A Systematic Model-Based Approach for Feedback Control Trade-Off

Evaluation in Autonomic Systems

Sandro S. Andrade e Raimundo J. de A. MacêdoDistributed Systems Laboratory (LaSiD)

Department of Computer ScienceFederal University of Bahia

{sandros, macedo}@ufba.br

XXXII SBRC – IV Workshop de Sistemas Distribuídos Autonômicos – WoSiDA 2014

   

Context and Motivation

● Nearly 13 years have elapsed in Autonomic Systems research field

● Different research communities Different →underpinnings for self-management:– Reflexive middleware platforms– Graph grammars– Intelligent agents– Policy-based– Bio-inspired self-organizing structures

   

Context and Motivation

● Consequences:– Lack of organized design knowledge for routine use– False intuition and design bias– Sub-optimal architectures for self-management

● A perspective from Software Engineering● Research questions:

– To which extent may autonomic systems design knowledge be systematically represented for routine use ?

– How to support well-informed decision making regarding quality attribute trade-offs between alternative architectures ?

   

Our Approach (DuSE)

● We combine the use of ...– Metamodeling and Domain-Specific Languages (DSL)– Structured Architecture Design Spaces– Architecture Quality Metrics– Multi-Objective Optimization

● … in order to …– Enable a more disciplined and automated “handbook”

of autonomic systems design– Provide a solid basis for choosing between

architectures which exhibit different quality attributes

   

Our Approach (DuSE) - overview

   

Our Approach (DuSE) - examples

   

Our Approach (DuSE) – design space

   

SA:DuSE – design dimensionsVP11: ProportionalVP12: Proportional-IntegralVP13: Proportional-Integral-DerivativeVP14: Static State FeedbackVP15: Precompensated Static State FeedbackVP16: Dynamic State Feedback

DD1: Control Law

VP31: Fixed Gain (no adaptation)VP32: Gain SchedulingVP33: Model Identification Adaptation Control

DD3: Control Adaptation

VP21: Chien-Hrones-Reswick, 0 OS, Dist. RejectionVP22: Chien-Hrones-Reswick, 0 OS, Ref. TrackingVP23: Chien-Hrones-Reswick, 20 OS, Dist. RejectionVP24: Chien-Hrones-Reswick, 20 OS, Ref. TrackingVP25: Ziegler-NicholsVP26: Cohen-CoonVP27: Linear Quadratic Regulator

DD2: Tuning Approach

VP41: Global ControlVP42: Local Control + Shared ReferenceVP43: Local Control + Shared Error

DD4: MAPE Deployment

   

SA:DuSE – quality metrics

M2: Average

Settling Time

ME2=allOwnedElements ()→ selectAsType(QParametricController )→sum(stime())

allOwnedElements()→selectAsType(QParametricController )→size ()

; where QParametricController : : stime()=−4

log(maxi∣p i∣);and max i∣pi∣is themagnitude of thelargest closed−loop pole

M1: Control

OverheadME1=

allOwnedElements ()→ selectAsType (QController )→ collect(overhead ())→sum ()

allOwnedElements ( )→selectAsType (QController )→size()

;QController : : overhead( )increasingly penalizes VP32, VP33, VP41and VP43

M4: Control

RobustnessME4=

allOwnedElements ()→ selectAsType(QController )→collect (robustness())→sum ()

allOwnedElements()→selectAsType(QController )→size()

;QController : :robustness()increasingly penalizesVP31 andVP32

M3: Average

Maximum Overshoot

ME3=allOwnedElements()→selectAsType(QParametricController)→sum (maxOS ())

allOwnedElements()→selectAsType(QParametricController )→size ()

; where QParametricController : : maxOS ()={0 ;real dominant pole p1≥0

∣p1∣;real dominant pole p1<0

r π/∣θ∣;dominant poles p1, p2=r.e±j. θ}

   

Our Approach (DuSE) - optimization

● Even small input models generate huge design spaces:– 3 controllable components 54,010,152 candidates→– 4 controllable components 20,415,837,000 →

candidates● Multi-objective Optimization:

– NSGA-II evolutionary backend– The output is a set of Pareto-optimal architectures:

● They differ only on which quality metric is favored

   

Our Approach (DuSE) - optimization

   

Evaluation – tool support

   

Evaluation

● Evaluation dimensions:– Is autonomic systems design indeed a multi-objective

problem ?– To which extent the quality of Pareto-optimal

architectures are indeed observed in real prototypes ?– Do search-based approaches improve the design of

autonomic systems ?

   

Evaluation

● Evaluation dimensions:– Is autonomic systems design indeed a multi-objective

problem ?– To which extent the quality of Pareto-optimal

architectures are indeed observed in real prototypes ?– Do search-based approaches improve the design of

autonomic systems ?

Multi-objective quality indicators:

hypervolume and generational distance

#=9.7GD=0.12

   

Evaluation

● Evaluation dimensions:– Is autonomic systems design indeed a multi-objective

problem ?– To which extent the quality of Pareto-optimal

architectures are indeed observed in real prototypes ?– Do search-based approaches improve the design of

autonomic systems ?

Multi-objective quality indicators:

hypervolume and generational distance

#=9.7GD=0.12

Real prototypes of feedback controllers in distributed MapReduce

architectures21 machines Hadoop 2.3

clusterSettling Time error = 2%

   

Evaluation

● Evaluation dimensions:– Is autonomic systems design indeed a multi-objective

problem ?– To which extent the quality of Pareto-optimal

architectures are indeed observed in real prototypes ?– Do search-based approaches improve the design of

autonomic systems ?

Multi-objective quality indicators:

hypervolume and generational distance

#=9.7GD=0.12

Real prototypes of feedback controllers in distributed MapReduce

architectures21 machines Hadoop 2.3

clusterSettling Time error = 2%

A controlled experiment(24 students)

H01: GD(ra)=GD(ia) ☑H02: AC(ra)=AC(ia) ☑H03: QG(ra)=QG(ia)

   

Conclusion

● Limitations:● Only feedback control concerns up to now● We assume an initial annotated architecture model is

available● No guaranteed optimality (local Pareto-fronts)

● Contributions:● A domain-independent infrastructure for design space

specification● First search-based approach to self-managed systems

architectural design● 2nd controlled experiment involving self-managed systems● A design space exploration tool

   

Obrigado !

Sandro S. Andrade e Raimundo J. de A. MacêdoDistributed Systems Laboratory (LaSiD)

Department of Computer ScienceFederal University of Bahia

{sandros, macedo}@ufba.br