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Control Strategies for Self-Adaptive Software Systems

Control Strategies for Self-Adaptive Software Systems The pervasiveness and growing complexity of software systems are challenging software engineering to design systems that can adapt their behavior to withstand unpredictable, uncertain, and continuously changing execution environments. Control theoretical adaptation mechanisms have received growing interest from the software engineering community in the last few years for their mathematical grounding, allowing formal guarantees on the behavior of the controlled systems. However, most of these mechanisms are tailored to specific applications and can hardly be generalized into broadly applicable software design and development processes. This article discusses a reference control design process, from goal identification to the verification and validation of the controlled system. A taxonomy of the main control strategies is introduced, analyzing their applicability to software adaptation for both functional and nonfunctional goals. A brief extract on how to deal with uncertainty complements the discussion. Finally, the article highlights a set of open challenges, both for the software engineering and the control theory research communities. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Autonomous and Adaptive Systems (TAAS) Association for Computing Machinery

Control Strategies for Self-Adaptive Software Systems

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 ACM
ISSN
1556-4665
eISSN
1556-4703
DOI
10.1145/3024188
Publisher site
See Article on Publisher Site

Abstract

The pervasiveness and growing complexity of software systems are challenging software engineering to design systems that can adapt their behavior to withstand unpredictable, uncertain, and continuously changing execution environments. Control theoretical adaptation mechanisms have received growing interest from the software engineering community in the last few years for their mathematical grounding, allowing formal guarantees on the behavior of the controlled systems. However, most of these mechanisms are tailored to specific applications and can hardly be generalized into broadly applicable software design and development processes. This article discusses a reference control design process, from goal identification to the verification and validation of the controlled system. A taxonomy of the main control strategies is introduced, analyzing their applicability to software adaptation for both functional and nonfunctional goals. A brief extract on how to deal with uncertainty complements the discussion. Finally, the article highlights a set of open challenges, both for the software engineering and the control theory research communities.

Journal

ACM Transactions on Autonomous and Adaptive Systems (TAAS) – Association for Computing Machinery

