Realizing self-x properties by an automated planner

Schmitt, Julia; Roth, Michael; Kiefhaber, Rolf; Kluge, Florian; Ungerer, Theo

doi:10.1145/1998582.1998620

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…Currently, we do not distinguish the execution time and latency of rules (i.e., the time until an adaptation shows an effect in the system after its execution) as we assume immediate adaptation (repair) effects. To explicitly consider latency 4 Examples of such work are: Camara and de Lemos [2012]; Ehlers et al [2011]; Haupt [2012]; Neti and Mueller [2007]; Qun et al [2005]; Salehie and Tahvildari [2006]; Schmitt et al [2011]. 5 Such approaches either use observed and manually adjusted failure traces (e.g., Garlan and Schmerl [2002]; Haesevoets et al [2009]; Ippoliti and Zhou [2012]), probabilistic or simple random failure traces (e.g., Anaya et al [2014]; Chan and Bishop [2009]; Piel et al [2011]), or deterministic failure traces (e.g., Angelopoulos et al [2014]; Carzaniga et al [2008]; Casanova et al [2013]; Di Marco et al [2013]; Griffith et al [2009]; Hassan et al [2015]; Magalhaes and Silva [2015]; Perino [2013]).…”

Section: Related Workmentioning

confidence: 99%

“…Examples of approaches that use simulation to evaluate self-healing systems are:Anaya et al [2014];Angelopoulos et al [2014];Camara and de Lemos [2012];Carzaniga et al [2008];Casanova et al [2013];Chan and Bishop [2009]; DiMarco et al [2013];Ehlers et al [2011];Garlan and Schmerl [2002];Griffith et al [2009];Haesevoets et al [2009];Hassan et al [2015];Haupt [2012];Ippoliti and Zhou [2012];Magalhaes and Silva [2015];Neti and Mueller [2007];Perino [2013];Piel et al [2011];Qun et al [2005];Salehie and Tahvildari [2006];Schmitt et al [2011].ACM Trans. Autonom.…”

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confidence: 99%

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Improving Scalability and Reward of Utility-Driven Self-Healing for Large Dynamic Architectures

Ghahremani

Giese

Vogel

2019

ACM Trans. Auton. Adapt. Syst.

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Self-adaptation can be realized in various ways. Rule-based approaches prescribe the adaptation to be executed if the system or environment satisfies certain conditions. They result in scalable solutions but often with merely satisfying adaptation decisions. In contrast, utility-driven approaches determine optimal decisions by using an often costly optimization, which typically does not scale for large problems. We propose a rule-based and utility-driven adaptation scheme that achieves the benefits of both directions such that the adaptation decisions are optimal, whereas the computation scales by avoiding an expensive optimization. We use this adaptation scheme for architecture-based self-healing of large software systems. For this purpose, we define the utility for large dynamic architectures of such systems based on patterns that define issues the self-healing must address. Moreover, we use pattern-based adaptation rules to resolve these issues. Using a pattern-based scheme to define the utility and adaptation rules allows us to compute the impact of each rule application on the overall utility and to realize an incremental and efficient utility-driven self-healing. In addition to formally analyzing the computational effort and optimality of the proposed scheme, we thoroughly demonstrate its scalability and optimality in terms of reward in comparative experiments with a static rule-based approach as a baseline and a utility-driven approach using a constraint solver. These experiments are based on different failure profiles derived from real-world failure logs. We also investigate the impact of different failure profile characteristics on the scalability and reward to evaluate the robustness of the different approaches. S. Ghahremani et al.approaches Kephart and Das 2007] combine both phases. Adaptation is executed for specific events and under specific conditions by adaptation rules. In such approaches, events trigger the rules that subsequently check their conditions. If the conditions are fulfilled, the actions of the rules are applied and result in the envisioned changes. Thus, the applicable rules are identified (matched) and executed to adapt the system configuration at runtime. The main strengths of such approaches are the readability, elegance, and the efficient processing of the rules. The drawbacks are the fact that the adaptation decisions are often only satisfying and the limited expressiveness of rules since rules typically just relate events to actions [Fleurey and Solberg 2009] without defining and performing any further computation for analysis and planning (e.g., to identify optimal actions). On the other hand, utility-driven approaches [Esfahani et al. 2013;Kephart and Walsh 2004] determine optimal adaptation decisions by using optimization techniques for planning that are guided by a utility function. A utility function determines how valuable each possible system configuration is, and the optimization aims at identifying optimal configurations. However, the optimization usually prevents the ...

