed Performance Analysis implemented
  • Suitable as evaluator for online performance
    control / optimization
  • Future Work From System observations to
    analysable Model.
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