Published: Feb 3, 2017

Keywords: Self-adaptive software

References

  • Knowledge evolution in autonomic software product lines
    Abbas, N.; Andersson, J.; Weyns, D.
  • Introduction to control theory and its application to computing systems
    Abdelzaher, T.; Diao, Y.; Hellerstein, J.; Lu, C.; Zhu, X.
  • ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime
    Knauss, A.; Damian, D.; Franch, X.; Rook, A.; Mller, H. A.; Thomo, A.
  • Formal verification of hybrid systems
    Alur, R.
  • Discrete and continuous bang-bang and facial spaces or: Look for the extreme points
    Artstein, Z.
  • Event based control
    Åström, K.
  • Advanced PID Control
    Åström, K.; Hägglund, T.
  • Feedback Systems: An Introduction for Scientists and Engineers
    Åström, K.; Murray, R.
  • Adaptive Control
    Åström, K.; Wittenmark, B.
  • Green: A framework for supporting energy-conscious programming using controlled approximation
    Baek, W.; Chilimbi, T. M.
  • Controller synthesis for probabilistic systems
    Baier, C.; Grosser, M.; Leucker, M.; Bollig, B.; Ciesinski, F.
  • Model-based performance prediction in software development: A survey
    Balsamo, S.; Marco, A. D.; Inverardi, P.; Simeoni, M.
  • Toward open-world software: Issues and challenges
    Baresi, L.; Nitto, E. D.; Ghezzi, C.
  • Managing dynamic reconfiguration in component-based systems
    Batista, T.; Joolia, A.; Coulson, G.
  • Robust model predictive control: A survey
    Bemporad, A.; Morari, M.
  • A world full of surprises: Bayesian theory of surprise to quantify degrees of uncertainty
    Bencomo, N.; Belaggoun, A.
  • LTL model checking of interval markov chains
    Benedikt, M.; Lenhardt, R.; Worrell, J.
  • Linear controller design: Limits of performance via convex optimization
    Boyd, S.; Baratt, C.; Norman, S.
  • MORPH: A reference architecture for configuration and behaviour self-adaptation
    Braberman, V. A.; D’Ippolito, N.; Kramer, J.; Sykes, D.; Uchitel, S.
  • Controller synthesis and verification for markov decision processes with qualitative branching time objectives
    Brázdil, T.; Forejt, V.; Kučera, A.
  • Quantitative evaluation of model-driven performance analysis and simulation of component-based architectures
    Brosig, F.; Meier, P.; Becker, S.; Koziolek, A.; Koziolek, H.; Kounev, S.
  • Software Engineering for Experimental Robotics
    Brugali, D.
  • Engineering self-adaptive systems through feedback loops
    Brun, Y.; Serugendo, G. Marzo; Gacek, C.; Giese, H.; Kienle, H.; Litoiu, M.; Müller, H.; Pezzè, M.; Shaw, M.
  • Optimal software testing and adaptive software testing in the context of software cybernetics
    Cai, K.-Y.
  • Self-adaptive software needs quantitative verification at runtime
    Calinescu, R.; Ghezzi, C.; Kwiatkowska, M.; Mirandola, R.
  • Self-triggered networked control systems: An experimental case study
    Camacho, A.; Marti, P.; Velasco, M.; Lozoya, C.; Villa, R.; Fuertes, J. M.; Griful, E.
  • Model Predictive Control
    Camacho, E.; Alba, C.
  • Robustness evaluation of controllers in self-adaptive software systems
    Camara, J.; de Lemos, R.; Laranjeiro, N.; Ventura, R.; Vieira, M.
  • Reasoning about human participation in self-adaptive systems
    Cámara, J.; Moreno, G. A.; Garlan, D.
  • A sampling-and-discarding approach to chance-constrained optimization: Feasibility and optimality
    Campi, M. C.; Garatti, S.
  • The scenario approach for systems and control design
    Campi, M. C.; Garatti, S.; Prandini, M.
  • Measuring and synthesizing systems in probabilistic environments
    Chatterjee, K.; Henzinger, T. A.; Jobstmann, B.; Singh, R.
  • Simple stochastic parity games
    Chatterjee, K.; Jurdziński, M.; Henzinger, T. A.