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Improving Scalability and Reward of Utility-Driven Self-Healing for Large Dynamic Architectures

Ghahremani

Giese

Vogel

2019

ACM Trans. Auton. Adapt. Syst.

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“…Self-adaptation issues in embedded software have been investigated across different areas of software engineering, such as requirements engineering [1][2][3][4], software architecture [5][6][7][8][9][10], middleware architectures [11][12][13][14], component-based development [15][16][17], model-driven development [18][19][20][21] and goal-driven models [22][23][24][25]. The majority of these works have been isolated initiatives.…”

Section: Self-adaptation In Embedded Softwarementioning

confidence: 99%

Engineering Self-Adaptive Capabilities in Wireless Sensors Based on Control Algorithms and Model-Based Design Methodologies

Matamoros¹,

Pruteanu²,

Haber³

2022

Preprint

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The main objective of this work is the design and implementation of self-adaptive capabilities in wireless sensors by applying control engineering and model-based design methodologies. It has been addressed the problem related to the changes in the flow of data packets through the network connection and the excess energy consumption that this causes in these devices. To design the solution, a systemic characterization of the scheduling and execution process of embedded tasks on the device has been carried out. This means defining cause-effect relationships in the system and its modelling theoretically and/or experimentally. In turn, these models facilitate the design of control strategies to improve the dynamic behavior of the system. As a solution, a self-adaptation strategy based on feedforward control algorithm has been designed and developed, which has been applied to improve the dynamic behavior and resource consumption. The developed solution has been satisfactorily evaluated experimentally.

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“…Figure 4 summarizes the design choices of inputs for the evaluation of SHS in the reviewed studies. Our SLR revealed eight studies (22%) that do not report on the employed trace for the occurrences of failures in their evaluations of SHS [7,[22][23][24][25][26][27][28]. Therefore, we only have data for 78% of the investigated papers.…”

Section: Rq2mentioning

confidence: 99%

Evaluation of Self-Healing Systems: An Analysis of the State-of-the-Art and Required Improvements

Ghahremani

Giese

2020

Computers

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Evaluating the performance of self-adaptive systems is challenging due to their interactions with often highly dynamic environments. In the specific case of self-healing systems, the performance evaluations of self-healing approaches and their parameter tuning rely on the considered characteristics of failure occurrences and the resulting interactions with the self-healing actions. In this paper, we first study the state-of-the-art for evaluating the performances of self-healing systems by means of a systematic literature review. We provide a classification of different input types for such systems and analyse the limitations of each input type. A main finding is that the employed inputs are often not sophisticated regarding the considered characteristics for failure occurrences. To further study the impact of the identified limitations, we present experiments demonstrating that wrong assumptions regarding the characteristics of the failure occurrences can result in large performance prediction errors, disadvantageous design-time decisions concerning the selection of alternative self-healing approaches, and disadvantageous deployment-time decisions concerning parameter tuning. Furthermore, the experiments indicate that employing multiple alternative input characteristics can help with reducing the risk of premature disadvantageous design-time decisions.

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Realizing self-x properties by an automated planner

Cited by 8 publications

References 5 publications

Improving Scalability and Reward of Utility-Driven Self-Healing for Large Dynamic Architectures

Improving Scalability and Reward of Utility-Driven Self-Healing for Large Dynamic Architectures

Engineering Self-Adaptive Capabilities in Wireless Sensors Based on Control Algorithms and Model-Based Design Methodologies

Evaluation of Self-Healing Systems: An Analysis of the State-of-the-Art and Required Improvements

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