| 1. |
- Angelopoulos, Konstantinos, et al.
(författare)
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Model predictive control for software systems with CobRA
- 2016
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Ingår i: Proceedings - 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2016. - New York, NY, USA : ACM. - 9781450341875 ; , s. 35-46
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Konferensbidrag (refereegranskat)abstract
- Self-adaptive software systems monitor their operation and adapt when their requirements fail due to unexpected phenomena in their environment. This paper examines the case where the environment changes dynamically over time and the chosen adaptation has to take into account such changes. In control theory, this type of adaptation is known as Model Predictive Control and comes with a well-developed theory and myriads of successful applications. The paper focuses on modelling the dynamic relationship between requirements and possible adaptations. It then proposes a controller that exploits this relationship to optimize the satisfaction of requirements relative to a cost-function. This is accomplished through a model-based framework for designing self-adaptive software systems that can guarantee a certain level of requirements satisfaction over time, by dynamically composing adaptation strategies when necessary. The proposed framework is illustrated and evaluated through a simulation of the Meeting-Scheduling System exemplar.
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| 2. |
- Filieri, Antonio, et al.
(författare)
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Control Strategies for Self-Adaptive Software Systems
- 2017
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Ingår i: ACM Transactions on Autonomous and Adaptive Systems. - : Association for Computing Machinery (ACM). - 1556-4665 .- 1556-4703. ; 11:4
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Tidskriftsartikel (refereegranskat)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.
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| 3. |
- Filieri, Antonio, et al.
(författare)
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Software Engineering Meets Control Theory
- 2015
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Ingår i: 2015 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. - Piscataway, NJ, USA : IEEE Press. - 9780769555676 ; , s. 71-82
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Konferensbidrag (refereegranskat)abstract
- The software engineering community has proposed numerous approaches for making software self-adaptive. These approaches take inspiration from machine learning and control theory, constructing software that monitors and modifies its own behavior to meet goals. Control theory, in particular, has received considerable attention as it represents a general methodology for creating adaptive systems. Control-theoretical software implementations, however, tend to be ad hoc. While such solutions often work in practice, it is difficult to understand and reason about the desired properties and behavior of the resulting adaptive software and its controller. This paper discusses a control design process for software systems which enables automatic analysis and synthesis of a controller that is guaranteed to have the desired properties and behavior. The paper documents the process and illustrates its use in an example that walks through all necessary steps for self-adaptive controller synthesis.
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| 4. |
- Konstantinos, Angelopoulos, et al.
(författare)
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Adaptive predictive control for software systems
- 2015
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Ingår i: Proceedings of the 1st International Workshop on Control Theory for Software Engineering. - New York, NY, USA : ACM. - 9781450338141 ; , s. 17-21
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Konferensbidrag (refereegranskat)abstract
- Self-adaptive software systems are designed to support a number of alternative solutions for fulfilling their requirements. These define an adaptation space. During operation, a self-adaptive system monitors its performance and when it finds that its requirements are not fulfilled, searches its adaptation space to select a best adaptation. Two major problems need to be addressed during the selection process: (a) Handling environmental uncertainty in determining the impact of an adaptation; (b) maintain an optimal equilibrium among conflicting requirements. This position paper investigates the application of Adaptive Model Predictive Control ideas from Control Theory to design self-adaptive software that makes decisions by predicting its future performance for alternative adaptations and selects ones that minimize the cost of requirement failures using quantitative information. The technical details of our proposal are illustrated through the meeting-scheduler exemplar.
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| 5. |
- Konstantinos, Angelopoulos, et al.
(författare)
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Engineering Self-Adaptive Software Systems : From Requirements to Model Predictive Control
- 2018
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Ingår i: ACM Transactions on Autonomous and Adaptive Systems. - : Association for Computing Machinery (ACM). - 1556-4665 .- 1556-4703. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Self-adaptive software systems monitor their operation and adapt when their requirements fail due to unexpected phenomena in their environment. This article examines the case where the environment changes dynamically over time and the chosen adaptation has to take into account such changes. In control theory, this type of adaptation is known as Model Predictive Control and comes with a well-developed theory and myriad successful applications. The article focuses on modeling the dynamic relationship between requirements and possible adaptations. It then proposes a controller that exploits this relationship to optimize the satisfaction of requirements relative to a cost function. This is accomplished through a model-based framework for designing self-adaptive software systems that can guarantee a certain level of requirements satisfaction over time by dynamically composing adaptation strategies when necessary. The proposed framework is illustrated and evaluated through two simulated systems, namely, the Meeting-Scheduling exemplar and an E-Shop.
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| 6. |
- Moreno, Gabriel, et al.
(författare)
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Comparing Model-Based Predictive Approaches to Self-Adaptation : CobRA and PLA
- 2017
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Ingår i: 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems SEAMS 17. ; , s. 42-53
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Konferensbidrag (refereegranskat)abstract
- Modern software-intensive systems must often guarantee certain quality requirements under changing run-time conditions and high levels of uncertainty. Self-adaptation has proven to be an effective way to engineer systems that can address such challenges, but many of these approaches are purely reactive and adapt only after a failure has taken place. To overcome some of the limitations of reactive approaches (e.g., lagging behind environment changes and favoring short-term improvements), recent proactive self-adaptation mechanisms apply ideas from control theory, such as model predictive control (MPC), to improve adaptation. When selecting which MPC approach to apply, the improvement that can be obtained with each approach is scenario-dependent, and so guidance is needed to better understand how to choose an approach for a given situation. In this paper, we compare CobRA and PLA, two approaches that are inspired by MPC. CobRA is a requirements-based approach that applies control theory, whereas PLA is architecture-based and applies stochastic analysis. We compare the two approaches applied to RUBiS, a benchmark system for web and cloud application performance, discussing the required expertise needed to use both approaches and comparing their run-time performance with respect to different metrics.
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