  • Quantitative stochastic parity games
    Chatterjee, K.; Jurdziński, M.; Henzinger, T. A.
  • Model-checking ω-regular properties of interval markov chains
    Chatterjee, K.; Sen, K.; Henzinger, T. A.
  • On the complexity of model checking interval-valued discrete time Markov chains
    Chen, T.; Han, T.; Kwiatkowska, M.
  • Software engineering for self-adaptive systems
    Cheng, B.; Lemos, R.; Giese, H.; Inverardi, P.; Magee, J.; Andersson, J.; Becker, B.; Bencomo, N.; Brun, Y.; Cukic, B.; Serugendo, G. Marzo; Dustdar, S.; Finkelstein, A.; Gacek, C.; Geihs, K.; Grassi, V.; Karsai, G.; Kienle, H.; Kramer, J.; Litoiu, M.; S., ; Mirandola, R.; Müller, H.; Park, S.; Shaw, M.; Tichy, M.; Tivoli, M.; Weyns, D.; Whittle, J.
  • Handling uncertainty in autonomic systems
    Cheng, S.-W.; Garlan, D.
  • Certifying Solutions for Numerical Constraints
    Darulova, E.; Kuncak, V.
  • Sound compilation of reals
    Darulova, E.; Kuncak, V.
  • Towards architecture-based self-healing systems
    Dashofy, E. M.; van der Hoek, A.; Taylor, R. N.
  • PROPhESY: A PRObabilistic ParamEter SYnthesis Tool
    Dehnert, C.; Junges, S.; Jansen, N.; Corzilius, F.; Volk, M.; Bruintjes, H.; Katoen, J.-P.; Ábrahám, E.
  • Integrating discrete controller synthesis into a reactive programming language compiler
    Delaval, G.; Rutten, E.; Marchand, H.
  • Self-managing systems: A control theory foundation
    Diao, Y.; Hellerstein, J.; Parekh, S.; Griffith, R.; Kaiser, G.; Phung, D.
  • A control theory foundation for self-managing computing systems
    Diao, Y.; Hellerstein, J. L.; Parekh, S.; Griffith, R.; Kaiser, G. E.; Phung, D.
  • Modeling self-adaptive software systems with learning petri nets
    Ding, Z.; Zhou, Y.; Zhou, M.
  • Synthesising non-anomalous event-based controllers for liveness goals
    D’ippolito, N.; Braberman, V.; Piterman, N.; Uchitel, S.
  • Synthesis of live behaviour models
    D’Ippolito, N. R.; Braberman, V.; Piterman, N.; Uchitel, S.
  • Modern Control Systems
    Dorf, R.; Bishop, R.
  • Compiling a high-level language for GPUs: (Via language support for architectures and compilers)
    Dubach, C.; Cheng, P.; Rabbah, R.; Bacon, D. F.; Fink, S. J.
  • Taming heterogeneity-the ptolemy approach
    Eker, J.; Janneck, J. W.; Lee, E. A.; Liu, J.; Liu, X.; Ludvig, J.; Neuendorffer, S.; Sachs, S.; Xiong, Y.
  • Model evolution by run-time parameter adaptation
    Epifani, I.; Ghezzi, C.; Mirandola, R.; Tamburrelli, G.
  • Taming uncertainty in self-adaptive software
    Esfahani, N.; Kouroshfar, E.; Malek, S.
  • Uncertainty in self-adaptive software systems
    Esfahani, N.; Malek, S.
  • Competitive Markov Decision Processes
    Filar, J.; Vrieze, K.
  • Self-adaptive software meets control theory: A preliminary approach supporting reliability requirements
    Filieri, A.; Ghezzi, C.; Leva, A.; Maggio, M.
  • Run-time efficient probabilistic model checking
    Filieri, A.; Ghezzi, C.; Tamburrelli, G.
  • Automated design of self-adaptive software with control-theoretical formal guarantees
    Filieri, A.; Hoffmann, H.; Maggio, M.
  • Automated multi-objective control for self-adaptive software design
    Filieri, A.; Hoffmann, H.; Maggio, M.
  • Software engineering meets control theory
    Filieri, A.; Maggio, M.; Angelopoulos, K.; D’Ippolito, N.; Gerostathopoulos, I.; Hempel, A.; Hoffmann, H.; Jamshidi, P.; Kalyvianaki, E.; Klein, C.; Krikava, F.; Misailovic, S.; Papadopoulos, A.; Ray, S.; Sharifloo, A.; Shevtsov, S.; Ujma, M.; Vogel, T.
  • Refactoring: Improving the Design of Existing Code
    Fowler, M.; Beck, K.
  • HySAT: An efficient proof engine for bounded model checking of hybrid systems
    Franzle, M.; Herde, C.
