Dynamic adaptation of software-defined networks for IoT systems

Shin, Seung Yeob; Nejati, Shiva; Sabetzadeh, Mehrdad; Briand, Lionel C.; Arora, Chetan; Zimmer, Frank

doi:10.1145/3387939.3391603

“…In the area of adaptive systems, approaches exist that employ online planning to find the best adaptation actions at runtime [22], [31]. A number of algorithms have been employed to this end: Hill climbing has been used to implement a search-based feedback loop [32]; genetic programming and genetic algorithms (including NSGA-II and novelty search) have been advocated as part of the vision of genetic improvement for adaptive software engineering [33] and used in determining optimal configurations [11], [23], [34]- [36]; finally, multi-armed bandits [37] and Bayesian optimization [7], [11] have been employed for online planning in self-adaptive systems.…”

Section: Cost Aspects In Self-adaptive and Online Experimentatiomentioning

confidence: 99%

“…Although the emphasis in these works is on the quality of the found solution, the time needed to find such solution is also evaluated and reported, as, e.g., in [21], [23], [36]. Elapsed time is indeed related to our time cost; however, we emphasize that in our approach, runtime experiments are performed with the system itself, not with a model of it.…”

Section: Cost Aspects In Self-adaptive and Online Experimentatiomentioning

confidence: 99%

Automated Online Experiment-Driven Adaptation–Mechanics and Cost Aspects

Gerostathopoulos

¹

,

Plášil

²

,

Prehofer

³

et al. 2021

IEEE Access

4

0

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As modern software-intensive systems become larger, more complex, and more customizable, it is desirable to optimize their functionality by runtime adaptations. However, in most cases it is infeasible to fully model and predict their behavior in advance, which is a classical requirement of runtime selfadaptation. To address this problem, we propose their self-adaptation based on a sequence of online experiments carried out in a production environment. The key idea is to evaluate each experiment by data analysis and determine the next potential experiment via an optimization strategy. The feasibility of the approach is illustrated on a use case devoted to online self-adaptation of traffic navigation where Bayesian optimization, grid search, and local search are employed as the optimization strategies. Furthermore, the cost of the experiments is discussed and three key cost components are examined-time cost, adaptation cost, and endurability cost.

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“…Coevolutionary analysis in software engineering. Despite the success of search-based software engineering (SBSE) in many application domains including software testing (Wegener et al, 1997;Wegener and Grochtmann, 1998;Lin et al, 2009;Arcuri et al, 2010;Shin et al, 2018), program repair (Weimer et al, 2009;Tan et al, 2016;Abdessalem et al, 2020), and self-adaptation (Andrade and Macedo, 2013; Chen et al, 2018;Shin et al, 2020), coevolutionary algorithms have been applied in only a few prior studies (Wilkerson and Tauritz, 2010;Wilkerson et al, 2012;Boussaa et al, 2013). Wilkerson et al (2010Wilkerson et al ( , 2012 present a coevolution-based approach to automatically correct software.…”

Section: Related Workmentioning

confidence: 99%

“…Search-based software engineering (SBSE) has been successfully applied in many application domains, including software testing (Wegener et al, 1997;Wegener and Grochtmann, 1998;Lin et al, 2009;Arcuri et al, 2010;Shin et al, 2018), program repair (Weimer et al, 2009;Tan et al, 2016;Abdessalem et al, 2020), and self-adaptation (Andrade and Macedo, 2013;Chen et al, 2018;Shin et al, 2020), where the search spaces are very large. Despite the success of SBSE, engineering problems in real-time systems have received much less attention in the SBSE community.…”

Section: Introductionmentioning

confidence: 99%

Optimal Priority Assignment for Real-Time Systems: A Coevolution-Based Approach

Lee¹,

Shin²,

Nejati³

et al. 2021

Preprint

Self Cite

0

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In real-time systems, priorities assigned to real-time tasks determine the order of task executions, by relying on an underlying task scheduling policy. Assigning optimal priority values to tasks is critical to allow the tasks to complete their executions while maximizing safety margins from their specified deadlines. This enables real-time systems to tolerate unexpected overheads in task executions and still meet their deadlines. In practice, priority assignments result from an interactive process between the development and testing teams. In this article, we propose an automated method that aims to identify the best possible priority assignments in real-time systems, accounting for multiple objectives regarding safety margins and engineering constraints. Our approach is based on a multi-objective, competitive coevolutionary algorithm mimicking the interactive priority assignment process between the development and testing teams. We evaluate our approach by applying it to six industrial systems

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“…Search-based software engineering (SBSE) has been successfully applied in many application domains, including software testing (Wegener et al 1997;Wegener and Grochtmann 1998;Lin et al 2009;Arcuri et al 2010;Shin et al 2018), program repair (Weimer et al 2009;Tan et al 2016;Abdessalem et al 2020), and self-adaptation (Andrade and Macêdo 2013;Chen et al 2018;Shin et al 2020), where the search spaces are very large. Despite the success of SBSE, engineering problems in real-time systems have received much less attention in the SBSE community.…”

Section: Introductionmentioning

confidence: 99%

Optimal priority assignment for real-time systems: a coevolution-based approach

Lee

¹

,

Shin

²

,

Nejati

³

et al. 2022

Self Cite

View full text Add to dashboard Cite

In real-time systems, priorities assigned to real-time tasks determine the order of task executions, by relying on an underlying task scheduling policy. Assigning optimal priority values to tasks is critical to allow the tasks to complete their executions while maximizing safety margins from their specified deadlines. This enables real-time systems to tolerate unexpected overheads in task executions and still meet their deadlines. In practice, priority assignments result from an interactive process between the development and testing teams. In this article, we propose an automated method that aims to identify the best possible priority assignments in real-time systems, accounting for multiple objectives regarding safety margins and engineering constraints. Our approach is based on a multi-objective, competitive coevolutionary algorithm mimicking the interactive priority assignment process between the development and testing teams. We evaluate our approach by applying it to six industrial systems from different domains and several synthetic systems. The results indicate that our approach significantly outperforms both our baselines, i.e., random search and sequential search, and solutions defined by practitioners. Our approach scales to complex industrial systems as an offline analysis method that attempts to find near-optimal solutions within acceptable time, i.e., less than 16 hours.

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Dynamic adaptation of software-defined networks for IoT systems

Cited by 16 publications

References 55 publications

Automated Online Experiment-Driven Adaptation–Mechanics and Cost Aspects

Automated Online Experiment-Driven Adaptation–Mechanics and Cost Aspects

Optimal Priority Assignment for Real-Time Systems: A Coevolution-Based Approach

Optimal priority assignment for real-time systems: a coevolution-based approach

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