Synthesizing self-adaptive connectors meeting functional and performance concerns

Marco, Antinisca Di; Inverardi, Paola; Spalazzese, Romina

doi:10.1109/seams.2013.6595500

Cited by 18 publications

(14 citation statements)

References 23 publications

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“…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]). similarly to , the latency of each repair rule needs to be estimated and then added to the execution time of the rule.…”

Section: Related Workmentioning

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 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%

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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“…Existing approaches to architectural adaptation (e.g. [2], [3], [4], [5], [6], [7], [8] incorporate elements from two key areas to enable runtime adaptation: Dynamic reconfiguration [9], [10], [11], [12], [13], [14], [15], [1], [1] and discrete-event control theory [14], [16], [17], [18], [19], [20], [21], [22], [23], [24]. The first, key for adapting the system configuration, studies how to change component structure and operational parameters ensuring that on-going operation is not disrupted and/or nonfunctional aspects of the system are improved.…”

Section: Introductionmentioning

confidence: 99%

MORPH: a reference architecture for configuration and behaviour self-adaptation

Braberman

D’Ippolito

Kramer

et al. 2015

Proceedings of the 1st International Workshop on Control Theory for Software Engineering

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Abstract-An architectural approach to self-adaptive systems involves runtime change of system configuration (i.e., the system's components, their bindings and operational parameters) and behaviour update (i.e., component orchestration). Thus, dynamic reconfiguration and discrete event control theory are at the heart of architectural adaptation. Although controlling configuration and behaviour at runtime has been discussed and applied to architectural adaptation, architectures for self-adaptive systems often compound these two aspects reducing the potential for adaptability. In this paper we propose a reference architecture that allows for coordinated yet transparent and independent adaptation of system configuration and behaviour.

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“…Another strand concerns the satisfaction of non functional goals that requires the development of a whole set of compositional results on the integration means for non functional properties. First results in this direction might be found in [45].…”

Section: Research Directionsmentioning

confidence: 91%

Producing software by integration: challenges and research directions (keynote)

Inverardi

Autili

Ruscio

et al. 2013

Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

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International audienceSoftware is increasingly produced according to a certain goal and by integrating existing software produced by third-parties, typically black-box, and often provided without a machine readable documentation. This implies that development processes of the next future have to explicitly deal with an inherent incompleteness of information about existing software, notably on its behaviour. Therefore, on one side a software producer will less and less know the precise behaviour of a third party software service, on the other side she will need to use it to build her own application. In this paper we present an innovative development process to automatically produce dependable software systems by integrating existing services under uncertainty and according to the specied goal. Moreover, we (i) discuss important challenges that must be faced while producing the kind of systems we are targeting, (ii) give an overview of the state of art related to the identied challenges, and finally (iii) provide research directions to address these challenges

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Synthesizing self-adaptive connectors meeting functional and performance concerns

Cited by 18 publications

References 23 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

MORPH: a reference architecture for configuration and behaviour self-adaptation

Producing software by integration: challenges and research directions (keynote)

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