  • Satisfiability modulo ODEs
    Gao, S.; Kong, S.; Clarke, E.
  • Rainbow: Architecture-based self-adaptation with reusable infrastructure
    Garlan, D.; Cheng, S.; Huang, A.; Schmerl, B. R.; Steenkiste, P.
  • Rainbow: Architecture-based self-adaptation with reusable infrastructure
    Garlan, D.; Cheng, S.-W.; Huang, A.-C.; Schmerl, B.; Steenkiste, P.
  • Living with uncertainty in the age of runtime models
    Giese, H.; Bencomo, N.; Pasquale, L.; Ramirez, A. J.; Inverardi, P.; Waetzoldt, S.; Clarke, S.
  • Hybrid Dynamical Systems: Modeling, Stability, and Robustness
    Goebel, R.; Sanfelice, R.; Teel, A.
  • Robust model predictive control: Reflections and opportunities
    Goodwin, G. C.; Kong, H.; Mirzaeva, G.; Seron, M. M.
  • Cuanta: Quantifying effects of shared on-chip resource interference for consolidated virtual machines
    Govindan, S.; Liu, J.; Kansal, A.; Sivasubramaniam, A.
  • Nonlinear Model Predictive Control
    Grüne, L.; Pannek, J.
  • PARAM: A model checker for parametric markov models
    Hahn, E. M.; Hermanns, H.; Wachter, B.; Zhang, L.
  • Genetic improvement for adaptive software engineering (keynote)
    Harman, M.; Jia, Y.; Langdon, W. B.; Petke, J.; Moghadam, I. H.; Yoo, S.; Wu, F.
  • Predictive control using an FPGA with application to aircraft control
    Hartley, E. N.; Jerez, J. L.; Suardi, A.; Maciejowski, J. M.; Kerrigan, E. C.; Constantinides, G. A.
  • An introduction to event-triggered and self-triggered control
    Heemels, W. P. M. H.; Johansson, K. H.; Tabuada, P.
  • Analysis of event-driven controllers for linear systems
    Heemels, W. P. M. H.; Sandee, J. H.; Van Den Bosch, P. P. J.
  • Engineering autonomic systems
    Hellerstein, J. L.
  • Feedback Control of Computing Systems
    Hellerstein, J. L.; Diao, Y.; Parekh, S.; Tilbury, D. M.
  • Applying control theory in the real world: Experience with building a controller for the
    Hellerstein, J. L.; Morrison, V.; Eilebrecht, E.
  • HyTech: A model checker for hybrid systems
    Henzinger, T.; Ho, P.-H.; Wong-Toi, H.
  • What’s decidable about hybrid automata? Journal of Computer and System Sciences 57, 1 (1998), 94--124
    Henzinger, T. A.; Kopke, P. W.; Puri, A.; Varaiya, P.
  • Functional Analysis and Time Optimal Control
    Hermes, H.; Lasalle, J. P.
  • CoAdapt: Predictable behavior for accuracy-aware applications running on power-aware systems
    Hoffmann, H.
  • Software engineering meets control theory
    Hoffmann, H.
  • Dynamic knobs for responsive power-aware computing
    Hoffmann, H.; Sidiroglou, S.; Carbin, M.; Misailovic, S.; Agarwal, A.; Rinard, M.
  • Software architecture for correct components assembly
    Inverardi, P.; Tivoli, M.
  • DSVerifier: A Bounded Model Checking Tool for Digital Systems
    Ismail, H. I.; Bessa, I. V.; Cordeiro, L. C.; de Lima Filho, E. B.; Filho, J. E. Chaves
  • Autonomic resource provisioning for cloud-based software
    Jamshidi, P.; Ahmad, A.; Pahl, C.
  • Fuzzy self-learning controllers for elasticity management in dynamic cloud architectures
    Jamshidi, P.; Sharifloo, A.; Pahl, C.; Arabnejad, H.; Metzger, A.; Estrada, G.
  • Foundations of Fuzzy Control: A Practical Approach
    Jantzen, J.
  • Architecture-based planning of software evolution
    Kang, S.; Garlan, D.
  • The vision of autonomic computing
    Kephart, J.; Chess, D.
  • Co-design of hardware and algorithms for real-time optimization
    Kerrigan, E. C.
  • Nonlinear Control
    Khalil, H.
  • Brownout: Building more robust cloud applications
    Klein, C.; Maggio, M.; Årzén, K.-E.; Hernández-Rodriguez, F.
  • When polyhedral transformations meet SIMD code generation
    Kong, M.; Veras, R.; Stock, K.; Franchetti, F.; Pouchet, L.-N.; Sadayappan, P.
