mRUBiS

Vogel, Thomas

doi:10.1145/3194133.3194161

Cited by 24 publications

(8 citation statements)

References 41 publications

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“…To evaluate our scheme, we use the mRUBiS simulator [Vogel 2018], a variant of RUBiS that is frequently used for validating self-adaptation targeting performance [Patikirikorala et al 2012]. mRUBiS is a marketplace on which users sell or auction items.…”

Section: Discussionmentioning

confidence: 99%

“…We demonstrate these benefits of our adaptation scheme by comparing it to two alternative solutions in simulations of mRUBiS [Vogel 2018]. We show that our scheme is only slightly slower but reaches a higher utility over time (reward) than a static rule-based solution.…”

Section: Introductionmentioning

confidence: 88%

“…As the running example, we use mRUBiS-an online marketplace that hosts an arbitrary number of shops, each consisting of 18 components [Vogel 2018]. Each shop can be configured differently and runs isolated from the other shops.…”

Section: Architectural Self-adaptation and Runtime Modelsmentioning

confidence: 99%

“…Threats to internal validity concern how we performed the experiments and interpreted the results. To address such threats, we systematically investigated the scalability and reward of the static, solver, and u-driven self-healing approaches by using the controlled simulation environment mRUBiS [Vogel 2018] for the experiments. Particularly, we are interested in the effects of different planning mechanisms on the scalability and reward.…”

Section: Impact On Scalabilitymentioning

confidence: 99%

“…The major threats to construct validity are the correctness of the simulation environment, our implementation of the self-healing approaches, our adaptation of the realistic failure profile models, and our construction of the failure traces from these models. To address these threats, we use mRUBiS as our simulation environment, which has been accepted as an exemplar by the research community on self-adaptive software and has been extensively tested by students in the scope of four courses on self-adaptive software [Vogel 2018]. Moreover, the implementations of the three self-healing approaches have been tested with the mRUBiS simulator, whereas the adapted failure profile models and the traces constructed from all failure profile models have been double-checked by two authors of the article.…”

Section: Impact On Scalabilitymentioning

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: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 88%

Section: Architectural Self-adaptation and Runtime Modelsmentioning

confidence: 99%

Section: Impact On Scalabilitymentioning

confidence: 99%

Section: Impact On Scalabilitymentioning

confidence: 99%

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