  • Ensuring code safety without runtime checks for real-time control systems
    Kowshik, S.; Dhurjati, D.; Adve, V.
  • Self-managed systems: An architectural challenge
    Kramer, J.; Magee, J.
  • ACTRESS: Domain-specific modeling of self-adaptive software architectures
    Krikava, F.; Collet, P.; France, R.; others,
  • Stochastic Systems: Estimation, Identification and Adaptive Control
    Kumar, P. R.; Varaiya, P.
  • Automated verification and strategy synthesis for probabilistic systems
    Kwiatkowska, M.; Parker, D.
  • A survey of modeling and control of hybrid systems
    Labinaz, G.; Bayoumi, M. M.; Rudie, K.
  • Autonomic provisioning with self-adaptive neural fuzzy control for percentile-based delay guarantee
    Lama, P.; Zhou, X.
  • Knowledge-based control systems
    Lambert, S.
  • Uncertainty, risk, and information value in software requirements and architecture
    Letier, E.; Stefan, D.; Barr, E. T.
  • Control-Based Operating System Design
    Leva, A.; Maggio, M.; Papadopoulos, A.; Terraneo, F.
  • Tuning of event-based industrial controllers with simple stability guarantees
    Leva, A.; Papadopoulos, A.
  • The Control Systems Handbook: Control System Advanced Methods, (2nd ed)
    Levine, W.
  • Switching in Systems and Control
    Liberzon, D.
  • Actor-oriented control system design: A responsible framework perspective
    Liu, J.; Eker, J.; Janneck, J.
  • System Identification: Theory for the User (2nd ed
    Ljung, L.
  • Handbook of Hybrid Systems Control: Theory, Tools, Applications
    Lunze, J.; Lamnabhi-Lagarrigue, F.
  • A state-feedback approach to event-based control
    Lunze, J.; Lehmann, D.
  • Predictive Control: With Constraints
    Maciejowski, J.
  • Comparison of decision making strategies for self-optimization in autonomic computing systems
    Maggio, M.; Hoffmann, H.; Papadopoulos, A.; Panerati, J.; Santambrogio, M.; Agarwal, A.; Leva, A.
  • Automated test suite generation for time-continuous simulink models
    Matinnejad, R.; Nejati, S.; Briand, L. C.; Bruckmann, T.
  • Perceptual Computing: Aiding People in Making Subjective Judgments
    Mendel, J.; Wu, D.
  • Robust Process Control
    Morari, M.; Zafiriou, E.
  • Visibility of control in adaptive systems
    Müller, H.; Pezzè, M.; Shaw, M.
  • Automated Synthesis of Distributed Controllers
    Muscholl, A.
  • Spiral in scala: Towards the systematic construction of generators for performance libraries
    Ofenbeck, G.; Rompf, T.; Stojanov, A.; Odersky, M.; Püschel, M.
  • An architecture-based approach to self-adaptive software
    Oreizy, P.; Gorlick, M. M.; Taylor, R. N.; Heimbigner, D.; Johnson, G.; Medvidovic, N.; Quilici, A.; Rosenblum, D. S.; Wolf, A. L.
  • A dynamic modelling framework for control-based computing system design
    Papadopoulos, A. V.; Maggio, M.; Terraneo, F.; Leva, A.
  • PEAS: A performance evaluation framework for auto-scaling strategies in cloud applications
    Papadopoulos, A. V.; Ali-Eldin, A.; Årzén, K.-E.; Tordsson, J.; Elmroth, E.
  • Feedback Control Techniques for Performance Management of Computing Systems
    Parekh, S.
  • Using control theory to achieve service level objectives in performance management
    Parekh, S.; Gandhi, N.; Hellerstein, J.; Tilbury, D.; Jayram, T.; Bigus, J.
  • A systematic survey on the design of self-adaptive software systems using control engineering approaches
    Patikirikorala, T.; Colman, A.; Han, J.; Wang, L.
  • Synthesis of reactive(1) designs
    Piterman, N.; Pnueli, A.; Sa’ar, Y.
  • The temporal logic of programs
    Pnueli, A.
  • On the synthesis of a reactive module
    Pnueli, A.; Rosner, R.
  • Polynomial-time verification of PCTL properties of MDPs with convex uncertainties
    Puggelli, A.; Li, W.; Sangiovanni-Vincentelli, A. L.; Seshia, S. A.
  • Markov Decision Processes: Discrete Stochastic Dynamic Programming
    Puterman, M. L.
  • Rationalism with a dose of empiricism: Case-based reasoning for requirements-driven self-adaptation
    Qian, W.; Peng, X.; Chen, B.; Mylopoulos, J.; Wang, H.; Zhao, W.
  • Cyber-physical systems: The next computing revolution
    Rajkumar, R. R.; Lee, I.; Sha, L.; Stankovic, J.
  • The control of discrete event systems
    Ramadge, P. J. G.; Wonham, W. M.
  • Relaxing Claims: Coping with Uncertainty While Evaluating Assumptions at Run Time
    Ramirez, A. J.; Cheng, B. H. C.; Bencomo, N.; Sawyer, P.
  • A taxonomy of uncertainty for dynamically adaptive systems
    Ramirez, A. J.; Jensen, A. C.; Cheng, B. H. C.
  • DynaQoS: Model-free self-tuning fuzzy control of virtualized resources for QoS provisioning
    Rao, J.; Wei, Y.; Gong, J.; Xu, C.-Z.
  • Lightweight modular staging: A pragmatic approach to runtime code generation and compiled DSLs
    Rompf, T.; Odersky, M.
  • Self-adaptive software: Landscape and research challenges
    Salehie, M.; Tahvildari, L.
  • Paraprox: Pattern-based approximation for data parallel applications
    Samadi, M.; Jamshidi, D. A.; Lee, J.; Mahlke, S.
  • Pattern-based control systems engineering
    Sanz, R.; Zalewski, J.
  • Architectures for distributed and hierarchical model predictive control--a review
    Scattolini, R.
  • Multivariable Feedback Control: Analysis and Design
    Skogestad, S.; Postlethwaite, I.
  • System identification for adaptive software systems: A requirements engineering perspective
    Souza, V. E. S.; Lapouchnian, A.; Mylopoulos, J.
  • Respect the unstable
    Stein, G.
  • LPV model and its application in web server performance control
    Sun, Q.; Dai, G.; Pan, W.
  • Event-triggered real-time scheduling of stabilizing control tasks
    Tabuada, P.
  • Reinforcement learning in autonomic computing: A manifesto and case studies
    Tesauro, G.
  • A fluid model for layered queueing networks
    Tribastone, M.
  • A framework for evaluating quality-driven self-adaptive software systems
    Villegas, N. M.; Müller, H. A.; Tamura, G.; Duchien, L.; Casallas, R.
  • Model-driven engineering of self-adaptive software with EUREMA
    Vogel, T.; Giese, H.
  • Software engineering in industrial automation: State-of-the-art review
    Vyatkin, V.
  • QoS-driven cloud resource management through fuzzy model predictive control
    Wang, L.; Xu, J.; Zhao, M.
  • A simple approximation for modeling nonstationary queues
    Wang, W.-P.; Tipper, D.; Banerjee, S.
  • Markov analysis of software specifications
    Whittaker, J. A.; Poore, J. H.
  • Computer Control: An Overview
    Wittenmark, B.; Åström, K.; Årzén, K.-E.
  • On the use of fuzzy modeling in virtualized data center management
    Xu, J.; Zhao, M.; Fortes, J.; Carpenter, R.; Yousif, M.
  • An Introduction to Fuzzy Logic Applications in Intelligent Systems
    Yager, R.; Zadeh, L.
  • Networked control system: A brief survey
    Yang, T. C.
  • Formal modelling, analysis and verification of hybrid systems
    Zhan, N.; Wang, S.; Zhao, H.
  • Understanding Uncertainty in Cyber-Physical Systems: A Conceptual Model
    Zhang, M.; Selic, B.; Ali, S.; Yue, T.; Okariz, O.; Norgren, R.
  • Robust and Optimal Control
    Zhou, K.; Doyle, J. C.; Glover, K.
  • What does control theory bring to systems research? SIGOPS Opererating System Review 43 (2009), 62--69
    Zhu, X.; Uysal, M.; Wang, Z.; Singhal, S.; Merchant, A.; Padala, P.; Shin, K.
  • Bayesian statistical model checking with application to simulink/stateflow verification
    Zuliani, P.; Platzer, A.; Clarke, E. M.